Psi Chi Journal Volume 30.1 | Spring 2025

SPRING 2025 | VOLUME 30 | ISSUE 1

ISSN: 2325-7342

Published by Psi Chi, The International Honor Society in Psychology ®

PSI CHI JOURNAL OF PSYCHOLOGICAL RESEARCH SPRING 2025 | VOLUME 30, NUMBER 1

EDITOR

STEVEN V. ROUSE, PhD

Pepperdine University

Telephone: (310) 506-7959

Email: steve.rouse@psichi.org

ASSOCIATE EDITORS

JENNIFER L. HUGHES, PhD Agnes Scott College

STELLA LOPEZ, PhD University of Texas at San Antonio

TAMMY LOWERY ZACCHILLI, PhD Saint Leo University

ALBEE MENDOZA, PhD Delaware State University

KIMBERLI R. H. TREADWELL, PhD University of Connecticut

ROBERT R. WRIGHT, PhD Brigham Young University-Idaho

EDITOR EMERITUS

DEBI BRANNAN, PhD Western Oregon University

MANAGING EDITOR

BRADLEY CANNON

DESIGNER

JANET REISS

EDITORIAL ASSISTANTS

EMMA SULLIVAN

ADVISORY EDITORIAL BOARD

GLENA ANDREWS, PhD RAF Lakenheath USAF Medical Center

AZENETT A. GARZA CABALLERO, PhD Weber State University

MARTIN DOWNING, PhD Lehman College

HEATHER HAAS, PhD University of Montana Western

ALLEN H. KENISTON, PhD University of Wisconsin–Eau Claire

MARIANNE E. LLOYD, PhD Seton Hall University

DONELLE C. POSEY, PhD Washington State University

LISA ROSEN, PhD Texas Women's University

CHRISTINA SINISI, PhD Charleston Southern University

PAUL SMITH, PhD Alverno College

ABOUT PSI CHI

Psi Chi is the International Honor Society in Psychology, founded in 1929. Its mission: "recognizing and promoting excellence in the science and application of psychology." Membership is open to undergraduates, graduate students, faculty, and alumni making the study of psychology one of their major interests and who meet Psi Chi’s minimum qualifications. Psi Chi is a member of the Association of College Honor Societies (ACHS), and is an affiliate of the American Psychological Association (APA) and the Association for Psychological Science (APS). Psi Chi’s sister honor society is Psi Beta, the national honor society in psychology for community and junior colleges.

Psi Chi functions as a federation of chapters located at senior colleges and universities around the world. The Psi Chi Headquarters is located in Chattanooga, Tennessee. A Board of Directors, composed of psychology faculty who are Psi Chi members and who are elected by the chapters, guides the affairs of the Organization and sets policy with the approval of the chapters.

Psi Chi membership provides two major opportunities. The first of these is academic recognition to all inductees by the mere fact of membership. The second is the opportunity of each of the Society’s local chapters to nourish and stimulate the professional growth of all members through fellowship and activities designed to augment and enhance the regular curriculum. In addition, the Organization provides programs to help achieve these goals including conventions, research awards and grants competitions, and publication opportunities.

JOURNAL PURPOSE STATEMENT

The twofold purpose of the Psi Chi Journal of Psychological Research is to foster and reward the scholarly efforts of Psi Chi members, whether students or faculty, as well as to provide them with a valuable learning experience. The articles published in the Journal represent the work of undergraduates, graduate students, and faculty; the Journal is dedicated to increasing its scope and relevance by accepting and involving diverse people of varied racial, ethnic, gender identity, sexual orientation, religious, and social class backgrounds, among many others. To further support authors and enhance Journal visibility, articles are now available in the following databases: PsycINFO, EBSCO, Web of Science's Emerging Sources Citation Index, Crossref, and Google Scholar. In 2016, the Journal also became open access (i.e., free online to all readers and authors) to broaden the dissemination of research across the psychological science community.

JOURNAL INFORMATION

The Psi Chi Journal of Psychological Research (ISSN 2325­7342) is published quarterly in one volume per year by Psi Chi, Inc., The International Honor Society in Psychology. For more information, contact Psi Chi Headquarters, Publication and Subscriptions, 651 East 4th Street, Suite 600, Chattanooga, TN 37403, (423) 756­2044. https://www.psichi.org;psichijournal@psichi.org

Statements of fact or opinion are the responsibility of the authors alone and do not imply an opinion on the part of the officers or members of Psi Chi.

Advertisements that appear in Psi Chi Journal do not represent endorsement by Psi Chi of the advertiser or the product. Psi Chi neither endorses nor is responsible for the content of third­party promotions. Learn about advertising with Psi Chi at https://www.psichi.org/Advertise

COPYRIGHT

Permission must be obtained from Psi Chi to reprint or adapt a table or figure; to reprint quotations exceeding the limits of fair use from one source, and/or to reprint any portion of poetry, prose, or song lyrics. All persons wishing to utilize any of the above materials must write to the publisher to request nonexclusive world rights in all languages to use copyrighted material in the present article and in future print and nonprint editions. All persons wishing to utilize any of the above materials are responsible for obtaining proper permission from copyright owners and are liable for any and all licensing fees required. All persons wishing to utilize any of the above materials must include copies of all permissions and credit lines with the article submission.

2 INVITED EDITORIAL:

Mentoring Undergraduate Researchers: A Suggestion for More Training

Darcey N. Powell1, John E. Edlund2, Deanne Buffalari3, Meagan M. Patterson4, Crystal Quillen5, and Sadie Leder Elder6

1Texas A&M University – Corpus Christi (TX)

2Rochester Institute of Technology (NY)

3Westminster College (PA)

4University of Kansas (KS)

5Rutgers Institute for Teaching, Innovation, & Inclusive Pedagogy (NJ)

6High Point University (NC)

6 College Students’ Reflections on Their Academic Motivation During COVID-19: A Thematic Analysis

Nicolette P. Rickert*, Cristian Miralles, Hannah L. Boblasky, Emily K. Wallace, Molly A. Balducci, Tori E. Byars, Emily G. Cabay, Katelyn McLeod, and Leah C. Davis Department of Psychology, Georgia Southern University

18 Effectiveness of a Dissonance-Based Body Image Intervention on Eating Pathology Symptoms

Lindsay M. Howard*1, Spencier R. Ciaralli*1, Ariel E. Schillerberg1, Angelica C. Morales1, and Rachel I. MacIntyre*2

1Departments of Psychology and Sociology, Augustana University

2Department of Psychology, Millersville University

26 LGBTQ Minus: Predictors of Anti-Asexual Bias Among Straight, Gay, and Bisexual Individuals

Nicholas A. Ashenfelter and Kristina Howansky* Department of Psychology, St. Mary’s College of Maryland

38 Attitudes of the Public Toward the Criminal Justice System and Offenders

Mackenzie L. Creighton and William A. Jellison* Department of Psychology, Quinnipiac University

51 Dating Apps Users Among a Religious College Student Body: Profiles of Emotional and Psychosocial Well-Being

Lindsay Johnson, Robert R. Wright*, Brandon A. Jones, Maren Batman, Lavear Whitney, Kiyomi Miyasaki, and Anna Aho Department of Psychology, Brigham Young University–Idaho

65 State of the First Semester Freshman: Health and Wellness Through the COVID-19 Pandemic, Years 2018–2023

Robert R. Wright*, Skyler Brough, Joshua Castro, McKenna Osborne, Lindsay Johnson, and Spencer Johnson Department of Psychology, Brigham Young University–Idaho

84 Causal Pathways From Child Maltreatment to Peer Popularity

Keith T. Jennings and Connie M. Tang* Department of Psychology, Stockton University

93 Reviewer Appreciation

INVITED EDITORIAL:

Mentoring Undergraduate Researchers: A Suggestion for More Training

(Part of the 2024/25 Editorial Series: Collaborating With Students in High-Quality Publishable Research)

Darcey N. Powell1, John E. Edlund2, Deanne Buffalari3, Meagan M. Patterson4, Crystal Quillen5, and Sadie Leder Elder6

1Texas A&M University – Corpus Christi (TX)

2Rochester Institute of Technology (NY)

3Westminster College (PA)

4University of Kansas (KS)

5Rutgers Institute for Teaching, Innovation, & Inclusive Pedagogy (NJ)

6High Point University (NC)

Mentoring undergraduate researchers can be one of the most rewarding aspects of an academic career—fostering students’ first ­ hand experiences in the process of conducting psychological research, encouraging them as they identify their interests, aiding them as they work to contribute knowledge to the field with their own projects, and guiding them as they pursue postgraduation opportunities. The benefit to students from having mentored experiences, such as being a part of a research team, is well­documented (Lopatto, 2010; Russell et al., 2007; Seymour et al., 2004; Thiry et al., 2017). However, mentoring is not without moments of frustration (Hall et al., 2018). There may be times when a faculty member engaged in mentoring thinks that projects might move more quickly if they were working independently, or questions whether the extra work and effort is appreciated by the student(s) they are mentoring. Relatedly, a faculty member might wonder if the frustrations they have are more frequent than what others experience, and whether they are more likely because of the style with which they mentor undergraduate researchers. The authors of this editorial each have personal stories of both wonderful and frustrating moments mentoring undergraduate researchers. We stem from different subfields of psychology (e.g., developmental,

Preregistration and Open Materials badges earned for transparent research practices. The preregistration and materials can be viewed at https://osf.io/mt2wr

social, educational, health, neuroscience). We have our own styles of mentorship and ways in which we typically work with student researchers. Aspects of our styles were influenced by our own experiences as mentees, best practices in the literature, and have been modified over the years in response to personal experiences. Research suggests that training of mentors is rare, with most faculty left to muddle through on their own (Pfund et al., 2015). Relatedly, none of us recall any formalized training that we received specifically on the topic of how to effectively mentor student researchers. We wondered, are other faculty receiving formalized training on mentoring undergraduate student researchers? Is much of one’s mentoring style driven by personal experiences with mentorship in undergraduate or graduate training? Or, do faculty typically figure it out on the fly once they begin such mentoring?

Interestingly, individuals who have been mentored themselves are more likely to provide mentoring to others (Bozionelos 2004; Morales et al., 2017), suggesting that mentorship might be critical in creating a sustained community of effective mentors. In addition, a lack of deliberate mentorship training may indicate to faculty that, although research productivity is critical, training the next generation of productive researchers is not. Faculty who are willing to mentor undergraduate

researchers might also be hesitant to do so given a lack of confidence due to limited training in this area (Allen, 2003). Furthermore, institutional policies and structures may not incentivize or reward training of future scientists (Hall et al., 2018; Morales et al., 2017), which could cause faculty to deprioritize student research mentorship (Morales et al., 2017). Given the impact that mentorship can have on students and faculty alike, and the fact that mentor quality can significantly impact mentee attitudes toward their careers (Bozionelos, 2004), multiple calls have been issued to “train the trainers’’ to promote excellence and impact in the scientific community overall (Pfund et al., 2015; Wilson­Ahlstrom, 2017). To obtain a better understanding of the mentorship­related experiences of faculty in the field of psychology, we asked midcareer psychology faculty about their prior training and guidance on mentoring undergraduate researchers.

Method

Procedure

The full study was approved by an institutional review board and preregistered on Open Science Framework ( https://osf.io/mt2wr ; no hypotheses related to the analyses in this paper were preregistered). In the recruitment information, prospective participants were told the survey was for midcareer psychologists in any subfield of psychology, and at any type of institution, to share details about their current position and experiences; prior engagement with Psi Chi was not required. The recruitment information was shared through the Society for the Teaching of Psychology (STP) and the Committee on Associate and Baccalaureate Education (CABE) listservs. It was also posted on the general STP and midcareer psychologists Facebook pages. Prospective participants were also encouraged to share the information with others they thought might be eligible and interested (e.g., by forwarding the email, sharing the social media post, announcing it at a departmental meeting). Upon completion, participants could enter a raffle for 1 of 22 Amazon gift cards valued at $13.75.

Materials

These data come from a larger survey of midcareer psychologists. The full survey can be found on Open Science Framework (https://osf.io/mt2wr). The questions on training and guidance related to mentoring undergraduate researchers were developed for this project and are described here. First, participants were asked whether they had received any early­career training (Yes/No). If they indicated that they had received training, they were asked to indicate from whom they received the training (i.e., graduate training, department/program, institution, discipline, colleagues/friends informally, other) and

then further describe the type(s) of early­career training and guidance received about mentoring undergraduate researchers. Furthermore, they were asked how satisfied they were with the training they received (1 Very Dissatisfied to 7 Very Satisfied). At the end of the survey, they provided demographic information, with an additional series of questions asking about their experience with Psi Chi (see Table 1).

Participants

Although 105 individuals answered at least 20% of the survey, 83 participants fully completed it. Respondents tended to be in middle adulthood (Mage = 45.53 years, SD = 6.36). Among participants who reported their race and gender (n = 24 did not report), most were White or European American (n = 66; 1 African American or Black, 2 Asian or Asian American, 3 Chicano/a, Latino/a, or Hispanic, 9 multiple selections) and women (n = 68; 13 men). All geographic regions of the United States were represented among respondents (6 in New England, 14 in Middle Atlantic, 6 in South Atlantic, 11 in East North Central, 7 in East South Central, 6 in West North Central, 10 in West South Central, 9 in Mountain, 5 in Pacific; 23 did not report).

Results

We were surprised by the proportion of respondents who reported previously, but not currently, being members of Psi Chi. All memberships in Psi Chi are lifetime upon induction into the Society. So, although faculty may not be actively involved with the society, they are still members and can access benefits afforded by their membership (e.g., grants; https://www.psichi.org/page/ class#faculty ). However, many respondents reported

TABLE 1

Respondents’ Prior Experiences With Psi Chi

JOURNAL

staying actively involved with the society as faculty members, as almost half reported currently or previously having been a chapter advisor to their institution’s Psi Chi chapter. We believe these Psi Chi­related details to be notable given that membership in Psi Chi was not a requirement for participation in the survey.

Focusing on the questions related to mentoring undergraduate researchers, an overwhelming majority indicated they had not received any early­career training or guidance that was mentorship­specific (n = 78, 83.0%).

Of those who indicated that they had received earlycareer training and guidance on mentorship (n = 16, 17.0%), all but one participant shared details as to where they had received the training. Respondents were most likely to say they had received it during their graduate training (n = 10). However, when permitted space to describe the training and guidance they received, only one mentioned systematic, formalized programming (i.e., a National Science Foundation fellowship). Others mentioned observing their graduate advisors (n = 2) and/or beginning their mentoring experiences as a graduate student and learning through those interactions ( n = 3). The second most frequent response from respondents was that they had received informal training from colleagues/friends as early­career faculty (n = 8).

For example, one respondent expressed appreciation for “nice, supportive colleagues,” and another mentioned having discussions with colleagues on their approaches to working with undergraduate researchers. Less than half of respondents who received early­career training and guidance on mentorship received any from their department ( n = 6), their institution (n = 4), or the discipline ( n = 4). One respondent who reported receiving training and guidance from their department mentioned shadowing colleagues as they interacted with undergraduate researchers; another mentioned initiating a peer mentoring group within the department that included discussing ways to work with undergraduate researchers. Those who reported receiving training and guidance from their institutions mentioned workshops and training sessions focused on mentoring research assistants. Two specific organizations were listed by respondents who reported receiving training and guidance within the discipline—the Council on Undergraduate Research and STP.

Although only a small group of respondents had received training or guidance as early­career faculty on mentoring undergraduate research assistants, they were significantly more satisfied with the guidance they received on mentoring undergraduate researchers (M = 5.00, SD = 1.00) compared to those who reported not receiving training or guidance (M = 3.18, SD = 1.21), t(80) = 5.44, p < .001, 95% CI [1.15, 2.49], g = 1.55.

Discussion

Overall, we believe that faculty mentors could benefit from, and would appreciate, specialized training in mentoring undergraduate researchers. Such training would be beneficial to the field and to future cohorts of researchers in faculty members’ labs. Interestingly, some work suggests that the midcareer stage might be a place where motivation to mentor others is particularly high (Morales et al., 2017). Thus, the discipline should adequately prepare faculty with the skill sets needed to do so effectively (Allen, 2003). Luckily, given recent calls for improved mentorship by mentors, many resources exist for both training mentors and mentoring students (Feldman et al., 2009; Lee et al., 2007; Nichols & Powell, 2019; Pfund et al., 2016; Shanahan et al., 2015; Sood et al., 2016; Spencer et al., 2018; Walkington et al., 2020; Wilson­Ahlstrom et al., 2017) that can support individuals seeking training in how to best guide and support undergraduate researchers. However, published work suggests that systematic, formalized training or programming is likely to have more impact, rather than relying on faculty to seek such information or training on their own. Additionally, training that extends beyond one’s institution may foster a sense of broader community among those who participate. Faculty members desire the support of professional communities, and the importance of having a sense of community is well­documented (Eib & Miller, 2006; Strayhorn, 2023; Student Experience Project, 2022). However, many faculty report a lack of community (Center for Postsecondary Research, n.d.; Rice et al., 2000; Robert, 2023), particularly at midcareer (Leder Elder et al., 2023; Quillen et al., 2022; Powell et al., 2022). Given the engagement of Psychology faculty with Psi Chi, and Psi Chi’s support of undergraduate research and training, we contend that Psi Chi may be a premier organization for facilitating formalized training and providing a sense of community among faculty who mentor undergraduate researchers in the field of psychology.

References

Allen, T. D. (2003). Mentoring others: A dispositional and motivational approach. Journal of Vocational Behavior, 62(1), 134–154. https://doi.org/10.1016/S0001-8791(02)00046-5

Bozionelos, N. (2004). Mentoring provided: Relation to mentor’s career success, personality, and mentoring received. Journal of Vocational Behavior, 64(1), 24–46. https://doi.org/10.1016/S0001-8791(03)00033-2 Center for Postsecondary Research (n.d.) Faculty insights: Belonging and environment. https://nsse.indiana.edu/research/annual-results/2020/ belonging-story/faculty-insights.html

Eib, B. J., & Miller, P. (2006). Faculty development as community building. International Review of Research in Open and Distributed Learning, 7(2), 1–15. https://doi.org/10.19173/irrodl.v7i2.299

Feldman, M. D., Huang, L., Guglielmo, B. J., Jordan, R., Khan, J., Creasman, J. M., Weiner-Kronish, J. P., Lee, K. A., Tehrani, A., Yaffe, K., & Brown, J. S. (2009). Training the next generation of research mentors: The University of California, San Francisco, Clinical & Translational Science Institute mentor development program. Clinical and Translational Science, 2(3), 216–221. https://doi.org/10.1111/j.1752-8062.2009.00120.x

Hall, E. E., Walkington, H., Shanahan, J. O., Ackley E., & Stewart, K. A. (2018). Mentor perspectives on the place of undergraduate research mentoring in academic identity and career development: An analysis of award winning mentors. International Journal for Academic Development, 23(1), 15–27. https://doi.org/10.1080/1360144X.2017.1412972

Lee, A., Dennis, C., & Campbell, P. (2007). Nature’s guide for mentors. Nature, 447(14), 791–797. https://doi.org/10.1038/447791a

Leder Elder S., Quillen, C. A., Powell, D. N., Buffalari, D., Edlund, J. E., & Patterson, M. M. (2023, October). Lessons learned from two years of STP mid-career mentoring reading groups. Symposium presented at the Society for the Teaching of Psychology’s Annual Conference on Teaching, Portland, OR. Lopatto, D. (2010). Undergraduate research as a high-impact student experience. Peer Review, 12(2), 27–31.

Morales, D. X., Grineski, S. E., & Collins, T. W. (2017). Faculty motivation to mentor students through undergraduate research programs: A study of enabling and constraining factors. Research in Higher Education, 58(8), 520–544. https://doi.org/10.1007/s11162-016-9435-x

Nichols, D. F., & Powell, D. N. (2019). How to make publishing with undergraduate co-authors at liberal arts colleges with a teacher-scholar model worth your time. In R. J. Harnish, K. R. Bridges, D. N. Sattler, M. Munson, S. Bridgen, F. J. Slack, & S. M. Smith (Eds.), What I wished my mentor had told me https://teachpsych.org/ebooks/

Pfund, C., Spencer, K. C., Asquith, P., House, S. C., Miller, S., & Sorkness, C. A. (2015). Building national capacity for research mentor training: An evidence-based approach to training the trainers. Life Sciences Education, 14(2), 1–12. https://doi.org/10.1187/cbe.14-10-0184

Powell, D. N., Quillen, C., Edlund, J. E., Leder Elder, S., & Patterson, M. M. (2022, February). Mid-career mentoring for psychology faculty: A conversation on preliminary ideas from STP’s working group. Symposium presented at the Southeastern Teaching of Psychology conference, virtual.

Quillen, C. A., Powell, D. N., Leder Elder S., Edlund, J. E., & Patterson, M. M. (2022, October). STP mid-career mentoring group: Exploring reading groups. Symposium presented at the Society for the Teaching of Psychology’s Annual Conference on Teaching, Pittsburgh, PA.

Rice, R. E., Sorcinelli, M. D., & Austin, A. E. (2000). Heeding new voices: Academic careers for a new generation. [Working Paper Inquiry No. 7)] New pathways: Faculty careers and employment for the 21st century Series. American Association for Higher Education. https://files.eric.ed.gov/fulltext/ED451740.pdf

Robert, J. (2023, November 13). EDUCAUSE quickpoll results: A sense of belonging for workforce retention. EDUCAUSE Review. https://er.educause.edu/articles/2023/11/ educause-quickpoll-results-a-sense-of-belonging-for-workforce-retention

Russell, S. H., Hancock, M. P., & McCullough, J. (2007). Benefits of undergraduate research experiences. Science, 316(5824), 548–549. https://doi.org/10.1126/science.1140384

Seymour, E., Hunter, A-B., Laursen, S. L., & DeAntoni, T. (2004). Establishing the benefits of research experience for undergraduates in the sciences: First findings from a three-year study. Science Education, 88(4), 493–534. https://doi.org/10.1002/sce.10131

Shanahan, J. O., Ackley-Holbrook, E., Hall, E., Stewart, K., & Walkington, H. (2015). Ten salient practices of undergraduate research mentors: A review of the literature, mentoring, and tutoring. Partnership in Learning, 23(5), 359–376. https://doi.org/10.1080/13611267.2015.1126162

Sood, A., Tigges, B., & Helitzer, D. (2016). Mentoring early-career faculty researchers is important—but first “train the trainer.” Academic Medicine, 91(12), 1598–1600. https://doi.org/10.1097/acm.0000000000001264

Spencer, K. C., McDaniels, M., Utzerath, E., Rogers, J. G., Sorkness, C. A., Asquith, P., & Pfund, C. (2018). Building a sustainable national infrastructure to expand research mentor training. Life Sciences Education, 17(3), 1–11. https://doi.org/10.1187/cbe.18-03-0034

Strayhorn, T. (2023, April 13). Workplace belonging in higher education. Higher Education Digest https://www.highereducationdigest.com/workplacebelonging-in-higher-education/ Student Experience Project. (2022). Increasing equity in student experience: Findings from a national collaborative. https://studentexperienceproject.org/report/ Thiry, H., Weston, T. J., Laursen, S. L., & Hunter, A-B. (2017). The benefits of multiyear research experiences: Differences in novice and experienced students’ reported gains from undergraduate research. Life Sciences Education, 11(3), 201–332. https://doi.org/10.1187/cbe.11-11-0098

Walkington, H., Stewart, K. A., Hall, E. E., Ackley, E., & Shanahan, J. O. (2020). Salient practices of award-winning undergraduate research mentors— balancing freedom and control to achieve excellence. Studies in Higher Education 45(7), 1519–1532. https://doi.org/10.1080/03075079.2019.1637838

Wilson-Ahlstrom, A., Ravindranath, N., Yohalem, N., & Tseng V. (2017). Pay it forward: Guidance for mentoring junior scholars (updated edition). The Forum for Youth Investment, Washington, DC.

Author Note

Darcey N. Powell https://orcid.org/0000­0001­6076­9741

John E. Edlund https://orcid.org/0000­0003­3868­1844

Deanne Buffalari https://orcid.org/0000­0002­4308­8378

Meagan M. Patterson https://orcid.org/0000­0002­4788­8401

Crystal Quillen https://orcid.org/0009­0004­9310­4497

Sadie Leder Elder https://orcid.org/0000­0001­9919­3735

The authors are current or former members of the Society for the Teaching of Psychology Mid­Career Psychologists Committee. Darcey N. Powell, PhD, CFLE, is an associate professor in the Department of Psychology and Sociology at Texas A&M University – Corpus Christi in Corpus Christi, TX. John E. Edlund, PhD, is a full professor at Rochester Institute of Technology in Rochester, NY. Deanne Buffalari, PhD, is an associate professor of psychology and neuroscience at Westminster College in New Wilmington, PA. Meagan Patterson, PhD, is a professor in the Department of Educational Psychology and Associate Vice Provost for Faculty Policy and Recognition at the University of Kansas in Lawrence, KS. Crystal A. Quillen, PhD, is an assistant director at the Rutgers Institute for Teaching, Innovation, & Inclusive Pedagogy in New Brunswick, NJ. Sadie Leder Elder, PhD, was an associate professor of psychology at High Point University in High Point, NC, and is now the executive director of the nonprofit organization Working Wardrobe in High Point, NC.

Correspondence concerning this article should be addressed to Darcey N. Powell, Department of Psychology & Sociology, Texas A&M University – Corpus Christi, 6300 Ocean Dr., Corpus Christi, TX 78412, United States; Email: Darcey.Powell@tamucc.edu

College Students’ Reflections on Their Academic Motivation During COVID-19: A Thematic Analysis

Nicolette P. Rickert*, Cristian Miralles, Hannah L. Boblasky, Emily K. Wallace, Molly A. Balducci, Tori E. Byars, Emily G. Cabay, Katelyn McLeod, and Leah C. Davis

Department of Psychology, Georgia Southern University

ABSTRACT. The current study examined the effects of the COVID­19 pandemic on the academic experiences of college students with a focus on the challenges faced during the transition to remote, online learning. We surveyed 371 college students in the southeastern United States during spring 2022 through an online, retrospective survey. Participants were 71.4% women and 58% White from a variety of undergraduate school years. Thematic analyses were utilized to examine a series of qualitative, open­ended questions and responses. Students reported a variety of challenges to their academic motivation during the COVID ­ 19 pandemic (e.g., transitioning to remote learning, declining engagement, struggles with accountability, changes in their learning environments). However, students noted several resources that helped them offset these barriers (e.g., personal organization, time management, social support, engaged and caring professors, resources on campus). Further, students appreciated several aspects of remote learning, including flexibility with pacing and learning environments. Although many students felt positively about returning to in­person classes and activities, some reported being concerned, perhaps because of mixed feelings about university policies (i.e., mask mandates). These findings may be generalizable to students coping with other extenuating circumstances (e.g., natural disasters, war and political strife, and public health concerns). Universities can prioritize students’ academic and mental health by providing extra resources like academic services, counseling, financial aid, and mentorship programs. This can help students overcome motivational challenges, manage their academic and personal lives, and succeed academically.

Keywords: COVID­19 pandemic, academic motivation, college students, academic support, remote learning, thematic analysis

Many students have reported declines in motivation and productivity, increases in stress, and disruptions to learning in general during and beyond the immediate times of the COVID­19 pandemic (e.g., Gravelle et al. 2022; Hicks et al., 2021; Kim et al., 2021; Ober et al., 2021; Usher et al., 2021). This is in part due to a variety of health and safety protocols, which also impacted learning environments, including social distancing policies, quarantine protocols, abrupt movement to fully online learning, adaptations to hybrid learning,

and even school closures. Yet, some students have also reported appreciating the freedom and versatility that remote and hybrid classes offered to their schedules with the COVID­19 pandemic (Garris & Fleck, 2020; Smoyer et al., 2020; Stevanović et al., 2021). The current study aimed to build upon this previous research in further examining college students’ perceptions of motivation and learning during the COVID ­ 19 pandemic and identifying supports to bolster their academic experiences.

Rickert, Miralles, Boblasky, Wallace, Balducci, Byars, Cabay, McLeod, and Davis

Negative Academic Experiences

During the COVID-19 Pandemic

A variety of negative experiences have been documented across college students as they transitioned from in­person to online classes during the COVID­19 pandemic. Some studies have found declines in students’ attentional resources (e.g., focus, sustained attention, self­control, ability to perceive time; Armstrong et al., 2021; Garris & Fleck, 2020; Gravelle et al., 2022; Hicks et al., 2021; Ober et al., 2021; Stevanović et al., 2021) as well as interest, effort, and affect toward learning (Armstrong et al., 2021; Garris & Fleck, 2020) as college students transitioned quickly to online instructional modalities. In addition, studies have found that students reported being frustrated by changes in teaching methods and shifting workloads (Kinsky et al., 2021; Tasso et al. 2021), faced barriers to remote learning like finding a quiet place to work and good internet quality (Gonzalez­Ramirez et al., 2021; Gravelle et al., 2022; Kinsky et al., 2021; Ober et al., 2021; Stevanović et al., 2021), and felt distanced from their course due to a lack of social interactions with peers and professors (Gravelle et al., 2022; Kim et al., 2019; Stevanović et al., 2021; Zhou & Zhang, 2021).

Further, Gravelle et al. (2022) found that introductory psychology students who reported greater difficulties transitioning to online learning were less likely to turn in homework assignments, perform well on exams, and subsequently pass their course. This falls in line with other studies that found students felt overwhelmed by the amount of schoolwork and difficult assignments in online learning (Ober et al., 2021), which may be in part due to students’ perceptions of online learning as lower quality and with less instructional support (Garris & Fleck, 2020; Gravelle et al., 2022; Zhou & Zhang, 2021) compared to in­person classes. Findings from most studies demonstrate that students preferred in­person learning to their experiences transitioning to online learning during the COVID­19 pandemic (e.g., Gonzalez­Ramirez et al., 2021; Smoyer et al., 2020).

Motivation and Mental Health

The challenges students reported facing with the transition to online learning during the COVID­19 pandemic may also be due to motivational declines seen across this time period (Armstrong et al., 2021; Gonzalez­Ramirez et al., 2021; Kinsky et al., 2021; Marler et al., 2021; Tasso et al., 2021; Usher et al., 2021). More specifically, Stevanović et al. (2021) found that students who had originally been engaged with traditional, in ­ person learning before the start of the COVID­19 pandemic reported lower levels of motivation than students who had been enrolled in online or hybrid classes before the pandemic started. Further, many college students also experienced

threats to their mental health and well­being during the COVID­19 pandemic. For example, Hicks et al. (2019) discovered that students’ reported school­related anxiety when they transitioned to online classes, and Zhou and Zhang (2021) found that students experienced mid to high levels of depression but low levels of anxiety after a year into the pandemic. This may be exacerbated by students’ perceptions of decreases in their social connections and healthy habits during the COVID­19 pandemic (Gonzalez­Ramierez et al., 2021).

Positive Academic Experiences

During

the COVID-19 Pandemic

Despite these instructional, interpersonal, motivational, and mental health stressors, some college students reported positive experiences with the emergency transition to online learning during the COVID­19 pandemic. Kinsky et al. (2021) found that students noted skill development (e.g., video conferencing, time management, adaptability, communication skills) as a positive outcome during this period. This may in part be due to positive features of online learning that some students have identified. For example, Smoyer et al. (2020) found that, although most social work students preferred in­person to online learning, those who did prefer online learning appreciated the flexibility and convenience of remote, asynchronous instruction. This appreciation for flexible courses and less rigid deadlines (Garris & Fleck, 2020; Stevanović et al., 2021) has been replicated in other studies. Drawing on data from a mixture of in­person, hybrid, and remote learning college students a year into the COVID­19 pandemic, Zhou and Zhang (2021) found that, on average, students reported high levels for belonging to their learning community, receiving instructional support, and satisfaction with learning in online classes. Further, Kim et al. (2021) conducted thematic analyses of student responses to the transition to online learning and found that students reported that active learning (i.e., participating with engaging and supportive materials), face ­ to ­ face instruction (i.e., live, remote interactions with peers and the instructor), course content (e.g., specific, interesting topics covered in the course), and teaching methods (i.e., instructor’s teaching style and course structure) were crucial to them feeling engaged in online courses.

Current Study

Given the mixture of positive and negative outcomes identified in previous studies, the current study sought to highlight college students’ experiences and perceptions of the COVID­19 pandemic and its impact on their learning and engagement two years into initial university and health restrictions and adjustments. To examine

College Academic Motivation and COVID-19

| Rickert, Miralles, Boblasky, Wallace, Balducci, Byars, Cabay, McLeod, and Davis

participants’ responses, a qualitative approach using thematic analysis was utilized to denote themes using the participants’ own words and descriptions of their motivational and academic challenges. The goals of this study were to identify challenges to college students’ academic motivation during the pandemic and supports or resources that could offset these challenges; positive features of remote learning; feelings about in­person activities; and views on university COVID­19 policies. Based on previous research, we hypothesized that students faced a variety of challenges to their academic motivation during the COVID­19 pandemic, such as technological difficulties, external distractions, and mental health struggles. Further, we hypothesized that students would note a plethora of supports needed to help them re­engage with school, including receiving adequate support from professors and access to in­person and online resources. Given mixed findings in the literature regarding students’ perceptions of remote learning, we hypothesized that students would have some positive experiences with remote learning, such as flexibility with schedules. No specific hypotheses were formed about returning to in­person activities or university policies given the lack of research on these topics in previous studies to date.

Method

Participants

A total of 371 college students from a university in the southeastern United States with an average age of 20.4 years (SD = 3.52) participated in this study. The sample

TABLE 1

Open-Ended Survey Questions

Topic Question

Motivational Challenges

Needed Resources

Remote Learning

In-Person Activities

COVID Policies

1. What are any challenges you have faced in staying academically motivated since the pandemic (starting in 2020)?

2. What would help you to address or offset these challenges?

2a. What personal characteristics or resources do you need to stay academically motivated?

2b. What instructional characteristics or resources do you need to stay academically motivated?

2c. What institutional/university characteristics or resources do you need to stay academically motivated?

3. What were any aspects of remote instruction and hybrid classes that you liked?

4. How do you feel about attending in-person classes this school year? Why do you feel this way?

5. How do you feel about attending other in-person school activities? Why do you feel this way?

6. What school COVID policies do you think are beneficial? Why?

7. Which policies could be improved? How and why?

was primarily women (71.4% women, 27.5% men, 1.1% prefer not to say) and more than half (58.0%) of participants identified as White, 28.3% identified as Black, 6.5% identified as Hispanic, 4.6% as other, and 2.7% identified as Asian/Pacific Islander. For grade level in school, 38.5% of participants were in their first year of college, 31.3% were in their second year, 19.1% were in their third year, 8.1% were in their fourth year, and 1.9% were in their fifth year or higher. Students came from a variety of majors, including 39.9% in Psychology, 8.6% from Exercise Science, and 6.7% Nursing. Only 5.1% of students were enrolled in the Honors College.

Measures

A series of open­ended questions were posed to participants to gather their thoughts and opinions of their experiences during the COVID­19 pandemic (see Table 1). Retrospective questions targeted students’ challenges to academic motivation during the pandemic; resources and supports needed at personal, instructional, and institutional levels; aspects of remote learning that they liked; feelings about returning to in­person activities in and out of the classroom; and views on the university’s COVID­19 policies.

Procedure

This study was approved by the Georgia Southern University Institutional Review Board (H22307: “College Students’ Motivation during COVID­19”). Participants were recruited through an online surveying platform (Sona) housed by the university where they were able to access a link to consent and complete the study through a Qualtrics survey. Participants completed both open­ended and Likert­type scale questions as part of a larger research study. The current study draws upon the open­ended questions targeting students’ retrospective reflections since the COVID­19 pandemic started in 2020. After completion of the survey, participants were directed to a debriefing page with resources for the COVID­19 pandemic, academic support, and counseling services. Students’ instructors received an automatic notification that their student had received a research credit for completing the study, which the instructor converted into course or extra credit. The researchers did not have access to students’ names through the online survey, and responses remained anonymous. All data were collected during spring 2022, during which the university had reopened and was fully functioning with limited COVID­19 health and safety policies in place.

Analysis Plan

Thematic analysis was utilized to examine students’ qualitative, open­ended responses. Thematic analysis

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Academic Motivation and COVID-19

allows for the extraction of concepts and codes that are most consistent among students’ responses, find patterns among those responses, and deciphers broader themes from the data (Braun & Clarke, 2006; Nowell et al., 2017). Four to eight coders1 individually reviewed each participant response for each question and derived brief codes describing them. Coders met weekly to discuss any discrepancies in their codes until a consensus was reached. After all responses were coded, frequency analyses were used to identify the most common themes for each question across all participant responses; counts were derived for each theme as each student response could elicit multiple codes and subcodes.

Results

Several common themes arose across questions related to college students’ challenges to learning during the COVID­19 pandemic, necessary supports, reflections on their remote learning experiences during this time, feelings about in­person activities, and views on university COVID­19 policies.

Challenges to Academic Motivation

Many students noted challenges remaining motivated with school­related activities when faced with remote learning and environmental disruptions during the pandemic, mental health and coping with the transition to online learning, as well as dealing with external factors related to everyday life (see Table 2).

Remote Learning, Transitions, and Environment

A substantial number of participants noted challenges with the transition and experience of utilizing remote learning methods. Students struggled with going from in­person classes to remote learning, staying engaged with the material, learning new software, taking tests, interacting with others, and attending courses. Participants expressed 172 times that they faced challenges to their academic motivation because of “remote learning,” and they noted an additional 30 times that they faced challenges due to the “transition” to remote learning (see Table 2). One participant noted,

The learning process was extremely hard, [it] felt like we were being taught all curriculum on a surface level and we were responsible for learning the depth of the content on our own. It was hard to be online for 4–12 hours a day. I was not motivated and extremely unhappy.

1Coding was completed across the 2022–23 and 2023–24 school years. Fluctuations in the number of coders available were determined by coders graduating or joining the project midway. There were no fluctuations in the number of coders within a given question, only between questions.

Participants expressed that changes to the learning “environment” (i.e., transition from in ­ person, on­campus classes to remote learning from home or another noneducational setting) were another hurdle. Some participants noted that outside distractions, technology challenges, and the ability to work in their bed influenced their experiences. One participant noted,

I’m not as academically motivated as I was pre­COVID because of not being in an actual classroom. Not being in a school environment really affected me because I’ve gotten super lazy. It was also easy for me not to actually learn or try hard because some teachers/professors just passed students anyways.

The psychological, physical, and technological aspects of transitioning to remote learning during the pandemic were the most salient challenges to students’ academic motivation.

Accountability

Participants expressed challenges with holding themselves “accountable” for completing schoolwork and keeping to a schedule during the pandemic. Many participants expressed challenges with creating an

Themes for Challenges to Students’ Motivation

Theme Count Description Example Quote

Remote learning 172 Mentions use of technology to engage with school

Accountability 82 Extent to which a student felt responsibility over their schedule and classes

Engagement

68 Extent to which students maintained an interest in class material or topics

Environment 57 The physical surroundings and/or location of the student during schooling or learning activities

Transition 30 Mentions of moving from on-campus, in-person classes to remote learning

External factors 21 Factors not related to schooling that influence the life of the student (e.g., finances, illness, mental health)

“...It was hard to be online for 4–12 hours a day. I was not motivated and extremely unhappy.”

“I’ve had a lot of issues with completing work on my own outside of class due to daily distractions.”

“Since COVID, I do feel as though I am less engaged in my coursework. When I went to class, there was a teacher and interactions that helped me engage in the material.”

“During the pandemic, I struggled with being able to wake up on time for school and staying focused. I think it was hard only because school was virtual, while sitting comfortably at home.”

“Since the pandemic began, staying motivated has been difficult because online schooling is not nearly as encouraging as going to class in person…”

“I have always struggled with mental health problems such as depression and anxiety. Since the start of the pandemic my mental health bounces between being great and being terrible.”

Note. Eight participants did not note any challenges to staying academically motivated, and 3 participants left this question blank.

TABLE 2

College Academic Motivation and COVID-19 | Rickert, Miralles, Boblasky, Wallace, Balducci, Byars, Cabay, McLeod, and Davis

organized schedule or just finding time to complete work. Others found it difficult to find the desire to complete schoolwork, much less to the best of their abilities. As one participant explained, “When entering college in 2020, everything was virtual. I felt I didn’t need to try whatsoever, and as a result, I failed two classes. After that, I realized my time management needed to upgrade.” Another participant noted, “I’ve had a lot of issues with completing work on my own outside of class due to daily distractions.”

Engagement

Participants also noted that staying “engaged” with the material and feeling as though they were a partner in their

TABLE 3

Themes for Challenges to Students’ Motivation

Personal Organization

Accountability

Social support

52 Scheduling or managing of environment

51 Extent to which student felt responsibility over schedule and classes

41 Mentions of friends, family members, etc. to turn to for support

Time management

In-person classes

Instructional Environment

Engagement

Professor support

Professor engagement

Institutional Access to resources

Professor support

39 Exercising control over time spent doing specific tasks to increase productivity

27 Mentions of students and professors being in the same physical space

48 The physical surroundings and/or location of the student during schooling or learning activities

47 Student maintains interest in topic

33 Professor shows empathy for their students

33 How a professor keeps the class interested

134 Access to physical spaces and resources on campus

23 Professor shows empathy for their students

“I have started…to keep my work area less cluttered…so that I have nothing else to work on but my homework.”

“...getting up in the morning on time and getting myself moving. This would wake me up and get me in the zone to be ready for online…”

“I personally keep myself surrounded with others who push themselves academically…doing this really motivates me to do better in school…”

“Write down my assignments that I need to do and make myself do the assignments earlier in the week…”

“... I need to physically be in a working/ learning environment with my teachers/ professors and peers.”

“Small classroom .... 30 students or less.”

“The content of the class helps if it’s interesting.”

“I just wish my professors were more accommodating.”

“An engaging professor definitely helps.”

“…accommodations do a good job at keeping me motivated for tests because I don’t feel as pressured.”

“Professors that care.”

Note. For personal characteristics, 11 participants did not note any resources, and 6 participants left this question blank. For instructional characteristics, 13 participants did not note any resources, and 10 participants left this question blank. For institutional characteristics, 45 participants did not note any resources, and 20 participants left this question blank.

education was a barrier for their academic motivation. This theme reflected a desire for involvement in their own learning, feeling enthused to participate in school, interacting with what they were learning, and being captivated by teaching techniques. For example, one participant noted, “Since COVID, I do feel as though I am less engaged in my coursework. When I went to class, there was a teacher and interactions that helped me engage in the material.” This theme demonstrates the interconnections between motivational challenges in which transitioning to remote learning also disrupted opportunities for active engagement in class.

Mental Health and Other External Factors

Several participants noted challenges with their mental health and coping with “external factors” that led to distractions. Many faced challenges with learning differences, anxiety, or depression, while others had to cope with financial strain or family circumstances. One participant explained, “I have always struggled with mental health problems such as depression and anxiety. Since the start of the pandemic, my mental health bounces between being great and being terrible.” Another stated that they were, “Feeling guilty for not spending more time with the family. I see my family going on activities and doing other stuff, but I can’t join because I am doing schoolwork.” Nonacademic challenges also arose as barriers to students’ ongoing academic motivation during the COVID­19 pandemic.

Resources to Address Challenges

Students noted a variety of personal, instructional, and institutional characteristics and resources they found necessary to overcome the noted challenges to their motivation during the COVID­19 pandemic (see Table 3).

Personal Characteristics and Resources

Organization,

Accountability, and Time Management.

Several participants highlighted themes of personal organization, accountability, and/or time management as adaptive characteristics and resources that were crucial to their motivation. “Organization” reflected responses that indicated planning, managing of space/ environment, and various ways of setting reminders for task completion. For example, one participant wrote, “Planning and having all the due dates written down helps me. Also, I have my goals written on the front of my planner to keep me motivated on why I want to do well.” “Accountability” included any mention of a student’s responsibility for their classes, schedule, work, or homework. For example, one student expressed that waking up and getting ready for the day, much like they

Rickert,

Miralles, Boblasky, Wallace, Balducci, Byars, Cabay, McLeod, and Davis |

would do for in­person classes, would help with making sure they were ready to start their remote classes:

I think what helped me is just getting up in the morning on time and getting myself moving. This would wake me up and get me in the zone to be ready for online class the same way I would be for in­person class.

Qualifications for “time management” codes included references to planning/exercising control over time spent doing specific tasks (e.g., work, homework, studying, working out, friends) to increase productivity. For example, one student mentioned that they, “Write down my assignments that I need to do and make myself do the assignments earlier in the week to get rid or lower the academic stress.” Even in the face of greater motivational, situational, and environmental challenges, students identified personal actions and resources they could enact themselves to support their learning and academic success during the pandemic.

Social Support. “Social support” was described as any mention of having friends, peers, classmates, family members, or other social partners to turn to for support with difficulties, struggles, or for work completion. For example, one participant stated, “I personally keep myself surrounded with others who push themselves academically. I have found that doing this really motivates me to do better in school….” Another participant wrote, “Keeping a balance of my academics and my social life. Talking to my friends after a long day of virtual work helped me get through and stay motivated.” Although personal actions, organization, and accountability were vital for students to bolster their own academic motivation, surrounding themselves with socially supportive others was also an important motivational resource.

In-Person Classes. “In­person classes” coded for any responses that included mentions of students and professors being within the same physical space at the same time. A commonly held sentiment among students was the need to be in the same physical learning environment to better focus on their studies: “I believe I need to physically be in a working/learning environment with my teachers/professors and peers. Online learning is a useful tool but requires discipline and I’m not always capable of keeping myself on task.” Just as students noted that transitioning to remote learning was a challenge to their motivation, returning to in­person classes was identified as a necessary support.

Instructional Characteristics and Resources Environment. Responses given the code “environment” referred to the physical setting and any specific factors in

the setting which one learns or studies. One participant stated, “The larger the classroom (actual room size), the better I feel because I don’t feel so crammed into the classroom.” Another participant wrote, “I have to go to the library or at least somewhere away from where I sleep to stay motivated. I also have to have some music playing to stay focused.” Students believed it was important to learn and study in a setting they felt they could connect with, often involving in­person classroom settings. Engagement. “Engagement” codes consisted of responses in which an individual displayed interest in the specific courses they were taking, including the way class material was taught. Many participants stated that “More discussions as a class” would be an effective way to help them stay academically motivated. Visual aids, including pictures or videos, also increased engagement as one student stated, “It helps me when teachers use pictures or videos to break up lectures. A boring, redundant lecture makes it hard to pay attention, and then I have to study harder at home and lose motivation.” Active instructional strategies that foster engagement were crucial to students addressing motivational challenges. Professor Support and Engagement. Participants mentioned professor support and professor engagement 33 times each as crucial to their academic engagement (see Table 3). “Professor support” reflected a professor’s willingness to be flexible and empathetic with their students, as well as serving as a motivating factor for students to do their best. One student echoed this sentiment, stating they preferred,

A professor who is willing to work with me and help me succeed. I will do my best to stay motivated and reach out when I don’t understand something or need help, but I also want a professor who is willing to work with me to help me succeed.

“Professor engagement” described how the instructor specifically kept the class interested and attentive throughout lectures, whether through showing passion for the topic they were teaching or by encouraging participation during lectures. As one student stated, “Active lecturing, asking for participation makes me feel included in the lesson and not like I’m just listening waiting for it to be over,” would help boost their motivation. Students noted that having a professor who was willing to accommodate their students, motivate them to do their best, and keep lectures dynamic was necessary for fostering academic engagement and learning during the COVID­19 pandemic.

Institutional Characteristics and Resources

Access to Resources. Many students cited “access to resources” as having a major impact on their academic

College Academic Motivation and COVID-19 | Rickert, Miralles, Boblasky, Wallace, Balducci, Byars, Cabay, McLeod, and Davis

motivation, such as the campus counseling center, testing accommodations, or tutoring sessions as well as other physical resources like the library and on­campus study rooms. One student stated that, “I use the counseling center sometimes to give myself a push and someone to talk to.” Another participant said that using the campus library for “hard assignments has resulted in me getting more quality work done.” Finally, one student wrote, “The learning commons and student center help me stay academically motivated. It is nice to be social and out of my house during the pandemic, and [doing so] also helps me to stay engaged in on­campus activities.” Continued access to the entirety of diverse resources offered on campus remained a key factor for maintaining academic motivation during the COVID­19 pandemic. Professor Support. Consistent with its previous use, “professor support” referred to a professor being empathetic, motivating, and flexible with their students. One student noted, “I need professors that are willing to help students when they are struggling and answer questions when asked to help understanding of the material,” while another spoke to the importance of professor effort: “Professors that actually put in effort and want to see their students succeed and pass their class, they shouldn’t take pride in their low­pass rate, that’s nothing to be proud of.” Even with other campus resources available to students, a cooperative professor remained a major factor in increasing academic motivation.

TABLE 4

Themes for Positive Aspects of Remote/Hybrid Instruction

Flexibility 76 Freedom to do schoolwork where, when, and how desired

Time management 60 Exercising control over time spent doing specific tasks to increase productivity

Comfort 55 Working in a comfortable environment whenever/ wherever

Pace 47 Working on assignments at their own pace, as far in advance as they wanted

Environment 42 The physical surroundings and/or location of the student during schooling or learning activities

Asynchronous 31 How remote learning did not have scheduled class meeting times

“I liked the flexibility of the classes. It gave me more time to do my homework.”

“I liked that it made me able to create my own schedule and do what I wanted to do, freed my time up enough to do what I needed to do.”

“I did not have to get ready and was able to just log in and be there for the lecture.”

“I liked being able to take the course at my own pace.”

“Being able to do everything at home.”

“I liked the online classes because it worked better with my job, and I was able to pace myself differently with the classes instead of having a permanent time that I have to go to class.”

Note. Seventy-one participants did not note any benefits to remote instruction, and 10 participants left this question blank.

Remote Instruction

Next, participants were asked about their positive experiences with remote, online learning during the COVID­19 pandemic (see Table 4).

Flexibility, Time Management, and Pace

Students appreciated that remote learning was more “flexible” or convenient in accommodating for their daily activities and the freedom to do their schoolwork where, when, and how they wanted. For example, one participant noted that they liked how, “...most of the time the syllabus and work was already open and available for me to work on anytime, so I could get my work done ahead of time,” and another noted, “I liked the flexibility of the classes. It gave me more time to do my homework.” This flexibility also accommodated the theme of “time management” or exercising control over time spent doing specific tasks to increase productivity. One participant stated, “I liked that it made me able to create my own schedule and do what I wanted to do, freed my time up enough to do what I needed to do.” Another student noted that with remote learning, “I can do things on my own time without distractions and on my own schedule.” This was further demonstrated in the theme “pace” which addressed how remote learning allowed students to work on assignments at their own pace, as far in advance as they wanted, and completing as many at a time as they desired. For example, one participant wrote, “I liked being able to take the course at my own pace,” while another noted, “I liked being able to have more free time to do assignments and exams at my own time and pace.”

Comfort and Environment

“Comfort” referred to remaining in a comfortable environment whenever and wherever students chose. As one participant noted,

I personally enjoyed the online classes for the ability to learn in a comfortable environment (my home/room) which made me feel more comfortable and willing to learn because it did not force me out of my house, and I could easily get on my computer and do class all from the comfort of my own home.

This idea also showed up in the theme of “environment,” which addressed the freedom of choosing the location in which they completed their schoolwork, whether it be at home or in the library at school. “Being able to stay home” or “Being able to do everything at home” was stated in many of the responses associated with this theme.

Asynchronous

The theme “asynchronous” referred to how remote learning or instruction did not have scheduled class

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meeting times. This played into the freedom participants had in the pace of their work, time management, and comfort in choosing their own learning environments in the previous themes. One participant stated, “I liked the online classes because it worked better with my job, and I was able to pace myself differently with the classes instead of having a permanent time that I have to go to class.” Another participant wrote, “I liked the freedom it allows for you to complete assignments at your own time instead of having to show up to class at a specific time that might not work around your work or life schedule,” highlighting the flexibility of asynchronous remote learning.

Attending In-Person Activities

Students were further asked about their feelings toward attending in­person classes and activities that school year and asked to explain why they felt that way (see Table 5).

In-Person Classes

Participants were first asked what their feelings were about attending in­person classes, and 235 participants gave a positive response, 58 a negative response, and 44 a mix of positive and negative feelings. Detailed reasons for those feelings are described next.

Learning. The theme “learning” referred to students’ ability to retain information taught in their courses. For many students, attending in­person classes was positively associated with better learning. For example, one participant said, “I love it, it’s nice being able to sit down in a learning environment and be in class.” Another participant shared a similar opinion while also expressing concern about COVID­19 by noting, “I enjoy it because I learn better this way, but the amount of people not wearing masks and in large groups is unsettling,” denoting a mixed response of positive and negative feelings.

Socialization. The theme “socialization” was defined as interacting and connecting with others. Returning to in­person classes meant more students began socializing with peers and professors again. Many participants in this study expressed a positive feeling about socializing more. One participant wrote,

I really like being back in the classroom, it’s a really hard adjustment back but I really enjoy it. There [are] so many more people on campus and the campus just feels so lively now. It’s so easy to make conversations and build relationships because there are more people on campus.

Although some students expressed excitement about being in­person, other students expressed hesitation and anxiety. One participant stated mixed feelings:

I was excited, but extremely nervous during the fall semester. It was a big difference going from being strictly online to being back on campus with thousands of students. Also during quarantine I was socially besides with my coworkers. Seeing new people in a populated area was overwhelming.

Professor Access. The theme of “professor access” described the students’ perceived level of availability and connection to a professor. Participants made statements such as, “I liked in­person classes, because it helps me connect better with my teachers and the class. It’s hard to stay motivated if there’s nothing or no one there to inspire me to work hard,” and “I like it because it gives me a social outlet and lets me express my thoughts and concerns at the moment rather than waiting on a professor’s email.” While most participants expressed positive feelings about being in­person with professors, some also mentioned hesitation due to COVID­19:

I enjoyed attending in­person classes because I feel that I am better able to understand the material and connect to the professor. However, I did not enjoy attending in­person classes in which most of the students did not wear protective face masks. This made me feel unsafe and extremely disconnected from my peers.

In-Person School Activities

For attending in­person activities at school unrelated to classes, 207 participants shared positive feelings, 37 negative responses, and 39 a combination of positive and negative reactions.

TABLE 5

Themes for Challenges to Students’ Motivation

Classes Learning 87 Process of retaining information taught

Socialization 45 Interacting and connecting with others

Professor access 37 Perceived level of availability and connection to a professor

Activities Socialization 77 Interacting and connecting with others

COVID concerns 20 Apprehension due to COVID-19

“I am excited because I do better learning inside of a classroom.”

“I feel great about being able to see people again on campus.”

“I prefer in-person classes because I feel more connected to the students as well as the professor.”

“Again I like it because it gives me a social outlet and connects me to other students and new friends.”

“Pretty good as they are well planned but I still wished people wore masks to help stop COVID.”

Note. For in-person classes, 8 participants did not note any responses because they were only enrolled in online classes, and 8 participants left this question blank. For in-person activities, 43 participants did not note any responses because they do not attend in-person activities, 13 participants did not note any responses, and 12 participants left this question blank.

College Academic Motivation and COVID-19 | Rickert, Miralles, Boblasky, Wallace, Balducci, Byars, Cabay, McLeod, and Davis

Socialization. Similar to above, participants expressed that one of the top reasons for attending and enjoying in ­ person activities was the increased socialization with others. One participant stated, “I am more excited for more in­person activities to meet new people and make life­changing connections.” Although some participants were very positive about socializing again, other participants shared mixed feelings such as, “I am still a little nervous due to COVID, however I am ready to get back and see all of my peers and friends.”

COVID Concerns. In ­ person activities posed

TABLE 6

Themes for Challenges to Students’ Motivation

Question Theme Count Description Example Quote

Beneficial Masks

Social distancing

Testing

Vaccination

Reasons Contain spread

112 Protective coverings for the mouth/face

39 Maintaining safe distances from others

31 Testing for COVID-19 symptoms and antibodies

31 Administering and receiving vaccinations for COVID-19 strains

56 Limit the spread and infection of COVID-19

“When the pandemic was still at a high, I think the use of masks in spaces that tended to be crowded was beneficial.”

“Still keeping distance, especially from strangers, is very beneficial in limiting the spread of COVID.”

“Especially now that there is rapid testing and PCR test available on campus. It makes it a lot easier for students…”

“Having the COVID-19 vaccines readily available to every student is extremely beneficial. Students are more likely to get it when it is easier to get.”

“Wearing a mask. It slows the spread.”

Bus 43 Riding the free bus/ shuttle across and between campuses “Masks on the bus. Buses are super close quarters and already uncomfortable to ride most of the time, so the extra measure is appreciated.”

Safety 24 Ensure the wellbeing of self and others

To Improve Masks

77 Protective coverings for the mouth/face

Quarantine 22 Isolation from other individuals

“wearing masks and cleaning, because it helps to keep people safe.”

“making sure people wear mask everyday”

“How quickly students are allowed back in the classroom after contracting COVID because although they may be feeling better a few days later even before their quarantine period is up, they may still have it and could potentially spread it to others”

Reasons Mandate 34 Create rules or policies regarding COVID-19 “Masks should be mandatory for everyone.”

Enforcement 27 Ensure people obey COVID-19 rules

“There seems to be a lot of unclarity about when you are required to where a mask or not, or be vaccinated or not, and the rules need to be more clear, stated and inforced.”

Indoors 21 Inside environments, such as classrooms “Wearing masks in classrooms when they are just as close knit as being on the bus”

Note.For beneficial policies, 27 participants did not note any beneficial policies, 7 participants reported not being aware of policies, and 68 participants left this question blank. For in-person activities, 87 participants did not note any policies to improve, and 79 participants left this question blank

a concern of spreading COVID­19 for some participants, demonstrating the theme of “COVID concern.” Participants noted concerns such as, “I’m excited for things to go back to normal yet still scared. This is because COVID is still a thing, and it can be spread at any event at any time.” Other participants expressed more negative feelings about in­person activities by saying, “I do not enjoy attending many other in­person school activities due to the lack of protective face coverings and social distancing. Again, this makes me feel unsafe and disconnected from the community at this college.”

Views on University COVID-19 Policies

Finally, participants were asked about what they perceived as beneficial COVID­19 policies at the university, policies that could be improved, and why they held these views (see Table 6).

Beneficial School Policies

Masks and Social Distancing. The top two themes for beneficial university policies concerned rules about wearing “masks” or other protective face coverings and “social distancing” by maintaining six or more feet between people. These themes aligned well with reasons about “containing the spread” or infection of COVID­19 to other people and maintaining the “safety” or well­being of individuals, especially those riding the free shuttle or “bus” across and between campuses. Emphasizing the containment of COVID­19 spread, one student reported “wearing masks, social distancing, and doing the sickness form before events were beneficial. It helped make sure that people kept their germs to themselves and lowered the risk of transmission.” Likewise, many students were concerned with policies related to the vicinity of others such as added policies for bus riders: “I think encouraging the use of a mask on campus and the bus shuttle is very beneficial because COVID­19 cases have dropped dramatically since the beginning of the school year.”

Testing and Vaccinations. The next two wellregarded policies centered around “testing” for COVID­19 symptoms and antibodies and administering or receiving “vaccinations” for COVID­19 strains. As one student noted, “both getting tested and vaccinated are very important, and it’s nice that you can find all the info you need easily if you need it,” promoting the accessibility of preventative and remedial action. Another student wrote,

I appreciate that they are encouraging vaccines and still encouraging masks. I am immunocompromised, so I strongly feel that everyone should be doing their part to protect their fellow community members,

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and these encouragements and education are helping with that. I also appreciate there being on­campus testing and on­campus vaccine. This is much more accessible and therefore makes people more likely to use those resources.

Policies to Improve

Overall, many participants felt that university policies concerning “masks” and “quarantining,” or isolating from others while sick with COVID ­19, could have been improved. Some students noted that policies needed to be “mandated” or created, whereas others focused on the need to better “enforce” already existing policies. Participants were especially aware of the need for policies regarding “indoor” environments, such as classrooms. One participant stated,

I think that requiring masks would be beneficial because a lot of people do not even realize they have COVID, and therefore they spread it to many many people. I also think that the school should be more proactive when students have COVID because some people return to class while they still have symptoms even though it is after the quarantine period.

To contain the spread of COVID­19, students also felt like policies on quarantining could have been better:

I think the quarantining aspect of their policies could use some fine­tuning. This is a semicrowded school, so it is difficult to track who got sick from who, so I understand that, although I think that students in student living should also all have to quarantine if one person is sick.

Discussion

Using an online, retrospective survey drawing on qualitative data from college students, the current study sought to highlight students’ reflections and perceptions of motivational challenges, needed supports, positive features of remote learning, and views toward returns to in­person activities and university policies during the COVID ­ 19 pandemic. Many students reported experiencing a decrease in their academic motivation because of not being able to attend physical classes and transitioning to remote learning formats. Furthermore, they faced challenges in holding themselves accountable, organizing their schedules, and maintaining engagement in classes. The impact of external factors, such as mental health difficulties, dealing with family situations, and feeling isolated from their peers, also contributed to a decline in their motivation during this time. These findings align with previous research on the plethora

of learning and academic challenges created by the COVID ­ 19 pandemic (e.g., Armstrong et al., 2021; Gonzalez­Ramirez et al., 2021; Gravelle et al., 2022; Hicks et al., 2021; Marler et al., 2021; Ober et al., 2021; Tasso et al., 2021).

Key themes also arose regarding necessary supports during these challenging times. Being organized, personally accountable, and time management could bolster students’ academic learning experiences during the pandemic even in the face of motivational challenges. In addition, students noted that receiving social support, returning to in­person classes, professors’ own engagement and support, access to academic resources on campus as well as other mental health, well­being, and financial resources would be beneficial in bolstering students’ motivation, especially during the COVID­19 pandemic and remote learning (e.g., Kim et al., 2021; Zhou & Zhang, 2021).

The COVID­19 pandemic caused an unprecedented shift toward remote, online learning, and students worldwide have faced various challenges in maintaining motivation levels. Despite this, students in the current study found several positive features to remote learning that they appreciated, such as flexibility with schedules, environments, and asynchronous formats and easier time management and pacing. These findings coincide with previous literature highlighting some of the adaptive and beneficial features of remote learning (e.g., Garris & Fleck, 2020; Smoyer et al., 2020; Stevanović et al., 2021; Usher et al., 2021).

Despite these noted benefits of remote learning, most students reported having positive feelings about returning to in­person classes and activities, especially with regard to perceived improvements for their learning (e.g., Gonzalez ­ Ramirez et al. 2021), the ability to socialize and interact with peers, and gaining greater access to professors (e.g., Garris & Fleck, 2020; Gravelle et al., 2022; Zhou & Zhang, 2021). Nonetheless, several students were still concerned about COVID­19 at these events, in part due to their feelings about university COVID­19 policies. Although students noted many beneficial university policies, they also detailed policies that could be improved. The most noted improvements concerned the creation and continued enforcement of mandates like wearing masks, especially while indoors, and quarantining while sick with COVID­19. Of note, at the time of data collection, the university had removed mask mandates except for those who were positive for COVID­19 and currently symptomatic and reduced quarantine timelines. This could explain why several students in our sample stated that mask mandates and quarantine policies needed to be improved. Beneficial policies also targeted masks, social distancing from

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others, and encouraging testing and vaccination for COVID ­ 19 strains, especially since these policies were perceived as effective in containing the spread of COVID­19 and maintaining the safety of students, staff, and professors. Therefore, students in this sample had mixed feelings about mask mandates: Some perceived them as adequate and beneficial, whereas others called for stricter and continued enforcement of these policies.

Implications for Emergency Remote Learning

Findings from this study on the COVID­19 pandemic may generalize to learning, engagement, and motivation in other extreme, disruptive contexts that students face and offer insights on the resources, classroom adjustments, and policies that can be made and offered to support students during difficult times. For example, there have been historic and current challenges to learning in higher education with natural disasters (e.g., hurricanes, tsunamis, earthquakes, etc.; Wang, 2024), war and political strife (e.g., Galynska & Bilous, 2022), and public health concerns. Many of these events have and continue to result in emergency transitions to online learning like during the COVID­19 pandemic. Knowing that students struggled with the abrupt transition to remote learning (the most prominently identified motivational challenge in this study) instructors and universities can use this information to create smoother, easier adaptations with clearer instructions, seamless protocols, and already prepared online learning materials. As suggested by Wang (2024) regarding natural disasters, ensuring teachers and students are familiar with the use of online learning management systems can assist with continued course access and quality before, during, and after educational disruptions. Further, although students in this study did not always agree on which university policies were beneficial versus needed improvement (i.e., mask mandates), they did note important reasons for these policies, such as maintaining the safety of the self and others by containing the spread of COVID­19, especially for indoor environments like classrooms and buses. In times of unexpected disruptions to learning and quick enforcement of safety policies, universities can continue to transparently explain the reasoning for and usefulness of these policies to college students.

Implications for Practice and Instruction

The supports identified by students in this sample also offer practical implications for higher education instructors and institutions seeking to support the motivation of college students during “normal” instructional times, especially given growth in online learning (i.e., 61% of undergraduate students enrolled in at least one online course in 2021 compared to 36% in 2019; NCES, 2023). Student services, academic centers, and orientation

programs can continue assisting students with bolstering the personal characteristics identified in this study, such as helping students improve their organization skills, accountability, and time management in the face of learning challenges. Across learning contexts (i.e., in ­ person, hybrid, or online), instructors can draw on modeling their own engagement, care, support, and accessibility for their students to help them stay motivated and feel connected to their class and learning, even when not in the same physical space (Kim et al., 2021; Nuñez, 2009; Smoyer et al., 2020; Trolian et al., 2016). Further, colleges and universities can strive to create, support, and market on­campus and off­campus resources to students in promoting their development as whole individuals, beyond simply academics (e.g., mental health resources, financial supports, food and well­being, accessing technology, health reports and testing).

Limitations and Future Directions

Although the current study does expand upon the literature looking at college students’ learning, motivation, and engagement since the COVID­19 pandemic, there are several limitations that future studies could address. This study only examined one university, which had recently returned to in­person classes, and all collected data was retrospective in nature due to collection time (Spring 2022). Ideally, future studies seeking to understand students’ learning experiences with emergency transitions to online instruction, global health concerns, or natural disasters should aim to collect participant responses before, during, and after these unprecedented events to track change over time and limit retrospective biases. Due to this survey being anonymous and conducted online, there was no chance for interviews or follow­up questions. For example, some participants left answers blank, but it was unclear if those participants refused to answer those questions or had nothing they wanted to state like participants who explicitly wrote “Nothing.” Future research could utilize interviews and follow­up questions to dive deeper into participants’ responses and investigate ambiguous or confusing responses.

Further, psychology students were the main major recruited to complete the survey due to department access to a participant recruitment site (i.e., Sona) where information about the survey was located. While the findings from this study are generalizable to college students from psychology majors and minors at universities in the southeast, future research could expand to more majors by utilizing different recruitment techniques. Although the sample itself spanned all years in school and was relatively diverse and representative of the local university population, future research could draw on the voices of students from various cultural, socioeconomic,

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and regional backgrounds at other universities to see if similar patterns and themes emerge for motivational challenges, needed supports, and positive experiences with remote learning and university policies during the COVID­19 pandemic and beyond.

Conclusion

The COVID­19 pandemic has brought about a significant shift in the delivery of education, with remote learning continuing beyond initial pandemic protocols (NCES, 2023). Although this approach offers numerous advantages, such as flexibility and accessibility, some students have encountered various hurdles in maintaining their motivation in the face of remote learning and extenuating circumstances. Therefore, it is imperative for educational institutions to prioritize student motivation to ensure that students remain engaged and achieve optimal academic performance. By doing so, they can help students overcome the challenges associated with disruptions to learning and remote formats and attain their desired academic outcomes.

References

Armstrong, K. E., Goodboy, A. K., & Shin, M. (2022). Pandemic pedagogy and emergency remote instruction: Transitioning scheduled in-person courses to online diminishes effective teaching and student learning outcomes. Southern Communication Journal, 87(1), 56–69. https://doi.org/10.1080/1041794X.2021.2011954

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Galynska, O., & Bilous, S. (2022). Remote learning during the war: Challenges for higher education in Ukraine. International Science Journal of Education & Linguistics, 1(5), 1–6. https://doi.org/10.46299/j.isjel.20220105.01

Garris, C. P., & Fleck, B. (2022). Student evaluations of transitioned-online courses during the COVID-19 pandemic. Scholarship of Teaching and Learning in Psychology, 8(2), 119–139. https://doi.org/10.1037/stl0000229

Gonzalez-Ramirez, J., Mulqueen, K., Zealand, R., Silverstein, S., Reina, C., BuShell, S., & Ladda, S. (2021). Emergency online learning: College students’ perceptions during the COVID-19 pandemic. College Student Journal, 55(1), 29–46. https://dx.doi.org/10.2139/ssrn.3831526

Gravelle, C. D., Brodsky, J. E., Lodhi, A. K., Zapparrata, N. M., Che, E. S., Ober, T. M., & Brooks, P. J. (2022). Remote online learning outcomes in introductory psychology during the COVID-19 pandemic. Scholarship of Teaching and Learning in Psychology, 10(4), 442–470. https://doi.org/10.1037/stl0000325

Hicks, L. J., Caron, E. E., & Smilek, D. (2021). SARS-CoV-2 and learning: The impact of a global pandemic on undergraduate learning experiences. Scholarship of Teaching and Learning in Psychology, 9(3), 235–253. https://doi.org/10.1037/stl0000250

Kim, A. S. N., Khan, S. A., Carolli, A., & Park, L. (2021). Investigating teaching and learning during the coronavirus disease 2019 pandemic. Scholarship of Teaching and Learning in Psychology, 10(3), 308–319. https://doi.org/10.1037/stl0000296

Kinsky, E. S., Merle, P. F., & Freberg, K. (2021). Zooming through a pandemic: An examination of marketable skills gained by university students during the covid-19 crisis. Howard Journal of Communications, 32(5), 507–529. https://doi.org/10.1080/10646175.2021.1965927

Marler, E. K., Bruce, M. J., Abaoud, A., Henrichsen, C., Suksatan, W., Homvisetvongsa, S., & Matsuo, H. (2021). The impact of COVID-19 on university students’ academic motivation, social connection, and psychological well-being. Scholarship of Teaching and Learning in Psychology, 10(3), 320–330. https://doi.org/10.1037/stl0000294

National Center for Education Statistics. (2023). Undergraduate enrollment. Condition of Education. U.S. Department of Education, Institute of Education Sciences. Retrieved July 20, 2023, from https://nces.ed.gov/programs/coe/indicator/cha

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1609406917733847. https://doi.org/10.1177/1609406917733847

Nuñez, A. M. (2009). Latino students’ transitions to college: A social and intercultural capital perspective. Harvard Educational Review, 79(1), 22–48. https://doi.org/10.17763/haer.79.1.wh7164658k33w477

Ober, T. M., Brodsky, J. E., Lodhi, A., & Brooks, P. J. (2021). How did introductory psychology students experience the transition to remote online instruction amid the COVID-19 outbreak in New York City? Scholarship of Teaching and Learning in Psychology, 10(2), 163–178. https://doi.org/10.1037/stl0000264

Smoyer, A. B., O’Brien, K., & Rodriguez-Keyes, E. (2020). Lessons learned from COVID-19: Being known in online social work classrooms. International Social Work, 63(5), 651–654. https://doi.org/10.1177/0020872820940021

Stevanović, A., Božić, R., & Radović, S. (2021). Higher education students’ experiences and opinion about distance learning during the Covid‐19 pandemic. Journal of Computer Assisted Learning, 37(6), 1682–1693. https://doi.org/10.1111/jcal.12613

Tasso, A. F., Hisli Sahin, N., & San Roman, G. J. (2021). COVID-19 disruption on college students: Academic and socioemotional implications. Psychological Trauma: Theory, Research, Practice, and Policy, 13(1), 9–15. https://doi.org/10.1037/tra0000996

Trolian, T. L., Jach, E. A., Hanson, J. M., & Pascarella, E. T. (2016). Influencing academic motivation: The effects of student–faculty interaction. Journal of College Student Development, 57(7), 810–826. https://doi.org/10.1353/csd.2016.0080

Usher, E. L., Golding, J. M., Han, J., Griffiths, C. S., McGavran, M. B., Brown, C. S., & Sheehan, E. A. (2021). Psychology students’ motivation and learning in response to the shift to remote instruction during COVID-19. Scholarship of Teaching and Learning in Psychology, 10(1), 16–29. https://doi.org/10.1037/stl0000256

Wang, J. (2024). Impact of natural disasters on student enrollment in higher education programs: A systematic review. Heliyon, 10(6). https://doi.org/10.1016%2Fj.heliyon.2024.e27705

Zhou, J., & Zhang, Q. (2021). A survey study on US college students’ learning experience in COVID-19. Education Sciences, 11(5), 248. https://doi.org/10.3390/educsci11050248

Author Note

Nicolette P. Rickert https://orcid.org/0000­0001­6584­4413

We have no known conflict of interest to disclose. No funding was received for this project.

Acknowledgements: We would like to thank Cynaria Andrews, Arilyn Baldowski, Ethan Bradley, Kristen Carter, Ariah Lewis, and Jenna Shaffer for their work on this project.

Correspondence concerning this article should be addressed to Nicolette P. Rickert, Department of Psychology, Georgia Southern University, P.O. Box 8041, Statesboro, GA 30460.

Email: nrickert@georgiasouthern.edu

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Effectiveness of a Dissonance-Based Body Image Intervention on Eating Pathology Symptoms

Lindsay M. Howard*1, Spencier R. Ciaralli*1, Ariel E. Schillerberg1,

Angelica C. Morales1, and Rachel I. MacIntyre*2

1Departments of Psychology and Sociology, Augustana University

2Department of Psychology, Millersville University

ABSTRACT. Disordered eating (e.g., binge eating, dietary restriction) continues to be a prevalent mental health concern for female individuals, with roughly 20 million women in the United States meeting diagnostic criteria for an eating disorder at some point in their lives. The Body Project, a dissonance­based body image intervention, is the leading intervention for reducing disordered eating. Although the Body Project appears to be effective at reducing disordered eating behaviors broadly, it is not known whether the Body Project is best suited to target specific types of disordered eating behaviors in a nonclinical sample. Fifty university students who were assigned female at birth attended two 2.5­hour sessions led by trained Body Project facilitators. Participants completed pre­ and postintervention surveys that included the Eating Pathology Symptoms Inventory (EPSI), which assessed self­reported disordered eating behaviors. The EPSI scale is a 45­item measure designed as a multidimensional assessment of disordered eating and includes 8 subscales: body dissatisfaction, binge eating, cognitive restraint, purging, restricting, excessive exercise, negative attitudes about obesity, and muscle building. Results confirmed that the Body Project is effective at decreasing disordered eating behaviors broadly (all ps .08). However, effect sizes suggested that the intervention was most effective at decreasing subscale scores related to attaining thinness (i.e., cognitive restraint, excessive exercise, and restricting; ds > 0.50). Accordingly, the Body Project may be more effective at decreasing symptoms associated with dietary restriction in comparison to binge eating or compensatory behaviors in nonclinical samples (e.g., self­induced vomiting). As such, modifications may be necessary.

Keywords: disordered eating, undergraduates, eating disorders, body image intervention, eating pathology symptom inventory

Body dissatisfaction, defined as displeasure with the size and shape of one’s body, is the leading cause of eating disorder development in the United States (Stice & Shaw, 2002). Body dissatisfaction often arises from sociocultural pressures to meet appearance ideals (i.e., societally sanctioned standards of attractiveness), which in turn can lead to disordered eating (e.g., dieting) to manage dissatisfaction and ultimately put an individual at risk for eating disorder development (Bucchianeri & Neumark­Sztainer, 2014; Lewinsohn et al., 2000).

Disordered eating refers to unhealthy eating patterns that reflect symptoms of an eating disorder. Eating disorders

continue to be a pervasive mental health concern, with high mortality rates and approximately 20 million women in the United States suffering from an eating disorder at some point in their lifetime (Arcelus et al., 2011; Eating Disorders Coalition, 2016). Although body dissatisfaction and subsequent disordered eating can impact anyone, college women tend to be most vulnerable, with as many as 80% reporting body dissatisfaction and 13% meeting criteria for an eating disorder, making well­rounded prevention and intervention efforts a necessity (Eisenberg et al., 2011; Fitzsimmons­Craft, 2011; Stice, Marti et al., 2013). The goal of the present study was to assess the

Howard, Ciaralli, Schillerberg, Morales, and MacIntyre | Body Project and EPSI Scales

effectiveness of a well­established body image intervention at reducing types of disordered eating among nonclinical college women.

Body Image and Disordered Eating in College Women

College women are particularly susceptible to pressures to conform to unrealistic appearance ideals given societal norms, media representation, and peer influence. Westernized society places distinct importance on appearance for young women, which is perpetuated through media and advertising (e.g., Quick & ByrdBredbenner, 2014). Past research has argued that the high rates of disordered eating among young women is due to gendered cultural norms that associate maintaining a certain physical physique with beauty (Betz & Ramsey, 2017; Sypeck et al., 2004). In addition, college marks a transition to adulthood wherein peers become more important sources of influence, and there are subsequent increases in appearance­related comparisons (Duarte et al., 2015; Fitzsimmons­Craft, 2011). Conforming to peer pressure regarding appearance can lead to body dissatisfaction and disordered eating as college students attempt to fit in with fellow undergraduates. More specifically, the influence of negative body talk (i.e., statements made by others speaking negatively of their own or someone else’s body), body checking (e.g., looking in the mirror each time one goes to the bathroom), and increased appearance comparisons (i.e., comparing one’s own physical appearance to that of others) during the college years place individuals at heightened risk for developing body dissatisfaction and disordered eating during this time (Fitzsimmons­Craft et al., 2014). Illustrating the pervasiveness of negative body talk and appearance comparison, Bardone­Cone and colleagues (2016) found that, in a sample of 441 female college students, 56% had talked with their closest female friend about dieting and 22%–39% had talked about appearance comparisons. This same study noted that there was an association between the number of body ­ related topics discussed and an increase in disordered eating. Thus, societal pressures coupled with social dynamics in college environments can perpetuate body dissatisfaction and disordered eating among young women, making interventions aimed at preventing eating disorders in this population of vital importance.

The Body Project

The Body Project, a dissonance­based body image group intervention, is currently the leading body image intervention for college women with research support spanning 25 years and implementation on over 100 college campuses (Becker & Stice, 2017; Stice et al., 2017; Stice

et al., 2021). The Body Project was developed based on dissonance theory, with a goal of facilitating attitudinal and behavioral changes to reduce body dissatisfaction and prevent eating disorder development (Becker & Stice, 2017). According to dissonance theory, when a person’s cognitions do not align with their behaviors, psychological discomfort (i.e., dissonance) arises and motivates behavior change to produce greater consistency and alleviate the discomfort (Festinger, 1957; Stice et al., 2008). In accordance with this theory, the Body Project encourages women to take a counter­attitudinal stance to appearance ideals to create dissonance and provoke change in thoughts and behaviors that align with these ideals (e.g., decreased fad dieting; Stice et al., 2008). This is achieved through a series of verbal, behavioral, and written exercises that encourage women in a group environment to critique the appearance ideal and combat internalization of this ideal. The Body Project demonstrates efficacy across a variety of racial and ethnic groups, gender and sexual identities, ages, and countries (e.g., AlShebali et al., 2021; Brown et al., 2017; Rohde et al., 2017; Shaw et al., 2020; Stice et al., 2021; Unikel­Santoncini et al., 2019).

Although the Body Project appears to be effective at reducing disordered eating behaviors across a variety of demographic variables (Stice et al., 2006; Stice et al., 2008), recent research has begun to investigate whether the Body Project is best suited to prevent onset of specific types of eating disorders (D’Adamo et al., 2023). The most recent Diagnostic and Statistical Manual of Mental Disorders (DSM­5) recognizes three primary eating disorder diagnoses: anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED; American Psychiatric Association, 2013). A diagnosis of AN is categorized by restriction of food intake, an intense fear of weight gain, disturbance of body weight or shape, and low body mass index. A diagnosis of BN is categorized by recurrent episodes of binge eating and compensatory behaviors (e.g., self­induced vomiting, laxative or diuretic use, and over­exercise), and a self­evaluation unduly influenced by shape and weight. BED is categorized by recurrent episodes of binge eating without the compensatory behaviors seen with BN (American Psychiatric Association, 2013). A recent longitudinal study combined data from three Body Project trials to investigate whether the Body Project reduced onset of subthreshold/threshold AN, BN, and BED over long­term follow­up, with results suggesting that the Body Project may most effectively reduce risk of BN (D’Adamo et al., 2023).

The Present Study

The current study expands on the results of the D’Adamo research by investigating whether the Body Project is

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best suited to target specific types of disordered eating behaviors (i.e., unhealthy eating patterns that reflect symptoms of eating disorder development), as assessed by the Eating Pathology Symptom Inventory (EPSI), in a nonclinical sample of college women. Prevalence estimates of clinical eating disorders on college campuses range from 8% to 17% (Allen et al., 2013; Eisenberg et al., 2011; Stice, Marti et al., 2013), but the proportion of students engaging in disordered eating is much higher, especially for young women. In fact, nearly 50% of female college students report engagement in some type of disordered eating behavior (FitzsimmonsCraft, 2011). Disordered eating behaviors include binge eating, inappropriate compensatory behaviors (e.g., self­induced vomiting, excessive exercise), and dietary restriction that align with symptoms of BED, BN, and AN, respectively (American Psychiatric Association, 2013; Eisenberg et al., 2011; Lipson & Sonneville, 2017).

Although the research by D’Adamo and colleagues suggests that the Body Project best ameliorates risk of BN,

TABLE 1

Descriptive Statistics

Note. “Multiple selections” refers to individuals who selected more than one sexual orientation identity.

this study seeks to determine whether the Body Project is more effective at reducing specific types of unhealthy eating habits in a nonclinical sample.

The developers of the Body Project theorized, using the dual­pathway model of bulimic­spectrum eating disorders, that pressure for thinness and internalization of the thin ideal (i.e., a cultural standard of beauty that prioritizes a slim figure) are key risk factors for eating disorder development, and the program was designed to foster dissonance around thin­ideal attitudes and behaviors (Stice et al., 2008; Stice & Van Ryzin, 2019). They have demonstrated that the program reduces thin­ideal internalization and theorized that this is the intervention’s primary mechanism of change (i.e., reductions in thin ideal internalization leads to reductions in disordered eating; Marchand et al., 2011; Stice et al., 2008). More recent research suggests that body­ideal messages have shifted to include more than an emphasis on thinness, including messages that promote athleticism and curves (Betz & Ramsey, 2017; Uhlmann et al., 2020). Accordingly, revisions have been made to the Body Project to make it more inclusive of other appearance ideals, such as changing the language from “thin ideal” to “appearance ideal.” Despite these changes, this study hypothesized that the Body Project would be most effective at reducing restrictive eating behaviors and disordered eating tied to attaining thinness given the program’s design and previous success at reducing thin ideal internalization.

Methods

Participants

The sample included college students (N = 50) who were assigned female at birth (AFAB). Forty­five participants identified as cisgender women and five identified as gender queer. The average age of the sample was 21.67 (SD = 4.04). There were 17 first­year students, 13 sophomores, 7 juniors, 10 seniors, and 3 graduate students. Average body mass index was 27.29 (SD = 5.84), which is considered overweight but like other college samples (Center for Disease Control and Prevention, 2024). Participants identified as 24 White, 3 Latina, 18 Black, 3 Asian, and 2 participants that selected “Other.” Thirtyeight participants identified as straight, 6 identified as same ­ gender­ loving, and 6 selected multiple sexual orientation identities. All descriptive statistics and a breakdown of the two data sets can be found in Table 1.

Two data sets were combined in analysis. In the first data set (collected at a small liberal arts university in the Midwest between 2022 and 2023), 45 people completed a screening and interest questionnaire and were scheduled to attend Body Project groups (approximately 12 people per group). Sixteen people did not attend the initial group despite apparent interest in the intervention. Eight

Howard,

Ciaralli, Schillerberg, Morales, and MacIntyre | Body Project and EPSI Scales

people who attended the first group did not attend the second group. There were validity concerns with one participant’s postintervention data and another person, who attended both groups, did not fill out surveys beyond demographic data, leaving 19 participants. In the second data set (collected at a large public university in the southeast between 2019 and 2020), of the 79 participants who expressed apparent interest, 32% did not attend the first Body Project session (n = 18 did not attend their scheduled session, n = 22 were scheduled for groups that were canceled following pandemic closures). Of the 39 who attended the first Body Project session, eight did not attend their second group session, leaving 31 participants. There were 50 total participants between the two data sets

Body Project Intervention

As mentioned, the Body Project is an intervention based on dissonance theory that encourages individuals to change their perspective on society’s “appearance ideal” while simultaneously supporting women to challenge negative body­focused situations and promote change. With extensive research traversing 25 years, the Body Project has reliably demonstrated a reduction in body dissatisfaction and disordered eating (Becker & Stice, 2017; Stice et al., 2021). These successes are achieved through a manualized intervention that includes verbal, behavioral, and written exercises meant to inspire women to challenge harmful societal pressures and messages that occur over two, 2.5­hour in­person sessions one week apart in accordance with Body Project protocol (Becker & Stice, 2017).

Procedure

This research study was conducted on two campuses: a small liberal arts university in the Midwest (dataset 1) and a large public university in the Southeast (dataset 2). Approval for the study was obtained from the Old Dominion University and Augustana University Institutional Review Boards. Group facilitators were undergraduate or clinical psychology doctoral students trained by certified administrators from the Body Project Collaborative. All facilitators attended a 15­hour training over two days on how to lead the Body Project intervention using the scripted Body Project manual. In dataset 1, participants were recruited through an undergraduate research pool and class announcements. In dataset 2, participants were recruited through class and student organization announcements, flyers, and tabling events. Those interested were directed to complete an online survey where further information about the Body Project was provided and their student status, email address, and availability were collected. Participants were

scheduled for two, 2.5­hour sessions one week apart. Before the first group session, participants provided both verbal and written consent and completed the preintervention survey. The preintervention survey was developed using Qualtrics and administered via tablets or participants’ personal devices. Once all group members completed the preintervention survey, the trained facilitators initiated the first Body Project session. Consistent with the Body Project two­session manual, participants were asked to complete three homework exercises between the first and second sessions. After the second session, participants completed a postintervention survey. Both the pre ­ and postintervention surveys contained a variety of questionnaires designed to assess body image, eating habits, and other health outcomes (e.g., depression, self­esteem, social support).

Measures

Demographics

Demographic information was collected via a brief self­report questionnaire. The questionnaire assessed participants’ age, sex, gender, race, year in school, sexual orientation, body mass index, and other identifying information.

Eating Pathology Symptoms Inventory (EPSI; Forbush et al., 2013)

The EPSI is a 45­item measure designed as a multidimensional assessment of eating pathology (e.g., binge eating, caloric restriction, self­induced vomiting, over­exercise). The EPSI was originally designed to assess eating pathology over the past four weeks, but this number was changed to one week in the present study to allow for comparisons to be made between baseline and postintervention measures. A sample item is, “I felt that I needed to exercise nearly every day.” Response options range from 0 (never) to 4 (very often). There are 8 subscales: body dissatisfaction (disappointment in one’s body shape/weight), binge eating (consumption of a large amount of food and feeling a loss of control over eating), cognitive restraint (mental efforts to prevent or limit food consumption), purging (laxative use, self­induced vomiting, etc.), restricting (physical efforts to prevent or limit food consumption), excessive exercise (physical exercise that is compulsive and often of increased intensity), negative attitudes about obesity (negative attitudes towards or opinions of those who are obese or overweight), and muscle building (compulsion to develop increased muscle definition). Analyses were conducted on each subscale score. Item responses with higher scores suggest higher levels of these various facets of eating pathology. There is evidence of test­retest reliability (r = .73) and good to excellent

PSI CHI

convergent and discriminant validity in undergraduate samples (Forbush et al., 2013; Forbush et al., 2014). The EPSI subscales are correlated with other measures of disordered eating (e.g., the Body Dissatisfaction subscale is positively correlated with a well­established shape concern subscale [ r = .72]) and not correlated with measures of general psychopathology (e.g., the Binge Eating subscale is not significantly correlated with an assessment of general depression [r = .15]; Forbush et al., 2013). The EPSI subscales are also invariant across a variety of samples (Forbush et al., 2014; Richson et al., 2021). Cronbach’s alpha for the present sample was .90 at preintervention and .96 at postintervention, which suggests excellent internal consistency.

Results

Data Management

Box plots of total scores revealed one outlier on the muscle building subscale of the EPSI at time 1 and was subsequently winsorized. There was no missing data in Data Set 1, and missingness was relatively low in Data Set 2 (ranging from 9% to 13%). EM imputation was used to handle missing data in Data Set 2. Total scores were normally distributed, as evidenced by skewness and kurtosis.

Analyses

A post­hoc power analysis using G*Power 3.1 (Faul et al., 2007) suggested that this study was powered to detect effects. To control for risk of Type I error, a Bonferroni correction was applied. With a Bonferroni correction, the significance level was adjusted from p < .05 to

TABLE 2

Note. EPSI = Eating Pathology Symptom Inventory. SD = Standard Deviation. BD = Body Dissatisfaction. BE = Binge Eating. CR = Cognitive Restraint. PUR = Purging. RES = Restricting. EE = Excessive Exercise.

NAAO = Negative Attitudes About Obesity. MB = Muscle Building. Bonferroni correction applied to control for multiple comparisons. Corrected p = .006. * p < .05. ** p < .01. *** p < .001.

p < .006. All results remained significant at this adjusted level. For paired­samples t tests with an observed effect size of d = 0.50, a sample size of N = 50, and an alpha level .006, this study had a 92% probability of detecting an effect. Paired­samples t tests were conducted comparing pre­ and postintervention scores using SPSS. EPSI scores did not significantly differ between the two campuses where the intervention was performed. As shown in Table 2, there were decreases in all eight subscales of the EPSI. All decreases were significant ( p s < .006) except for the Purging and Muscle Building subscales (ps > .006). Subscales varied in their effect sizes with Body Dissatisfaction ( t = 7.06; d = 1.00), Cognitive Restraint (t = 4.46; d = 0.63), Excessive Exercise (t = 4.12; d = 0.58), and Restricting (t = 3.54; d = 0.50) exhibiting the largest effect sizes, and Binge Eating (t = 3.24; d = 0.46), Negative Attitudes About Obesity (t = 2.65 d = 0.38), Purging ( t = 1.80; d = 0.26), and Muscle Building ( t = 1.69; d = 0.24) exhibiting the smallest effect sizes.

Discussion

Overall, results suggested that the Body Project is effective at decreasing disordered eating behaviors, as measured by EPSI subscale scores, in AFAB college students. However, the strength of the effect size varied depending on the type of disordered eating behavior, such that changes in Purging, Binge Eating, Negative Attitudes about Obesity, and Muscle Building exhibited the smallest effect sizes, and Cognitive Restraint, Excessive Exercise, Restricting, and Body Dissatisfaction exhibited the largest effect sizes.

These results become clearer when placed in the context of the Body Project purpose and design. The Body Project is a dissonance­based body image intervention aimed at helping college women feel better about their bodies (Becker & Stice, 2017), and it follows that the largest changes following intervention would be to body dissatisfaction. In addition, the Body Project is intended to reduce body dissatisfaction by decreasing the pursuit of the appearance ideal. In the United States, the most sanctioned standard of attractiveness has been the “thin­ideal” (i.e., a slender physique with little body fat; Thompson & Stice, 2001), although these standards have been changing to include a focus on athleticism and curves (Betz & Ramsey, 2017). The Body Project is better positioned to change disordered eating behaviors associated with attaining thinness, such as cognitive restraint (i.e., cognitive efforts to limit or avoid eating), excessive exercise, and restricting food intake as opposed to disordered eating behaviors that tend to be driven by emotion regulation, such as binge eating and compensatory behaviors (e.g., purging). Emotion

Howard,

Ciaralli, Schillerberg, Morales, and MacIntyre | Body Project and EPSI

Scales

regulation models of binge eating and purging suggest that individuals engage in these behaviors to help distract from or mitigate negative feelings in the short­term due to lacking more adaptive coping strategies (Lavender et al., 2015). Beyond the scope of the intervention, most of the subscales that exhibited small effects are considered low frequency behaviors in nonclinical samples, which was evident in preintervention scores (Negative Attitudes About Obesity = 4.08, Purging = 1.82, Muscle Building = 2.62), and as such, this limits variability in results.

Clinical Implications

Perhaps due to prevention programs such as the Body Project, female body dissatisfaction has declined in the last few decades (Cash et al., 2004; Karazsia et al., 2017), but continued investigation is needed. Although successful in decreasing body dissatisfaction and disordered eating symptomatology overall, the results of the present study suggest that the Body Project is not as effective for reducing symptoms of binge eating and purging in a nonclinical sample. Given that the intervention largely focuses on deconstructing the thin ideal, it makes sense that the intervention is most effective for symptoms associated with AN, as this disorder is characterized by an intense fear of weight gain and restriction of caloric intake (American Psychiatric Association, 2013). It should be noted that a recent longitudinal study by D’Adamo and colleagues (2023) found that the Body Project effectively reduces risk of BN but is less successful at reducing risk of AN and BED. It could be that the purging results in the present study were due to a sample that generally did not engage in purging behavior. However, this study supports the findings from D’Adamo that the Body Project may be less effective at buffering against risk for BED. Consequently, modifications to the Body Project may be necessary for it to be more effective for people with binge eating type symptoms. For example, the Body Project may add an additional module that incorporates emotion regulation tools, or an entirely different manual may be warranted that is centered on binge eating behaviors. In fact, the Body Project has been modified in the past to best meet the needs of their participant sample, such as a tailored adaptation targeting sorority members (Becker et al., 2008; Becker et al., 2010), adolescent girls (Atkinson & Wade, 2016), young women of different races and ethnicities (Stice et al., 2021), men (Brown et al., 2017), individuals from different cultures (AlShebali et al., 2021; UnikelSantoncini et al., 2019), as well as female college student athletes (Becker et al., 2012). These studies could be used as a model in future modifications when considering all types of disordered eating.

The present study reinforces the importance of meeting the unique needs of varying disordered

eating symptoms. Historically, dietary restriction has received more attention than binge eating, and perhaps unsurprisingly, BED was only recognized as an eating disorder in the fifth version of the DSM (American Psychiatric Association, 2013). This lack of attention can have far reaching consequences. For example, cultural values around thinness can even inform news reporting on various disordered eating behaviors, with AN and BN often portrayed in a sympathetic light, whereas binge eating is more commonly attributed to poor individual choices or moral character (Saguy & Gruys, 2010). Seeing as a part of the Body Project group intervention entails problematizing the media’s influence on one’s perception of their body image, this may warrant integrating a section that more readily addresses biases around binge eating. It is imperative, on multiple levels, to address the ways in which different types of disordered eating behaviors may be perceived, treated, or missed entirely.

Limitations and Future Directions

The purpose of the Body Project is to challenge the psychological impacts that internalization of appearance ideals can have on its participants, and create more positive, healthy attitudes surrounding this issue. This intervention yielded significant decreases in participants’ disordered eating behaviors, but improvements are warranted.

First, although this study analyzed data from the preintervention and immediate postintervention surveys, previous research demonstrates that participants maintain their reductions until at least 1­year post­intervention (Stice, Rohde et al., 2013). Future research should investigate whether the preliminary findings from this study are maintained over time. Second, it is notable that the study did not include a control group. Lastly, a major limitation of the present study is the relatively small sample size that consisted of mostly Black and White, straight, cisgender, college women. Therefore, results cannot be generalized to men, LGBTQIA+, clinical samples, or races beyond Black and White. The impacts of body dissatisfaction and disordered eating may differ for those who face the additional challenge of being a member of a systemically minoritized group; continued modifications that are tailored to diverse participants might allow for a safer space to share unique pressures to conform to culture­specific appearance ideals. However, Stice and colleagues (2021) found that the intervention produces similar reductions in eating disorder risk factors and symptoms for Asian, Black, Latina, Native American, and White young women. The authors theorized that this may be related to the participant­driven nature of discussions within the intervention. These discussions may incorporate other cultural appearance standards beyond

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PSI CHI

JOURNAL OF PSYCHOLOGICAL RESEARCH

Body Project and EPSI Scales | Howard, Ciaralli, Schillerberg, Morales, and MacIntyre

the thin­ideal, such as expectations to have a curvy body that have been more commonly endorsed by Black and Latinx cultures in the past though are also influencing White and other racial group appearance standards today (Overstreet et al., 2010). Research that examines the appearance standards discussed among groups and other nuances within the experiences of members of minoritized groups in the Body Project is needed.

Conclusion

In summary, although the Body Project has demonstrated success in alleviating body dissatisfaction and associated disordered eating behaviors, ongoing adaptation is essential for its continued relevance and effectiveness across diverse populations with varying symptomatology, such as binge eating. This research contributes valuable insights to the ongoing discourse on body image interventions and lays the foundation for future endeavors aimed at enhancing the well­being of individuals struggling with body dissatisfaction and engagement in disordered eating.

References

Allen, K. L., Byrne, S. M., Oddy, W. H., & Crosby, R. D. (2013). DSM-IV-TR and DSM-5 eating disorders in adolescents: Prevalence, stability, and psychosocial correlates in a population-based sample of male and female adolescents. Journal of Abnormal Psychology, 122(3), 720–732. https://doi.org/10.1037/a0034004

AlShebali, M., Becker, C., Kellett, S., AlHadi, A., & Waller, G. (2021). Adapting the body project to a non-western culture: A dissonance-based eating disorder prevention program for Saudi women. Eating and Weight Disorders, 26, 2503–2512. https://doi.org/10.1007/s40519-021-01104-9

Atkinson, M. J., & Wade, T. D. (2016). Does mindfulness have potential in eating disorders prevention? A preliminary controlled trial with young adult women. Early Intervention in Psychiatry, 10(3), 234–245. https://doi.org/10.1111/eip.12160

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.

Arcelus, J., Mitchell, A. J., Wales, J., & Nielsen, S. (2011). Mortality rates in patients with anorexia nervosa and other eating disorders. A meta-analysis of 36 studies. Archives of General Psychiatry, 68(7), 724–731. https://doi.org/10.1001/archgenpsychiatry.2011.74

Bardone-Cone, A. M., Balk, M., Lin, S. L., Fitzsimmons-Craft, E. E., & Goodman, E. L. (2016). Female friendships and relations with disordered eating. Journal of Social and Clinical Psychology, 35(9), 781–805.

https://doi.org/10.1521/jscp.2016.35.9.781

Becker, C. B., Bull, S., Schaumberg, K., Cauble, A., & Franco, A. (2008). Effectiveness of peer led eating disorders prevention: A replication trial. Journal of Consulting and Clinical Psychology, 76(2), 347–354. https://doi.org/10.1037/0022-006X.76.2.347

Becker, C. B., McDaniel, L., Bull, S., Powell, M., McIntyre, K. (2012). Can we reduce eating disorder risk factors in female college athletes? A randomized exploratory investigation of two peer-led interventions. Body Image, 9(1), 31–42. https://doi.org/10.1016/j.bodyim.2011.09.005

Becker, C. B., & Stice, E. (2017). From efficacy to effectiveness to broad implementation: Evolution of the Body Project. Journal of Consulting and Clinical Psychology, 85(8), 767–782. https://doi.org/10.1037/ccp0000204

Becker, C. B., Wilson, C., Williams, A., Kelly, M., McDaniel, L., & Elmquist, J. (2010). Peer facilitated cognitive dissonance versus healthy weight eating disorders prevention: a randomized comparison. Body Image, 7(4), 280–288. https://doi.org/10.1016/j.bodyim.2010.06.004

Betz, D. E., & Ramsey, L. R. (2017). Should women be “All About That Bass?”: Diverse body ideal messages and women’s body image. Body Image, 22, 18–31. https://doi.org/10.1016/j.bodyim.2017.04.004

Brown T. A., Forney K. J., Pinner D., & Keel P. K. (2017). A randomized controlled trial of The Body Project: More Than Muscles for men with body

dissatisfaction. International Journal of Eating Disorders, 50(8), 873–883. https://doi.org/10.1002/eat.22724

Brown, T. A., & Keel, P. K. (2015). A randomized controlled trial of a peer co-led dissonance-based eating disorder prevention program for gay men. Behaviour Research and Therapy, 74, 1–10. https://doi.org/10.1016/j.brat.2015.08.008

Bucchianeri, M. M., & Neumark-Sztainer, D. (2014). Body dissatisfaction: An overlooked public health concern. Journal of Public Mental Health, 13(2), 64–69. https://doi.org/10.1108/JPMH-11-2013-0071

Cash, T. F., Morrow, J. A., Hrabosky, J. I., & Perry, A. A. (2004). How has body image changed? A cross-sectional investigation of college women and men from 1983 to 2001. Journal of Consulting and Clinical Psychology, 72(6), 1081–1089. https://doi.org/10.1037/0022-006X.72.6.1081

Center for Disease Control and Prevention. (2024, September). Assessing your weight https://www.cdc.gov/healthyweight/assessing/index.html

D’Adamo, L., Ghaderi, A., Rohde, P., Gau, J. M., Shaw, H., & Stice, E. (2023). Evaluating whether a peer-led dissonance-based eating disorder prevention program prevents onset of each eating disorder type. Psychological Medicine, 53(15), 7214–7221. https://doi.org/10.1017/S0033291723000739

Duarte, C., Ferreira, C., Trindade, I. A., & Pinto-Gouveia, J. (2015). Body image and college women’s quality of life: The importance of being selfcompassionate. Journal of Health Psychology, 20, 754–764. https://doi.org/10.1177/1359105315573438

Eating Disorders Coalition. (2016). Facts about eating disorders: What the research shows Eating Disorders Coalition. https://emilyprogram.com/wp-content/uploads/edc-fact-sheet-2019.pdf

Eisenberg, D., Nicklett, E. J., Roeder, K., & Kirz, N. E. (2011). Eating disorder symptoms among college students: Prevalence, persistence, correlates, and treatment-seeking. Journal of American College Health, 59(8), 700–707. https://doi.org/10.1080/07448481.2010.546461

Faul, F., Erdfelder, E., Lang, A., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/bf03193146

Festinger, L. (1957). A theory of cognitive dissonance. Stanford University Press. Fitzsimmons-Craft, E. E. (2011). Social psychological theories of disordered eating in college women: Review and integration. Clinical Psychology Review, 31(7), 1224–1237. https://doi.org/10.1016/j.cpr.2011.07.011

Fitzsimmons-Craft, E. E., Bardone-Cone, A. M., Bulik, C. M., Wonderlich, S. A., Crosby, R. D., & Engel, S. G. (2014). Examining an elaborated sociocultural model of disordered eating among college women: The roles of social comparison and body surveillance. Body Image, 11(4), 488–500. https://doi.org/10.1016/j.bodyim.2014.07.012

Forbush, K. T., Wildes, J. E., & Hunt, T. K. (2014). Gender norms, psychometric properties, and validity for the Eating Pathology Symptoms Inventory. International Journal of Eating Disorders, 47(1), 85–91. https://doi.org/10.1002/eat.22180

Forbush, K. T., Wildes, J. E., Pollack, L. O., Dunbar, D., Luo, J., Patterson, K., Petruzzi, L., Pollpeter, M., Miller, H., Stone, A., Bright, A., & Watson, D. (2013). Development and validation of the Eating Pathology Symptoms Inventory (EPSI). Psychological Assessment, 25(3), 859–878. https://doi.org/10.1037/a0032639

Karazsia, B. T., Murnen, S. K., & Tylka, T. L. (2017). Is body dissatisfaction changing across time? A cross-temporal meta-analysis. Psychological Bulletin, 143(3), 293–320. https://doi.org/10.1037/bul0000081

Lavender, J. M., Wonderlich, S. A., Engel, S. G., Gordon, K. H., Kaye, W. H., & Mitchell, J. E. (2017). Dimensions of emotion dysregulation in anorexia nervosa and bulimia nervosa: A conceptual review of the empirical literature. Clinical Psychology Review, 40, 111–122. https://doi.org/10.1016/j.cpr.2015.05.010

Lewinsohn, P. M., Striegel-Moore, R. H., & Seeley, J. R. (2000). Epidemiology and natural course of eating disorders in young women from adolescence to young adulthood. Journal of the American Academy of Child and Adolescent Psychiatry, 39(10), 1284–1292. https://doi.org/10.1097/00004583-200010000-00016

Lipson, S. K., & Sonneville, K. R. (2017). Eating disorder symptoms among undergraduate and graduate students at 12 U.S. colleges and universities. Eating Behaviors, 24, 81–88. https://doi.org/10.1016/j.eatbeh.2016.12.003

Marchand, E., Stice, E., Rohde, P., & Becker, C. B. (2011). Moving from efficacy to effectiveness trials in prevention research. Behaviour Research and Therapy, 49, 32–41. https://doi.org/10.1016/j.brat.2010.10.008

Overstreet, N. M., Quinn, D. M., & Agocha, V. B. (2010). Beyond thinness: The influence of a curvaceous body ideal on body dissatisfaction in Black and White women. Sex Roles: A Journal of Research, 63, 766.

Howard, Ciaralli, Schillerberg, Morales, and MacIntyre | Body Project and EPSI Scales

https://doi.org/10.1007/s11199-010-9887-y

Quick, V. M., & Byrd-Bredbenner, C. (2014). Disordered eating, socio-cultural media influencers, body image, and psychological factors among a racially/ ethnically diverse population of college women. Eating Behaviors, 15(1), 37–41. https://doi.org/10.1016/j.eatbeh.2013.10.005

Richson, B. N., Forbush, K. T., Chapa, D. A. N., Gould, S. R., Perko, V. L., Johnson, S. N., Christensen, K. A., Swanson, T. J., & Tregarthen, J. (2021). Measurement invariance of the Eating Pathology Symptoms Inventory (EPSI) in adolescents and adults. Eating Behaviors, 42, 101538. https://doi.org/10.1016/j.eatbeh.2021.101538

Rohde, P., Stice, E., Shaw, H., Gau, J. M., & Ohls, O. C. (2017). Age effects in eating disorder baseline risk factors and prevention intervention effects. International Journal of Eating Disorders, 50(11), 1273–1280. https://doi.org/10.1002/eat.22775

Saguy, A. C., & Gruys, K. (2010). Morality and health: News media constructions of overweight and eating disorders. Social Problems, 57(2), 231–250. https://doi.org/10.1525/sp.2010.57.2.231

Shaw, H., Rohde, P., Desjardins, C. D., & Stice, E. (2020). Sexual orientation correlates with baseline characteristics but shows no moderating effects of dissonance-based eating disorder prevention programs for women. Body Image, 32, 94–102. https://doi.org/10.1016/j.bodyim.2019.11.006

Stice, E., Marti, C., & Rohde, P. (2013). Prevalence, incidence, impairment, and course of the proposed DSM-5 eating disorder diagnoses in an 8-year prospective community study of young women. Journal of Abnormal Psychology, 122(2), 445–457. https://doi.org/10.1037/a0030679

Stice, E., Marti, C. N., Spoor, S., Presnell, K., & Shaw, H. (2008). Dissonance and healthy weight eating disorder prevention programs: Long-term effects from a randomized efficacy trial. Journal of Consulting and Clinical Psychology, 76(2), 329–340. https://doi.org/10.1037/0022-006x.76.2.329

Stice, E., Onipede, A., Shaw, H., Rohde, P., & Gau, J. (2021). Effectiveness of the body project eating disorder prevention program for different racial and ethnic groups and an evaluation of the potential benefits of ethnic matching. Journal of Consulting and Clinical Psychology, 89(12), 1007–1019. https://doi.org/10.1037/ccp0000697

Stice, E., Rohde, P., Durant, S., Shaw, H., & Wade, E. (2013). Effectiveness of peerled dissonance-based eating disorder prevention groups: Results from two randomized pilot trials. Behaviour Research and Therapy, 51(4–5), 197–206.  https://doi.org/10.1016/j.brat.2013.01.004

Stice, E., Rohde, P., Shaw, H., & Gau, J. M. (2017). Clinician-led, peer-led, and internet delivered dissonance-based eating disorder prevention programs: Acute effectiveness of these delivery modalities. Journal of Consulting and Clinical Psychology, 85(9), 883–895. https://doi.org/10.1037/ccp0000211

Stice, E., & Shaw, H. E. (2002). Role of body dissatisfaction in the onset and maintenance of eating pathology: A synthesis of research findings. Journal of Psychosomatic Research, 53(5), 985–993. https://doi.org/10.1016/s0022-3999(02)00488-9

Stice, E., Shaw, H., Burton, E., & Wade, E. (2006). Dissonance and healthy weight eatingdisorder prevention programs: A randomized efficacy trial. Journal of Consulting and Clinical Psychology, 74(2), 263–275. https://doi.org/10.1037/0022-006x.74.2.263

Stice, E., & Van Ryzin, M. J. (2019). A prospective test of the temporal sequencing of risk factor emergence in the dual pathway model of eating disorders. Journal of Abnormal Psychology, 128(2), 119–128. https://doi.org/10.1037/abn0000400

Sypeck, M. F., Gray, J. J., & Ahrens, A. H. (2004). No longer just a pretty face: Fashion magazines’ depictions of ideal female beauty from 1959 to 1999. International Journal of Eating Disorders, 36(3), 342–347. https://doi.org/10.1002/eat.20039

Thompson, J. K., & Stice, E. (2001). Thin-ideal internalization: Mounting evidence for a new risk factor for body-image disturbance and eating pathology. Current Directions in Psychological Science, 10(5), 181–183. https://doi.org/10.1111/1467-8721.00144

Uhlmann, L. R., Donovan, C. L., & Zimmer-Gembeck, M. J. (2020). Beyond the thin ideal: Development and validation of the Fit Ideal Internalization Test (FIIT) for women. Psychological Assessment, 32(2), 140–153.  https://doi.org/10.1037/pas0000773

Unikel-Santoncini, C., de León-Vázquez, C., Rivera-Márquez, J., BojorquezChapela, I., & Méndez-Ríos, E. (2019). Dissonance-based program for eating disorders prevention in Mexican university students. Psychosocial Intervention, 28(1), 29–35. https://doi.org/10.5093/pi2018a17

Author Note

Lindsay M. Howard https://orcid.org/0000­0003­2408­9268 Spencier R. Ciaralli https://orcid.org/0000­0003­4718­3986 We have no known conflict of interest to disclose. This study was funded in part by a research grant from Augustana University awarded to Lindsay Howard and Spencier Ciaralli. Correspondence concerning this article should be addressed to Lindsay M. Howard, Department of Psychology, 2001 S Summit Ave., Sioux Falls, SD 57197, United States.

Email: Lindsay.Howard@augie.edu

LGBTQ Minus: Predictors of Anti-Asexual Bias Among Straight, Gay, and Bisexual Individuals

Nicholas A. Ashenfelter and Kristina Howansky* Department of Psychology, St. Mary’s College of Maryland

ABSTRACT. Previous research on prejudice against sexual minority individuals has predominantly focused on gay and bisexual identities (Parmenter et al., 2020a). In the present work (n = 299), we tested for differences in asexual prejudice across belief systems (i.e., Social Dominance Orientation [SDO], moral disengagement, and traditional gender role acceptance) and sexual orientations (i.e., straight, bisexual, and gay). We found that straight individuals exhibit more asexual prejudice in domains of acephobia and social distancing than gay (p = .002; p = .003) or bisexual individuals ( p < .001; p = .02), but gay and bisexual individuals did not significantly differ (p = .25; p = .55). SDO and moral disengagement were significantly associated with acephobia (r = .56, p < .001; r = .43, p < .001), social distance ( r = ­ .43, p < .001; r = ­ .29, p < .001), and feelings towards asexual individuals (r = ­ .32, p < .001; r = ­.18, p = .002). Traditional gender role acceptance was significantly associated with acephobia and social distancing (r = .31, p < .001; r = ­.12, p = .04). This work contributes to the literature on bias towards asexual individuals, SDO, traditional gender role acceptance, and moral disengagement, with implications for future interventions that may combat asexual prejudice and improve inclusion and equity within and outside sexual minority spaces.

Keywords: asexuality, prejudice, LGBTQ+, social dominance orientation, acephobia

Diversity badge earned for conducting research focusing on aspects of diversity.

Preregistration+, Open Data, and Open Materials badges earned for transparent research practices. Preregistration can be viewed at https://osf.io/vpqc9. Materials can be accessed at https://osf.io/ w7h28. Data are available at https://osf.io/zw3au

Asexual individuals, those who have little or no romantic or sexual attraction to others (Lexicon Library.LGBT, n.d.), are a relatively unstudied sexual minority group. However, asexual individuals report higher levels of anxiety and depression compared to straight individuals (Borgogna et al., 2019), rendering study necessary to identify the causes of this discrepancy as well as potential means to counter it. Compounding

this issue, recent research has demonstrated the presence of inequities, both outside of and within the sexual minority community, that negatively impact asexual individuals (McInroy et al., 2022; Parmenter et al., 2020b). These inequities can be considered a manifestation of acephobia—negative attitudes and resentments toward asexual people (Lexicon Library. LGBT, n.d.). Although some work has identified belief

systems associated with acephobia, such as right­wing authoritarianism (Hoffarth et al., 2016; MacInnis & Hodson, 2012) and religiosity (Iraklis, 2023; Vu et al., 2021), there is limited work examining factors of prejudice from within the sexual and gender minority community. The present work contributes to the literature on anti­asexual bias by examining whether individual difference factors (i.e., sexual orientation1, Social Dominance Orientation [SDO], traditional gender roles, moral disengagement) function as predictors of acephobia, social distancing from asexual individuals, and negative feelings toward asexual individuals.

Personality Predictors of Asexual Prejudice

Past research has identified the presence of prejudice towards asexual individuals in countries such as Greece, Canada, the United States, and Australia (Iraklis, 2023; McInroy et al., 2022; Vu et al., 2021). These feelings may manifest in discriminatory behaviors exhibited towards asexual individuals, such as greater discomfort towards renting to and hiring asexual individuals both in correlational research (Hoffarth et al., 2016) and compared to straight individuals (MacInnis & Hodson, 2012). Findings corroborating the impact and existence of this social issue serve as a foundation for further research into different personality factors that may explain much of the manifestation of this form of prejudice. Observed predictors of asexual prejudice include rightwing authoritarianism (Hoffarth et al., 2016; MacInnis & Hodson, 2012; Thorpe & Arbeau, 2020), religiosity (Iraklis, 2023; Rye & Goldszmidt, 2023; Vu et al., 2021), and identifying as conservative (Hoffarth et al., 2016), or right­wing (Iraklis, 2023). Knowledge of additional predictors would greatly aid future work that seeks to correct erroneous beliefs about asexual people and ensure more inclusive and accepting views of asexual individuals.

An additional potential predictor of asexual prejudice may be SDO, which is the belief in intrinsic in­group superiority and out­group inferiority, as well as that social structures should mirror this assessment (Pratto et al., 1994). Individuals with high SDO are particularly prejudiced against groups perceived as inferior or those who pose a threat in a perceived social hierarchy (Cohrs & Asbrock, 2009). In line with individual differences theory, the theory that prejudice towards one group is associated with prejudice towards other groups (Allport, 1954), SDO has been found to predict multiple types of prejudice, including racism (Duriez & Van Hiel, 2002) and homophobia (Weber 1Throughout this manuscript we use the word “gay” as an umbrella term to describe the sexual orientation of people attracted to same­gender individuals regardless of gender identity (e.g., men attracted to men and women attracted to women).

& Gredig, 2018). A predictor of prejudice among many demographic groups would then be expected to predict negative bias against asexual individuals as another sexual minority group, as has been found in past research (Bittle & Anderson, 2023; Hoffarth et al., 2016; MacInnis & Hodson, 2012; Thorpe & Arbeau, 2020). This finding is particularly supported given the association between anti­asexual bias and benevolent and hostile sexism (Hoffarth et al., 2016), as well as negative views towards gay and bisexual individuals (MacInnis & Hodson, 2012), or those who are romantically or sexually attracted to multiple genders (Lexicon Library.LGBT, n.d.). Given the extensive past literature connecting SDO to prejudice, it is likely that SDO would predict asexual prejudice even when conceptualized along multiple dimensions, such as acephobia, social distance from asexual individuals, and negative feelings towards asexual individuals.

Endorsement of traditional gender roles may also predict asexual prejudice. Gender roles are expectations and standards for behavior and attributes individuals hold based on an observed target’s gender identity (Klocke & Lamberty, 2015). Traditional gender role attitudes align with well­established cultural conceptions of masculinity and femininity (Klocke & Lamberty, 2015). Gay men were construed as less masculine and lesbian women as less feminine than their straight counterparts (Blashill & Powlishta, 2009), which may explain the link between endorsement of traditional gender roles and homophobic beliefs (Klocke & Lamberty, 2015; Weber & Gredig, 2018). Past research has found the same association with asexual individuals (Hoffarth et al., 2016; Vu et al., 2021) when using the Attitudes Towards Asexual (ATA) Scale (Hoffarth et al., 2016), which may result from asexual individuals being expected not to produce children and so not expected to have a family. This behavior counters traditionally gendered expectations of men providing for a family and women caring for their children (Klocke & Lamberty, 2015). By this reasoning, the correlation between singlism and anti­asexual bias (Hoffarth et al., 2016; Thorpe & Arbeau, 2020; Vu et al., 2021) provides further evidence supporting the association between traditional gender role beliefs and anti­asexual bias, though not all research supports the association between this form of bias and singlism (MacInnis & Hodson, 2012). These potential perceptions of incongruity with gender roles may result in an association between a preference for gender role congruency and negative views towards asexual individuals in multiple domains of anti­asexual bias, such as acephobia, social distance from asexual individuals, and negative feelings towards asexual individuals.

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Another crucial predictor of asexual prejudice may be moral disengagement, which is describes as the willingness of an individual to set aside their own moral convictions based on circumstances and context (Bandura, 1991). Immoral actions may be justified through moral disengagement, in which an action one would otherwise view as negative is rationalized (Bandura et al., 1996). Techniques include sharing the blame with others, minimizing the consequences, dehumanizing the victim, or placing the blame on others, such as the victim (Bandura et al., 1996). Moral disengagement is associated with decreased prosocial activity and increased hostility (Bandura et al., 1996). In this way, a greater tendency to morally disengage may predict various harmful attitudes, such as homophobia, as found in past research (González­Fuentes et al., 2022). González­Fuentes and colleagues (2022) used a measure of attitudes, meaning that moral disengagement predicts not necessarily a willingness to act on homophobic ideation but the homophobic ideation itself. This finding indicates that moral disengagement may reflect other bigoted attitudes, particularly other heterosexist views. In this way, moral disengagement likely acts as a predictor of asexual prejudice as well, including domains of acephobia, social distance from asexual individuals, and negative feelings towards asexual individuals.

Prejudice From Within the LGBTQ+ Community

Past research has extensively documented homophobia’s prevalence and associations with social and personal factors in general society (Barnett et al., 2018; Ciocca et al., 2017; González ­ Fuentes et al., 2022; Weber & Gredig, 2018). Bias has been demonstrated to have pervaded even deeper, however, as recent studies have also explored perceptions of inequities and intolerance within the LGBTQ+ community (Ghabrial, 2019; McCormick & Barthelemy, 2020; Parmenter et al., 2020a; Parmenter & Galliher, 2022), or the group including individuals identifying as “lesbian, gay, bisexual, transgender, Queer/questioning, [or] various other Queer identities” (Lexicon Library.LGBT, 2020). This intolerance includes prejudice based on other identity categories, such as racism and sexism (Ghabrial, 2019; McCormick & Barthelemy, 2020), as well as towards different sexual orientation and gender identity groups (Ghabrial, 2019; McCormick & Barthelemy, 2020; Parmenter et al., 2020a; Parmenter & Galliher, 2022). LGBTQ+ in ­ group exclusion often manifests as gatekeeping, amplifying only select voices from within the community (Parmenter et al., 2020a), invalidating different identities (McCormick & Barthelemy, 2020), and failing to consider the issues faced by different LGBTQ+ subgroups (Ghabrial, 2019). These discriminatory

behaviors support majority identity groups, such as White cisgender men, by elevating “gay” as a majority identity within the marginalized community (Parmenter et al., 2020a). These findings suggest that LGBTQ+ individuals from non ­ majority ethnic, gender, and sexual identity groups, such as asexual individuals, may be susceptible to prejudice from within the LGBTQ+ community itself.

The potential vulnerability of asexual individuals is particularly concerning as past work indicates that engagement with the LGBTQ+ community has positively impacted the lives of sexual and gender minority individuals (Elmer et al., 2022; McConnell et al., 2018; Parmenter et al., 2020b; Salfas et al., 2019). Elmer and colleagues (2022) surveyed 7,856 18–88­year­old sexual minority participants from 85 countries to assess relationships between constructs such as loneliness, community involvement, and marginalization. This research found that greater support from LGBTQ+ groups may be associated with decreases in the negative impacts of marginalization, such as loneliness and proximal stress (Elmer et al., 2022). Connection to the sexual and gender minority community mitigated the adverse effects of LGBTQ+ stigma on stress (McConnell et al., 2018), which was associated with decreased anxiety and depressive symptoms (Salfas et al., 2019), and assisted in the development of personal identity (Parmenter et al., 2020b). The literature suggests that support within the LGBTQ+ community may be one buffer for sexually minoritized individuals against the impacts of marginalization, demonstrating the value in researching what may prevent some groups within this community from gaining these benefits.

The victims of LGBTQ+ in ­ group intolerance based on sexual orientation have frequently been nonmonosexual individuals (Ghabrial, 2019; Parmenter et al., 2020a; Parmenter & Galliher, 2022), or those not attracted to a singular gender identity (Ross et al., 2010), such as asexual and bisexual individuals (Parmenter & Galliher, 2022). Non­monosexual individuals were more likely than gay individuals to report perceptions of inequality and decreased access to LGBTQ+ community support (Parmenter & Galliher, 2022). These results are supported by qualitative reports that reveal bigotry towards non ­ monosexual people as a whole, more specific beliefs, such as that non­monosexual identities are insufficiently gay (Ghabrial, 2019), or stereotypes, including that bisexual individuals are more indecisive, more sexual, and more inclined to prefer open relationships (Burke & LaFrance, 2016). These findings indicate inequities within the LGBTQ+ community (McCormick & Barthelemy, 2020), emphasizing the presence of both rejection of and prejudice towards individuals based

on sexual identity (Parmenter et al., 2020a; Parmenter & Galliher, 2022) and demonstrating the need for research which focuses on specific marginalized sexual identity groups, such as asexual people.

Some insight into the experiences of asexual individuals has been uncovered both by interviews with members of the LGBTQ+ community and research related to bisexual individuals. Qualitative research found that in sexual and gender minority circles, asexuality is considered to violate group norms due to the belief that asexual individuals may have majority identities, such as being cisgender and hetero­romantic; therefore, they should not be considered part of the community (Parmenter et al., 2020a). In addition to asexuality­specific research, prejudice towards other non­monosexual identities, such as bisexuality, may also harm asexual individuals. Perceptions of bisexual individuals as indecisive or bisexuality as an unstable identity (Burke & LaFrance, 2016) may similarly be applied to asexual individuals. Biphobia as well may be more generally applied to orientations that violate the norm of physical attraction to one gender. Interviews with bisexual individuals who engaged in self­harm suggest the different aspects of biphobia (e.g., rejection, erasure, the idea that their identity is innately negative) contributed to their self­harm (Dunlop et al., 2022), demonstrating the potential negative consequences for asexual individuals. This past research provides insight into the potential experiences of asexual people, but all conclusions necessarily remain speculative.

Past work into prejudice within the sexual minority community leaves the experiences of asexual individuals largely overlooked. Although work by Parmenter and Galliher (2022) documented wide­ranging perceptions of resources and beliefs about equality within the LGBTQ+ community, past research in this domain has yet to include asexual individuals as participants. This limitation does not compromise the integrity or value of past research in providing insight into the experiences of sexual minority individuals. However, the lack of specific research into asexual individuals and the prejudice they face leaves unclear to what extent individuals of other sexual identities would endorse different domains of bias towards asexual individuals.

Sexual orientation may predict different forms of asexual prejudice, including acephobia, social distance from asexual individuals, and negative feelings towards asexual individuals. Research into intergroup action suggests that sharing a salient identity related to an obstacle allows groups to ally with one another (Dixon et al., 2020). Bisexual and asexual individuals both hold nonmonosexual identities and so, theoretically, may both be victimized through monosexism, or the belief that only

single­gender sexual orientations (e.g., heterosexuality, gay) are legitimate (Ross et al., 2010). As such, bisexual and asexual people may form a coalition based on shared monosexism­based prejudice, which gay and straight individuals would not experience. Gay individuals, as members of a sexual minority group, share a different coalition with bisexual and asexual individuals, of which straight individuals would not be part.

In past research, gay individuals did not appraise bisexual individuals more negatively than gay individuals, but straight individuals appraised heterosexuality most highly, followed by same sex attraction, and bisexuality was appraised most negatively (Burke & LaFrance, 2016). These findings are potentially due to the coalition shared between sexual minority groups, of which straight individuals are not part, lending credence to the applicability of coalition­building theory to the endorsement of asexual prejudice. Further, straight individuals have been observed to appraise asexual individuals as worse than straight, gay, or bisexual individuals (MacInnis & Hodson, 2012), suggesting that layers of difference are meaningful in appraisals of out­group members and that the same may be true for in­group members. When sexually diverse individuals are taken as a whole, they demonstrate lower anti­asexual bias than straight individuals (Bittle & Anderson, 2023; Thorpe & Arbeau, 2020; Vu et al., 2021); however, this finding calls for corroboration as well as greater study of both the distinct sexual minority groups as well as the different facets of anti­asexual bias. Altogether, past research provides a theoretical explanation for asexual prejudice being most commonly endorsed by straight individuals, followed by gay individuals, with asexual prejudice being endorsed least commonly by bisexual individuals.

Hypotheses

The present work sought to investigate the presence and variance of asexual prejudice. Past research has demonstrated that asexual individuals experience prejudice (Iraklis, 2023; McInroy et al., 2022; Vu et al., 2021), though the sources of prejudice remain unclear. Coalition­building theory suggests that there may be allyship between asexual, bisexual, and gay individuals that does not include straight individuals and between asexual and bisexual individuals that does not include gay individuals (Dixon et al., 2020). As such, we hypothesized that greater acephobia, greater social distance from asexual individuals, and more negative feelings towards asexual individuals would be endorsed by straight individuals than gay or bisexual individuals and by gay individuals than bisexual individuals. In addition, predictors of homophobia, such as SDO (Weber & Gredig, 2018), belief in traditional gender

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roles (Weber & Gredig, 2018), and moral disengagement (González­Fuentes et al., 2022), may predict domains of asexual prejudice. Specifically, we expected that higher endorsement of SDO, traditional gender roles, and greater moral disengagement would be associated with greater acephobia, social distance from asexual individuals, and more negative feelings towards asexual individuals.

Method

Participants

IRB approval (SMCM IRB SP23_10) was obtained before data collection. This work was preregistered. The preregistration, materials, and data are available at https://osf.io/pem62. We sampled 300 paid ($1.67) users of the web service Prolific who were eighteen or older and based in the United States or the United Kingdom. We aimed to recruit 100 straight individuals, 100 gay individuals, and 100 bisexual individuals, but ultimately recruited 104 straight participants, 98 gay participants, and 97 bisexual participants. The final sample included 299 participants, as one participant was excluded for reporting their sexual orientation as asexual. Individual scores on measures were also excluded when they were over three standard deviations above the mean, which are described when appropriate. This sample was predominantly cisgender men (40.8%) or cisgender women (49.2%), with smaller proportions of the sample being transgender men (0.7%), transgender women (0.7%), nonbinary or gender non­conforming individuals (3.7%), agender individuals (0.3%), or self ­ identifying individuals (4.7%). This sample was also predominantly non­Hispanic (94.6%), with a smaller proportion indicating Hispanic ethnicity (5%). This sample was mostly White (85.95%), with smaller proportions of the sample being Black (5.35%), Asian (6.35%), Hawaiian or Pacific Islander (0.33%), Middle Eastern or North African (0.67%), or of another race (3.01%). Participants also reported their age in years (M = 35.22, SD = 12.06). One participant’s age was excluded due to the submission of an impossible value.

Measures

Sexual Orientation

Participant reported their sexual orientation via a question in the demographics questionnaire. All participants were asked “What is your sexual orientation?” This question had answer options of “Heterosexual,” “Homosexual,” “Bisexual,” “Asexual,” and “Other,” which included a free response. Participants who did not select “Heterosexual,” “Homosexual,” or “Bisexual” were excluded from analyses. Though participants self­identified as “heterosexual,” “homosexual,” or “bisexual” in the measures of this

study, we describe them as straight, gay, or bisexual in the subsequent results based on Section 5.8 APA recommendations for bias­free language (American Psychological Association, 2020).

Acephobia

The 16­item Attitudes Towards Asexuals Scale (ATA; Hoffarth et al., 2016) measured participant acephobia. Items were scored on a scale from 1 (strongly disagree) to 9 (strongly agree), such that higher scores indicated greater acephobia (Hoffarth et al., 2016). Items include “asexual women are not real women” and “asexuality simply represents an immature, childlike approach to life” (Hoffarth et al., 2016). Three items were reversescored, and all items were averaged to form a composite score. This scale included no subscales. This measure had satisfactory reliability (α = .91) for this sample and in its original implementation (α = .94; Hoffarth et al., 2016). The original implementation also demonstrated evidence of validity (Hoffarth et al., 2016). Four participants were excluded from this measure as outliers for scoring over three standard deviations above the mean.

We also recorded participants’ feelings about asexual individuals through a feelings thermometer. Participants indicated how they felt about asexual individuals on a scale from 0 (very cold or unfavorable feelings), to 100 (very warm or favorable feelings; Alwin, 1997). This widely used attitudes measure was reliable within online surveys in past work (e.g., Chang & Krosnick, 2009) and was both reliable and had evidence of validity within research about the LGBTQ+ community (e.g., Herek & Norton, 2013). One participant was excluded from this measure as an outlier for scoring over three standard deviations below the mean. Participants also completed three other identical feeling thermometers on heterosexual, gay, and bisexual individuals as distractor questions intended to divert participants’ attention from asexual individuals as the target population of interest.

Social Distance

The Social Distance iScore Scale (Mather et al., 2017) includes seven items scored on a 1 (strongly disagree) to 5 (strongly agree) scale. This scale was used to measure the preferred social distance from asexual individuals. Lower scores indicated a higher preference for social distancing from asexual individuals. Items include “I would be willing to accept an asexual individual as a close relative by marriage” and “I would not exclude an asexual individual from my country” (Mather et al., 2017). This scale is scored by multiplying each item value by its item number (e.g., such that a response of three on item four would result in a total item score of twelve) and then summing every total item score. This

measure included no reverse scoring and no subscales. This scale demonstrated adequate reliability in past work (α = .92; Calabrese & Bell, 2019) and the present sample (α = .90). This measure has been linked to prejudice in past work (e.g., Shi et al., 2024). Seven participants were excluded from this measure as outliers for scoring over three standard deviations below the mean.

Social Dominance Orientation

Participants responded to the 8 ­ item SDO7(s), the Social Dominance Orientation Scale, with higher scores indicating higher SDO (Ho et al., 2015). Items were scored on a scale ranging from 1 (strongly oppose) to 7 (strongly favor; Ho et al., 2015). Items include “some groups of people are simply inferior to other groups” and “it is unjust to try to make groups equal” (Ho et al., 2015). Scores were averaged after reverse­coding four items to form a composite score. The scale had sufficient reliability (α = .87) for this sample, as well as high levels of reliability (αs ≥ .78) and evidence of validity in its original implementation (Ho et al., 2015). Three participants were excluded from this measure as outliers for scoring over three standard deviations above the mean.

Endorsement of Traditional Gender Roles

The 22­item Traditional­Antitraditional Gender­Role Attitudes Scale (TAGRAS) included 11 items about participant views related to men and 11 identical items about participant views on women (Klocke & Lamberty, 2015). These items were scored on a 5­point scale ranging from ­2 (very bad) to 2 (very good; Klocke & Lamberty, 2015). This scale measures participant views on gender ranging from traditional to antitraditional, in which negative values indicate antitraditional views, positive values indicate traditional views, and scores closer to 0 indicate more egalitarian views. Items are scored by identifying the more traditional gender to align with each of the 11 tasks illustrated in the 11 items of the survey and subtracting the score given to the opposite gender from the score given to the more traditionally congruent gender. One item reads, “pays the bill on a date.” To score this item, a participant’s appraisal of a woman who exhibited this behavior would be subtracted from their appraisal of a man who exhibited this behavior, as this is a traditionally masculine behavior (Klocke & Lamberty, 2015). By scoring the items this way, each item score reflected a preference for more traditional, egalitarian, or antitraditional gender roles, and so could be used to compute a scaled average. The measure included seven traditionally male items and four traditionally female items. This measure included no subscales. This scale demonstrated lower than optimal reliability within this sample (α = .68). However,

the TAGRAS did demonstrate sufficient reliability and evidence of validity in all three studies in its original implementation (αs ≥ .77; Klocke & Lamberty, 2015). Six participants were excluded from this measure as outliers for scoring over three standard deviations above the mean.

Moral Disengagement

Participants reported their moral disengagement through the 8­item Propensity to Morally Disengage scale (PMD; Moore et al., 2012). This scale is scored on a scale of 1 (strongly disagree) to 7 (strongly agree) and higher scores indicate greater moral disengagement (Moore et al., 2012) . Items include “taking personal credit for ideas that were not your own is no big deal” and “it is okay to spread rumors to defend those you care about” (Moore et al., 2012). No subscales or reverse scoring were included in this measure. Items were averaged to construct an overall score for the scale. This measure had sufficient reliability for this sample (α = .82) as well as in all samples in its original implementation (αs ≥ .70; Moore et al., 2012). This measure has been linked with indicators of prejudice in past work (e.g., Maimon et al., 2022). Four participants were excluded from this measure as outliers for scoring over three standard deviations above the mean.

Procedure

Participants on Prolific were directed to one of three identical Qualtrics surveys based on their responses to a Prolific prescreener item about their sexual identity (i.e., they self­identified as gay or lesbian, bisexual, or heterosexual), which were completed digitally. After granting informed consent and confirming their ages were at or above eighteen years, participants were tasked with completing the Attitudes Toward Asexuals Scale (Hoffarth et al., 2016), Social Distance iScore Scale (Mather et al., 2017), SDO7(s) (Ho et al., 2015), TAGRAS (Klocke & Lamberty, 2015), and PMD (Moore et al., 2012). Participants also completed four separate feelings thermometers referring to “gay or lesbian individuals,” “straight individuals,” “bisexual individuals,” and “asexual individuals" (Alwin, 1997). After completing these questionnaires, participants completed a brief demographics questionnaire. Finally, they were debriefed and thanked.

Results

Sexual Orientation

We conducted a series of between­subjects ANOVAs to compare heterosexual, gay, and bisexual participants regarding prejudice toward asexual individuals. We first ran a between­subjects ANOVA to compare levels

Anti-Asexual Bias | Ashenfelter and

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of acephobia, as measured with the Attitudes Towards Asexuals Scale, between straight, gay, and bisexual participants. Acephobia significantly differed between sexual orientation groups, F(2, 292) = 9.66, p < .001, ηp 2 = .06. Heterosexual participants endorsed significantly greater acephobia (M = 2.45, SD = 1.19) than gay (M = 1.98, SD = 1.06), p = .002, d = 0.42, 95% CI [0.14, 0.70], and bisexual participants (M = 1.80, SD = 0.98), p < .001, d = 0.59, 95% CI [0.31, 0.88] (see Figure 1).

Gay and bisexual participants did not significantly differ in terms of acephobia, p = .25. The means, standard deviations, ranges, and correlations for each measure used in any analyses are listed in Table 1.

We conducted a between ­ subjects ANOVA to

Acephobia Endorsed by Heterosexual, Gay, and Bisexual Individuals

compare straight, gay, and bisexual participants regarding their feelings about asexual individuals via the feelings thermometer. Straight ( M = 78.47, SD = 22.03), gay ( M = 74.48, SD = 23.06), and bisexual ( M = 78.48, SD = 21.40) individuals did not significantly differ regarding feelings about asexual individuals, F(2, 295) = 1.07, p = .35, ηp 2 = .01.

We conducted a between­subjects ANOVA to compare straight, gay, and bisexual participants regarding their social distance (Social Distance iScore Scale) from asexual individuals. Social distance significantly differed between sexual orientation groups, F(2, 289) = 5.24, p = .01, η p 2 = .04. Straight participants ( M = 133.16, SD = 11.81) endorsed significantly greater social distance than gay ( M = 137.39, SD = 7.85), p = .003, d = ­0.42, 95% CI [­0.70, ­0.14], and bisexual participants (M = 136.55, SD = 8.93), p = .02, d = ­0.32, 95% CI [­0.60, ­ 0.04] (see Figure 2). Gay and bisexual participants did not significantly differ in social distance, p = .55 (see Table 1).

Individual Difference Measures

Next, we conducted a series of simple Pearson correlations to examine the relationship between individual difference measures and attitudes toward asexual individuals (see Table 1). Higher levels of SDO were significantly associated with greater acephobia, greater social distance from asexual individuals, and more negative attitudes toward asexual individuals. Greater endorsement of traditional gender roles (TAGRAS) was significantly associated with acephobia and social distance from asexual individuals but was not significantly related to feelings towards asexual individuals. Finally, greater moral disengagement was significantly associated with greater acephobia, greater social distance from asexual individuals, and more negative attitudes toward asexual individuals2.

TABLE 1

Descriptive Statistics and Correlations for

2.

3.

4.

5.

6.

Note. ATA = Attitudes Toward Asexuals Scale, SDO7(s) = Social Dominance Orientation, TAGRAS = Traditional Gender Role Attitudes Scale, PMD = Propensity to Morally Disengage. * p < .05. ** p < .001.

Discussion

The present work investigated predictors of asexual prejudice to rectify a striking dearth in the literature. Although past research has focused on gay and bisexual populations, little research has examined asexuality or the experiences of asexual people. A deeper understanding of this specific prejudice may provide insight into the unique struggles faced by this group. In particular, the present work compared straight, gay, and bisexual individuals in terms of endorsement of multiple domains of 2As exploratory analyses, we examined whether individual difference measures moderated the associations between sexual orientation and asexual bias. TAGRAS did not predict feelings toward asexual individuals among gay or lesbian (versus heterosexual) participants and PMD was a weaker predictor of ATA among gay or lesbian (versus heterosexual) participants. There were no other significant interactions. See the supplemental analyses on https://osf.io/pem62/.

FIGURE 1

asexual prejudice, including acephobia, social distance from asexual individuals, and feelings towards asexual individuals, with the prediction that greater asexual prejudice would be endorsed by straight individuals, followed by gay individuals, and followed in turn by bisexual individuals. In addition, SDO, traditional gender role acceptance, and moral disengagement were investigated to determine if they were correlated with the endorsement of different domains of asexual prejudice, with the prediction that each would be associated with greater asexual prejudice.

Sexual Orientation

The hypothesis that straight individuals would endorse more prejudice towards asexual individuals than gay and bisexual individuals was moderately well­supported by the data. In comparison, the hypothesis that gay individuals would endorse more prejudice towards asexual individuals than bisexual individuals was not supported. Straight individuals endorsed significantly greater acephobia and social distance from asexual individuals in comparison to gay and bisexual individuals. However, straight individuals did not differ substantially from either group in terms of feelings towards asexual individuals. Why such conceptually similar constructs would differ quantitatively is unclear, though this could be due to differences in self­awareness between cognitions and affect. Respondents may have better understood their thoughts, as measured with the variables acephobia and social distance, than their feelings, leading to a false discrepancy. Alternately, respondents may have negative beliefs about asexual individuals in the absence of negative feelings. Future research must resolve this uncertainty and determine why sexual orientation would be a factor in levels of acephobia and social distance but not feelings towards asexual individuals. Gay and bisexual individuals did not significantly differ regarding acephobia, social distance from asexual individuals, or feelings towards asexual individuals.

These results provide crucial insight into the application of coalition­building theory in subjects of sexual identity. Compared to gay and bisexual individuals, straight individuals exhibit higher levels of acephobia and social distancing from asexual individuals, aligning with coalition­building theory (Dixon et al., 2020). This theory suggests that because bisexual and gay individuals share more common struggles with asexual individuals than straight individuals do, they are more likely to see themselves as allies and, therefore, express less prejudice toward asexual individuals. These findings also counter aspects of coalition­building theory when considering that no significant differences existed in prejudice towards asexual individuals between gay and bisexual

individuals. Coalition­building theory would suggest that bisexual individuals, who have a greater volume of shared struggles with asexual individuals due to monosexism, would view themselves as greater allies than gay individuals (Dixon et al., 2020), which may indicate decreased endorsement of asexual prejudice compared to gay individuals. This prediction was not reflected in the data, however, which may indicate a limitation to coalition­building theory when considering subgroups that already have a shared struggle–in other words, as bisexual and gay individuals both combat heterosexism, the addition of monosexism as an obstacle for bisexual individuals does not significantly increase feelings of allyship with asexual individuals, who face both forms of prejudice. This finding may also indicate that the presumption that allyship decreases the endorsement of prejudice was faulty and that groups that ally with one another may continue to hold the same appraisals regardless of conditional comradery. Future research should investigate coalition­building theory more fully with regard to subgroups, particularly to determine the degree of preference for allies perceived as more similar.

Individual Difference Measures

The hypothesis that SDO would significantly correlate with prejudice towards asexual individuals was strongly supported by the data, which found significant associations between SDO and all three measures of asexual prejudice. SDO was strongly correlated with acephobia and social distance from asexual individuals and moderately associated with negative feelings towards asexual

FIGURE 2 Social Distance

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individuals. This finding supports past research that found associations between anti­asexual bias and SDO, particularly given the use of common measures, such as the Attitudes Toward Asexuals Scale (Hoffarth et al., 2016) and a feelings thermometer (MacInnis & Hodson, 2012). The findings of the present work support past research that found associations between SDO and other forms of prejudice (Duriez & Van Hiel, 2002; Weber & Gredig, 2018), leading to the overall conclusion that SDO may predict universal prejudice. This work also supports the conclusion that SDO may predict sexual identity prejudice when combined with similar work, which found that SDO was associated with homophobia (Weber & Gredig, 2018). These findings contribute to the collective understanding of SDO and its relationship with prejudice.

The hypothesis that traditional gender role beliefs would be associated with asexual prejudice was moderately supported by the data, as both social distance and acephobia had significant associations, but feelings towards asexual individuals did not. The correlation with acephobia was relatively strong, but the association with social distance was weak. These associations provide moderate support for the overall conclusion that greater endorsement of traditional gender roles is associated with greater prejudice towards sexual minorities (Klocke & Lamberty, 2015; Weber & Gredig, 2018). These results reflect past research on asexual prejudice (Hoffarth et al., 2016; Vu et al., 2021), as endorsement of traditional gender roles was once again demonstrated to be strongly associated with acephobia. These findings leave unclear why acephobia and social distance have significant associations with traditional gender role acceptance but feelings do not, similar to findings related to differences between straight and both gay and bisexual individuals, justifying further exploration into why traditional gender role acceptance has these limited associations. Although this paper’s justification for the hypothesis included the element that asexual individuals may be less expected to form families with children, thereby countering traditionally gendered expectations of childrearing or providing for a family, it is unclear if this is the actual reason that traditional gender role acceptance has these associations. Also unclear is if non­straight individuals engaging in more heteronormative behavior, such as an asexual individual with a different­gender partner, would experience less prejudice, as may be expected if the endorsement of traditional gender roles is a cause of prejudice rather than just an association. In all, these findings demonstrate a crucial connection between prejudice towards asexual individuals and the endorsement of traditional gender roles, though future research must seek to determine the mechanisms behind this association.

The hypothesis that moral disengagement would be associated with asexual prejudice was strongly supported by the data, as all three measures of asexual prejudice had significant associations. Moral disengagement was strongly correlated with acephobia, moderately correlated with social distance, and weakly associated with negative feelings toward asexual individuals. The findings of the present research expand on past research, which identified an association between moral disengagement and homophobic beliefs (Gonzalez et al., 2021), by finding evidence of an association between moral disengagement and another type of sexual identity prejudice–acephobia. This finding supports the overall conclusion that moral disengagement and heterosexism may be associated. The present work also supports the findings of earlier research, which found that moral disengagement predicted homophobic beliefs rather than simply predicting homophobic behavior (Gonzalez et al., 2021), as the Attitudes Toward Asexuals Scale and feelings thermometer measured attitudes rather than behaviors. This paper’s study of moral disengagement has provided greater insight into its associations with different forms of prejudice.

Present and Future Work

The present work presents novel data that may begin rectifying a lack of literature on asexual prejudice and does so in strong and reliable ways. The use of multiple measures of prejudice towards asexual individuals, particularly measures that operationalize distinct domains of prejudice, allows findings to more thoroughly examine what specific attitudes are associated with the variables of interest. Using a large sample also supports the generalizability of findings and increases the statistical power of the results. When considering measures, the Attitudes Toward Asexuals Scale, Social Distance iScore Scale, SDO7(s), and PMD all demonstrated sufficient reliability, supporting conclusions related to constructs of acephobia, social distance from asexual individuals, SDO, and moral disengagement. In all, the present work is a successful investigation of prejudice against asexual individuals.

Despite the strengths, the limitations of the study warrant consideration. The TAGRAS was unreliable, rendering conclusions about traditional gender role endorsement less certain. Past research used the Attitudes Toward Gender Roles Scale (ATGR; Vu et al., 2021), which may be a more apt measure for this subject. Feelings towards asexual individuals were measured with a single item, the feelings thermometer, which may not be as accurate as a measure with a series of items; however, past research used this tool as a measure of anti­asexual bias with some success (Bittle

& Anderson, 2023; Hoffarth et al., 2016; MacInnis & Hodson, 2012; Rye & Goldszmidt, 2023). The sample was predominantly White, non­Hispanic, and young, rendering it not representative of the overall population. A particularly important demographic limitation is the low proportion of transgender participants, as these individuals, as members of the LGBTQ+ community, may endorse systematically different levels of asexual prejudice than cisgender individuals based on coalitionbuilding theory. The limitations of both measures and the sample are worth considering in any conclusions drawn from the present work.

Although this study contributes to the literature on asexuality and asexual prejudice, particular questions remain unanswered. Although the present work has sought to understand associations with attitudes towards asexual individuals, it neglects the perspective of asexual people. Future qualitative work may benefit from investigating what forms asexual prejudice takes, its intensity, and how pervasive it is in the lives of asexual individuals. Other qualitative work may dive deeper into the domains of asexual prejudice to determine what meaningful differences exist between social distance from asexual people, feelings towards asexual people, and acephobia among individuals who are prejudiced against asexual people.

Future research should particularly investigate if using different measures would resolve the discrepancy between feelings towards asexual individuals and the other measures of asexual prejudice. This future research may also measure asexual prejudice behaviorally as opposed to through self­report questionnaires. Indeed, past work indicates that straight individuals prefer speaking to other straight individuals rather than asexual individuals (MacInnis & Hodson, 2012), and discriminatory behaviors such as gatekeeping have been reported from within the LGBTQ+ community (Parmenter et al., 2020a). Still unclear is whether the content or extent of discriminatory behaviors toward asexual people differs by sexual orientation. Additionally, although past work examining prejudice within the LGBTQ+ community did not find any associations between social desirability scales and prejudice toward bisexual individuals (Matsick & Rubin, 2018), it is possible gay and bisexual individuals in this sample may have been more motivated to misrepresent themselves due to perceptions of obligation based on the LGBTQ+ coalition. Future work should include measures of social desirability to mitigate this issue.

Future research should also investigate the extent to which allyship under coalition­building theory reduces feelings of prejudice, particularly when considering differences between groups with different levels of allyship. This research may particularly benefit from

focusing on sexual identity groups, which are perfect examples of subgroups with different levels of theoretical allyship. Notably, although prejudice reduction is a start, allyship involves preventing the perpetuation of bias and counteracting seen bias at the interpersonal and institutional levels (De Souza & Schmader, 2024), further justifying research into why some coalitions form and others do not in the presence of shared obstacles. Overall, it is clear that the present work leads naturally to future research, which may provide deeper insight into the current study’s results and the experiences of asexual people.

Most importantly, the goal of studying prejudice must be to combat it. All future work must keep an eye on learning what may aid efforts to form more inclusive and equitable spaces within and outside the sexual minority community. This research may include studying other potential factors that may influence asexual prejudice, including facets such as how “out” an asexual individual is as well as whether they are in a seemingly straight relationship. This may corroborate past and future research examining these domains when focusing on bisexual individuals. Other factors worth studying include those on the end of the prejudiced individual, such as gender identity and moral foundations, which may help identify what personal attributes and beliefs are the greatest predictors of prejudiced attitudes towards asexual people. Research on this subject may also include more deeply studying the factors considered in the present work, such as why traditional gender role acceptance is associated with asexual prejudice. In summation, the current work provides meaningful insight into the experiences of asexual individuals and the prejudice they face. Future research to better understand this form of prejudice is crucial to ensuring that all marginalized groups may receive the benefits of community involvement and support.

References

Alwin, D. F. (1997). Feeling thermometers versus 7-point scales: Which are better? Sociological Methods & Research, 25(3), 318–340.

https://doi.org/10.1177/0049124197025003003

Allport, G. W. (1954). The nature of prejudice. Addison-Wesley. American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

Bandura, A. (1991). Social cognitive theory of moral thought and action. In W. M. Kurtines & J. L. Gewirtz (Eds.), Handbook of moral behavior and development, Vol. 1. Theory; Vol. 2. Research; Vol. 3. Application (pp. 45–103). Lawrence Erlbaum Associates, Inc.

Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996). Mechanisms of moral disengagement in the exercise of moral agency. Journal of Personality and Social Psychology, 71(2), 364–374. https://doi.org/10.1037/0022-3514.71.2.364

Barnett, M. D., Öz , H. C. M., & Marsden, A. D. (2018). Economic and social political ideology and homophobia: The mediating role of binding and individualizing moral foundations. Archives of Sexual Behavior, 47(4), 1183–1194. https://doi.org/10.1007/s10508­017­0989­2

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Anti-Asexual Bias | Ashenfelter and Kristina Howansky

Bittle, A., & Anderson, V. N. (2023). As human as anybody else: Attitudes toward asexual people. Psychology of Sexual Orientation and Gender Diversity. Advance online publication. https://doi.org/10.1037/sgd0000652

Blashill, A. J., & Powlishta, K. K. (2009). Gay stereotypes: The use of sexual orientation as a cue for gender-related attributes. Sex Roles: A Journal of Research, 61(11–12), 783–793. https://doi.org/10.1007/s11199-009-9684-7

Borgogna, N. C., McDermott, R. C., Aita, S. L., & Kridel, M. M. (2019). Anxiety and depression across gender and sexual minorities: Implications for transgender, gender nonconforming, pansexual, demisexual, asexual, queer, and questioning individuals. Psychology of Sexual Orientation and Gender Diversity, 6(1), 54–63. https://doi.org/10.1037/sgd0000306

Burke, S. E., & LaFrance, M. (2016). Lay conceptions of sexual minority groups. Archives of Sexual Behavior, 45(3), 635–650. https://doi.org/10.1007/s10508-015-0655-5

Calabrese, C., & Bell, R. A. (2019). Opposition to nonprescription naloxone access: Measurement and psychosocial predictors. Substance Use & Misuse, 54(11), 1853–1861. https://doi.org/10.1080/10826084.2019.1618331

Chang, L., & Krosnick, J. A. (2009). National surveys via Rdd telephone interviewing versus internet comparing sample representativeness and response quality. The Public Opinion Quarterly, 73(4), 641–678. https://dx.doi.org/10.1093/poq/nfp075

Ciocca, G., Niolu, C., Déttore, D., Antonelli, P., Conte, S., Tuziak, B., Limoncin, E., Mollaioli, D., Carosa, E., Gravina, G. L., Di Sante, S., Di Lorenzo, G., Fisher, A. D., Maggi, M., Lenzi, A., Siracusano, A., & Jannini, E. A. (2017). Crosscultural and socio-demographic correlates of homophobic attitude among university students in three European countries. Journal of Endocrinological Investigation, 40(1), 227–233. https://doi.org/10.1007/s40618-016-0554-1

Cohrs, C., & Asbrock, F. (2009). Right-wing authoritarianism, social dominance orientation and prejudice against threatening and competitive ethnic groups. European Journal of Social Psychology, 39(2), 270–289. https://doi.org/10.1002/ejsp.545

De Souza, L., & Schmader, T. (2024). When people do allyship: A typology of allyship action. Personality and Social Psychology Review. Advance online publication. https://doi.org/10.1177/10888683241232732

Dixon, J., Elcheroth, G., Kerr, P., Drury, J., Al Bzour, M., Subašić, E., Durrheim, K., & Green, E. G. T. (2020). It’s not just “us” versus “them”: Moving beyond binary perspectives on intergroup processes. European Review of Social Psychology, 31(1), 40–75. https://doi.org/10.1080/10463283.2020.1738767

Dunlop, B. J., Hunter, C., Shafti, M., Coleman, S. E., Hartley, S., & Taylor, P. J. (2022). ‘Why is it so different now I’m bisexual?’: Young bisexual people’s experiences of identity, belonging, self-injury, and COVID19. Psychology & Sexuality, 13(3), 756–773. https://doi.org/10.1080/19419899.2021.1924241

Duriez, B., & Van Hiel, A. (2002). The march of modern fascism. A comparison of social dominance orientation and authoritarianism. Personality and Individual Differences, 32(7), 1199–1213. https://doi.org/10.1016/S0191-8869(01)00086-1

Elmer, E. M., van Tilburg, T., & Fokkema, T. (2022). Minority stress and loneliness in a global sample of sexual minority adults: The roles of social anxiety, social inhibition, and community involvement. Archives of Sexual Behavior, 51(4), 2269–2298. https://doi.org/10.1007/s10508-021-02132-3

Ghabrial, M. A. (2019). “We can shapeshift and build bridges”: Bisexual women and gender diverse people of color on invisibility and embracing the borderlands. Journal of Bisexuality, 19(2), 169–197. https://doi.org/10.1080/15299716.2019.1617526

Gonzalez, K. A., Flanders, C. E., Pulice-Farrow, L., & Bartnik, A. (2021). “It’s almost like bis, pans kind of stick together:” Bi + belonging and community connection. Journal of Bisexuality, 21(2), 194–224. https://doi.org/10.1080/15299716.2021.1927282

González-Fuentes, J. A., Moreno-Manso, J. M., Guerrero-Molina, M., GuerreroBarona, E., & García-Baamonde, M. E. (2022). Moral disengagement mechanisms and personality dimensions implicit to homophobia. International Journal of Environmental Research and Public Health, 19(14), Article 14. https://doi.org/10.3390/ijerph19148583

Herek, G., & Norton, A. T. (2013). Heterosexuals’ attitudes toward transgender people: Findings from a national probability sample of U.S. adults. Sex Roles, 68(11–12), 738–753. https://doi.org/10.1007/s11199-011-0110-6

Ho, A. K., Sidanius, J., Kteily, N., Sheehy-Skeffington, J., Pratto, F., Henkel, K. E., Foels, R., & Stewart, A. L. (2015). The nature of social dominance orientation: Theorizing and measuring preferences for intergroup inequality using the new SDO₇ scale. Journal of Personality and Social Psychology, 109, 1003–1028. https://doi.org/10.1037/pspi0000033

Hoffarth, M. R., Drolet, C. E., Hodson, G., & Hafer, C. L. (2016). Development and validation of the Attitudes Towards Asexuals (ATA) scale. Psychology & Sexuality, 7(2), 88–100. https://doi.org/10.1080/19419899.2015.1050446

Iraklis, G. (2023). Examining the social tabooisation of asexuality: The underpinnings of anti-asexual bias. Sexuality Research and Social Policy https://doi.org/10.1007/s13178-023-00884-2

Klocke, U., & Lamberty, P. (2015). The Traditional-Antitraditional Gender Role Attitudes Scale (TAGRAS): Development and validation. 44. Lexicon Library.LGBT. (n.d.). Acephobia. In Lexicon Library.LGBT. Retrieved September 13, 2024 from https://lexicon.library.lgbt/definitions/acephobia/ Lexicon Library.LGBT. (n.d.). Asexual. In Lexicon Library.LGBT. Retrieved on September 12, 2024, from https://lexicon.library.lgbt/definitions/asexual/ Lexicon Library.LGBT. (n.d.). Biphobia. In Lexicon Library.LGBT. Retrieved September 13, 2024 from https://lexicon.library.lgbt/definitions/biphobia/ Lexicon Library.LGBT. (n.d.). Bisexual. In Lexicon Library.LGBT. Retrieved September 13, 2024 from https://lexicon.library.lgbt/definitions/bisexual/ Lexicon Library.LGBT. (n.d.). LGBTQ+. In Lexicon Library.LGBT. Retrieved September 13, 2024 from https://lexicon.library.lgbt/definitions/lgbtq/ MacInnis, C. C., & Hodson, G. (2012). Intergroup bias toward “Group X”: Evidence of prejudice, dehumanization, avoidance, and discrimination against asexuals. Group Processes & Intergroup Relations, 15(6), 725–743. https://doi.org/10.1177/1368430212442419

Maimon, M., Sanchez, D. T., Rodriguez, S., & Albuja, A. (2022). Personality traits as identity threat cues: Stigmatized perceivers infer prejudice from disagreeableness. Journal of Personality, 92(2), 620–635. https://doi.org/10.1111/jopy.12855

Mather, D. M., Jones, S. W., & Moats, S. (2017). Improving upon Bogardus: Creating a more sensitive and dynamic Social Distance Scale. Survey Practice, 10(4), Article 4. https://doi.org/10.29115/sp-2017-0026

Matsick, J. L., Rubin, J. D. (2018). Prejudice among lesbian and gay people, Examining the roles of gender and perceived sexual orientation. Psychology of Sexual Orientation and Gender Diversity, 5(2), 143–155. https://dx.doi.org/10.1037/sgd0000283

McConnell, E. A., Janulis, P., Phillips II, G., Truong, R., & Birkett, M. (2018). Multiple minority stress and LGBT community resilience among sexual minority men. Psychology of Sexual Orientation and Gender Diversity, 5, 1–12. https://doi.org/10.1037/sgd0000265

McCormick, M., & Barthelemy, R. S. (2020). Excluded from “inclusive” communities: LGBTQ youths’ perception of “their” community. Journal of Gay & Lesbian Social Services, 33(1), 103–122. https://doi.org/10.1080/10538720.2020.1850386

McInroy, L. B., Beaujolais, B., Leung, V. W. Y., Craig, S. L., Eaton, A. D., & Austin, A. (2022). Comparing asexual and non-asexual sexual minority adolescents and young adults: Stressors, suicidality and mental and behavioural health risk outcomes. Psychology & Sexuality, 13(2), 387–403. https://doi.org/10.1080/19419899.2020.1806103

Moore, C., Detert, J. R., Klebe Treviño, L., Baker, V. L., & Mayer, D. M. (2012). Why employees do bad things: Moral disengagement and unethical organizational behavior. Personnel Psychology, 65(1), 1–48. https://doi.org/10.1111/j.1744-6570.2011.01237.x

Parmenter, J. G., & Galliher, R. V. (2022). Experiences of community resilience and inequity among LGBTQ+ people: A person-centered analysis [SPECIAL BC PREPRINT, CONSULT 10.8]. The Counseling Psychologist, 00110000221124274. https://doi.org/10.1177/00110000221124274

Parmenter, J. G., Galliher, R. V., & Maughan, A. D. A. (2020a). LGBTQ+ emerging adults perceptions of discrimination and exclusion within the LGBTQ+ community. Psychology & Sexuality, 12(4), 289–304. https://doi.org/10.1080/19419899.2020.1716056

Parmenter, J. G., Galliher, R. V., & Maughan, A. D. A. (2020b). An exploration of LGBTQ+ community members’ positive perceptions of LGBTQ+ culture. The Counseling Psychologist, 48(7), 1016–1047. https://doi.org/10.1177/0011000020933188

Pratto, F., Sidanius, J., Stallworth, L. M., & Malle, B. F. (1994). Social dominance orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67(4), 741–763. https://doi.org/10.1037/0022-3514.67.4.741

Ross, L. E., Dobinson, C., & Eady, A. (2010). Perceived determinants of mental health for bisexual people: A qualitative examination. American Journal of Public Health, 100(3), 496–502. https://doi.org/10.2105/AJPH.2008.156307

Rye, B. J., & Goldszmidt, R. (2023). Do attitude functions and perceiver demographics predict attitudes towards asexuality? Psychology & Sexuality, 14(3), 572–592. https://doi.org/10.1080/19419899.2023.2185534

Salfas, B., Rendina, H. J., & Parsons, J. T. (2019). What is the role of the community? Examining minority stress processes among gay and bisexual men. Stigma and Health, 4(3), 300–309. https://doi.org/10.1037/sah0000143

Shi, E., Platow, M. J., Bar-Tal, D., Augoustinos, M., Spears, R., Van Rooy, D. (2024).

Pandemic and prejudice: Revisiting Bogardus’s social distance concept in a time of COVID-19. Group Processes and Intergroup Relations, 27(2), 239–255. https://doi.org/10.1177/13684302221133715

Thorpe, C., & Arbeau, K. (2020). Judging an absence: Factors influencing attitudes towards asexuality. The Canadian Journal of Human Sexuality, 29(3), 307–313. https://doi.org/10.3138/cjhs.2020-0003

Vu, K., Riggs, D. W., & Due, C. (2021). Exploring anti-asexual bias in a sample of Australian undergraduate psychology students. Psychology & Sexuality, 13(4), 984–995. https://doi.org/10.1080/19419899.2021.1956574

Weber, P., & Gredig, D. (2018). Prevalence and predictors of homophobic behavior among high school students in Switzerland. Journal of Gay & Lesbian Social Services, 30(2), 128–153. https://doi.org/10.1080/10538720.2018.1440683

Author Note

Nicholas A. Ashenfelter https://orcid.org/0009­0006­3901­9501

Kristina Howansky https://orcid.org/0000­0003­1704­9436 Preregistration, materials, and data for this work are available at https://osf.io/vpqc9, https://osf.io/w7h28, and https://osf.io/zw3au, respectively. We have no known conflicts of interest to disclose. Correspondence concerning this article should be addressed to Kristina Howansky, 47645 College Drive, St. Mary’s City, MD 20686. Email: khowansky@smcm.edu

Ashenfelter

Attitudes of the Public Toward the Criminal Justice System and Offenders

Mackenzie L. Creighton and William A. Jellison* Department of Psychology, Quinnipiac University

ABSTRACT. The attitudes of the public can have a great impact on the success of individuals in the criminal justice system, but much of the literature is lacking when it comes to representing these attitudes. We used self­report questionnaires to examine factors (e.g., political ideologies, opinions on social dominance, attitudes toward individuals of another race, experience with the justice system, safety concerns) that relate to the general public’s attitudes toward individuals with criminal records and the justice system. Multiple regression results demonstrated that social dominance (Study 1: B = ­.29, SE = .07, 95% CI [­.42, ­.15], p < .001; Study 2: B = ­.31, SE = .04, 95% CI [­.39, ­.23], p < .001) and experience with the justice system (Study 1: B = ­.33, SE = .13, 95% CI [­.57, ­.08], p = .010; Study 2: B = ­.39, SE = .08, 95% CI [­.55, ­.22], p < .001) uniquely predicted attitudes toward individuals involved with the system. Furthermore, political views (Study 1: B = ­.14, SE = .06, 95% CI [­.27, ­.015], p = .033; Study 2: B = ­.15, SE = .03, 95% CI [ ­ .20, ­ .10], p < .001) and safety concerns (Study 1: B = .76, SE = .25, 95% CI [.28, 1.24], p = .002; Study 2: B = .31, SE = .14, 95% CI [.04, .59], p = .026) uniquely predicted attitudes toward the effectiveness of the system as a whole. Furthermore, some demographic differences were found. Implications for public policy are discussed.

Keywords: mass incarceration, public attitudes, offenders, criminal justice system, public policy

The label of “criminal” was given to approximately 5.4 million individuals in the United States in 2022 alone (Buehler & Kluckow, 2024). Once someone is labeled as such, they are dehumanized by society and punished long after their sentence has been completed, often for the rest of their lives (Alexander, 2010). There is a stigma that surrounds individuals with a criminal record, affecting their civil liberties and chances of obtaining things such as employment, housing, higher education, public assistance, business licenses, life insurance, and voter rights (Li, 2018; Wakefield & Uggen, 2010). In the United States at the end of 2022, over 1.8 million individuals were incarcerated and over 3.6 million were on probation or parole (Buehler & Kluckow, 2024). This translates to 1 in 48 U.S. adults being under some form of correctional supervision (i.e., prison, jail, probation, parole; Buehler & Kluckow, 2024). Additionally, it is estimated that the United States holds 5% of the global population, but about 20% of the world’s prisoners (Wagner & Bertram, 2020). With this many individuals caught up in the criminal justice

system, it is important to know how the general public views these individuals and the system overall. The current study sought to explore factors that relate to attitudes toward individuals with criminal records and the effectiveness of the criminal justice system itself. When it comes to representing these attitudes, much of the literature is lacking. Many research studies focus solely on the attitudes of professionals working within the system, such as police officers or recruits (e.g., Carlson et al., 1971; Cunha et al., 2021), correctional officers (e.g., Shannon & Page, 2014), or defense attorneys (e.g., Avery et al., 2018). Studies that do focus on the attitudes of the public also seem to exclusively use university students as participants, especially ones who may be planning to pursue careers related to the criminal justice system, such as students studying social work (e.g., Weaver et al., 2019), criminal justice (e.g., Weaver et al., 2019), or law students (e.g., Na & Loftus, 1998). Additionally, the few articles that do assess the general public’s attitudes and do not utilize university students tend to focus on individuals within the system

who have extenuating circumstances, such as psychological or physiological health issues, compared to individuals who may not struggle with these issues (e.g., Avery et al., 2018; Weaver et al., 2019). Furthermore, there seems to be a focus in the literature on attitudes toward punishments, as opposed to attitudes toward the individuals themselves (e.g., Baumer et al., 2003; Gerber & Jackson, 2016). There is also a focus on attitudes toward sex offenders (e.g., de Vel­Palumbo et al., 2019; Rosselli & Jeglic, 2017), as well as violent offenders (e.g., Atkin­Plunk, 2020; Grossi, 2017), specifically because they are seen as the most stigmatized groups of individuals with criminal records. The current study sought to investigate factors that may predict attitudes more representative of the general population in the United States toward individuals in the system.

Opinions of professionals in the system are important to investigate for implications on how individuals will be treated during their involvement with the system, but the attitudes of the public can have a huge influence on a person’s likelihood of succeeding outside of the criminal justice system, especially regarding individuals released from prison (Rade et al., 2016). Those with criminal records are often demonized and viewed by the public as dangerous, dishonest, and disreputable (Hirschfield & Piquero, 2010). The discrimination and stigmatization of these individuals also results in them being excluded from various activities (e.g., social, economic, political) when they return to the community (Li, 2018; Wakefield & Uggen, 2010). This exclusion feeds into the discrimination against those who have been involved with the criminal justice system, and there is evidence to show that of these individuals, those who have experienced discrimination have greater reconviction rates than those who have not (Boag & Wilson, 2014). Misconceptions and false beliefs create barriers, which can be detrimental to the lives of these individuals and lead to higher rates of recidivism.

However, positive aspects of the social environment (e.g., neighborhoods) also influence attitudes toward those who have been labeled as criminal offenders. For example, when crime is viewed as a local problem with criminal offenders being otherwise “good” kids getting into trouble, members of the neighborhood are more likely to believe in the redeemability of those who commit crimes (Leverentz, 2011). Furthermore, Moak and colleagues (2020) were able to show that increasing empathy and understanding can help decrease negative views of individuals in the system, which can help them succeed when they reenter the community. This study utilized simulations to humanize perspectives and help develop a better understanding of former offenders. An increased understanding is what aids in increasing empathy, reducing discrimination and stigma, and improving the chances of successful reintegration for individuals

trying to move on from their involvement. Furthermore, it has been shown that people who have engaged with these individuals in an actual prison environment have increased empathy and decreased prejudice toward this population, even with perpetrators of serious offenses such as sexual assault and murder (Boag & Wilson, 2014). Thus, the opinions of everyday people are important as they have implications on how these individuals may be treated after their involvement with the criminal justice system, as well as influencing recidivism rates and the likelihood of success for people with criminal records.

Therefore, we wish to better understand the actual, current attitudes held by the general public toward individuals involved in all areas of the system (e.g., incarcerated individuals, those on probation or parole, people who have been arrested but not convicted), attitudes toward the criminal justice system itself, and what factors may influence those attitudes. There is evidence to show that individuals who are more politically liberal tend to have more positive attitudes toward individuals involved with the system (Hirschfield & Piquero, 2010), but being authoritarian or conservative can feed into negative attitudes toward these individuals and can be connected to increased support for harsher punishments (Na & Loftus, 1998; Rade et al., 2016). Viewing individuals with criminal records as competition (e.g., for resources, power), or of lower social status (e.g., economically, educationally; Côté­Lussier, 2016), as well as believing more in traditional values (Gerber & Jackson, 2016), can also be connected to negative attitudes and increased support for harsh punishments. Although, as mentioned previously, other research has shown that having experience with or exposure to this population can help combat negative stereotypes and reduce negative attitudes (Hirschfield & Piquero, 2010; Kerr et al., 2018). Fearing victimization can be connected to negative attitudes and the support of harsher punishments as well (Gerber & Jackson, 2016).

A factor that may also relate to attitudes toward people with criminal records are attitudes toward the effectiveness of the criminal justice system itself. However, like research on general attitudes toward the individuals, much of the literature centering around public confidence in the criminal justice system, or the effectiveness and fairness of the criminal justice system, is lacking. What literature exists is either not relevant to the current study (e.g., Smith, 2005), does not consider how the efficiency and credibility of the criminal justice system relates to the attitudes toward individuals within the system (e.g., Dandurand, 2014), or dates back to the 1970s (e.g., Ashworth & Feldman­Summers, 1978; Cook, 1979). Dandurand (2014) also argues there is a tension between at least two different viewpoints that

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make defining effectiveness difficult (peace and public safety vs. legal rights and following the rules of society). Therefore, how people interpret the effectiveness of the criminal justice system may vary, so we felt it was important to use terms that could be broadly interpreted by participants. For example, for some people, effectiveness could mean preventing crime, but for others, it could mean how well the system reforms the individuals it controls. Therefore, the present study also wanted to evaluate the public’s attitudes toward the effectiveness of the criminal justice system based on each individual’s interpretation of effectiveness.

Across two studies, we investigated the attitudes of the general public toward individuals involved with the criminal justice system and the criminal justice system as a whole. Additionally, we aimed to investigate what factors may contribute to those attitudes. Based on the literature, the factors we chose to focus on include demographics, political ideologies, opinions on social dominance, attitudes toward racial outgroups, experience with the criminal justice system, and prior victimization or safety concerns.

Study 1

Method

Participants and Procedure

Before beginning data collection, all materials were approved by Quinnipiac University’s Institutional Review Board. Study 1 included 68 men, 109 women, and 3 unreported ( M age = 58.04, SD = 18.15, range 18–88). Race/ethnicity consisted of 84.39% White, 7.2% Black/African American, 3.9% Asian American, 2.8% Hispanic/Latino/a, 1.7% multiracial, and 0.6% Native American/Indigenous. Participants completed an online survey and received monetary compensation for their participation through CloudResearch.com. CloudResearch (formerly MTurkPrime) is an online portal in which requesters (individuals or businesses) can submit jobs for workers (participants). These services (e.g., CloudResearch, MTurk) have been used by academics as a means of collecting high ­ quality data in a timely manner from a diverse sample of individuals (Buhrmester et al., 2011). Participation in the survey was voluntary, refusal would not have any consequences, and participants could end their participation at any time or skip any questions without penalty.

Measures

Attitudes Toward Prisoners

To assess attitudes on the character of prisoners, their morals, their circumstances, and how they should be treated, 36 items were adapted from the Attitudes Toward Prisoners Scale (Melvin et al., 1985; α = .94; e.g., “Most

prisoners are victims of circumstance and deserve to be helped.”). Items were rated from 1 (strongly disagree) to 5 (strongly agree). Responses were reverse coded as necessary and averaged, such that greater positive values indicated more positive attitudes toward prisoners.

Attitudes Toward the Effectiveness of the Criminal Justice System

Attitudes surrounding the effectiveness of the criminal justice system were assessed through one item written for this study, rated from 1 (strongly disagree) to 5 (strongly agree): “Our criminal justice system is effective” (see Appendix). Therefore, greater positive values reflected beliefs in greater effectiveness of the criminal justice system.

Political Views

Endorsement of political ideologies was analyzed through two separate items used to assess one’s position on the political spectrum and how important they viewed democracy. Questions included “How do you view yourself politically?” rated from 1 (conservative) to 5 (liberal), and “How important is it for you to live in a country that is governed democratically?” rated from 1 (not at all important) to 5 (very important). These items were analyzed separately such that greater positive values reflected the endorsement of more liberal or more democratic political views.

Personal Opinions and Experiences

Social Dominance. Opinions on the concept of a social hierarchy with superior in­groups and inferior outgroups were assessed through 16 items taken from the Social Dominance Orientation Scale (Pratto et al., 1994; α = .92; e.g., “Some people are just inferior to others.”). Items were rated from 1 (strongly disagree) to 5 (strongly agree). Responses were reverse coded when necessary and averaged, such that greater positive values indicated a greater belief in social dominance or support for inequality. Attitudes Toward Racial Outgroups. Attitudes toward individuals of another race were assessed through 16 items adapted from the Outgroup Comfort Scale (Arriola & Cole, 2001; α = .87; e.g., “I feel uncomfortable around people of other races because they are different from me.”). Items were rated from 1 (strongly disagree) to 5 (strongly agree). Responses were reverse coded when necessary and averaged, such that greater positive values indicated more positive attitudes toward individuals of another race.

Experience with the Criminal Justice System. Participants’ own criminal record was assessed through five items written for this study rated yes or no (α = .78; e.g., “Have you ever been convicted of a crime?”). Responses were averaged such that greater positive values reflect more experience with the criminal justice system.

Safety Concerns

One’s concern for their safety and security was assessed through a series of items adapted from the World Values Survey (Inglehart et al., 2014). These items included “How secure do you feel in your neighborhood these days?” and were rated from 1 ( not at all secure ) to 4 (very secure), and “In the last 12 months, how often have you or your family felt unsafe from crime in your own home?” was rated from 1 (never) to 5 (very often). Security precautions due to safety concerns and having a history of victimization, or knowing someone who has, were assessed through five items rated yes or no (e.g., “For security reasons, do you prefer not going out at night?”). When necessary, items were reverse coded. Given the different response options, z ­ scores were created for each item and these scores were averaged (α = .58), such that greater positive values reflected greater safety concerns.

Results

General Attitudes

A series of one­sample t­tests were conducted to explore the general attitudes of our sample using the midpoint (3) of the scale. Analyses demonstrated that the sample overall had a positive attitude toward prisoners (M = 3.36; SD = 0.53), t(179) = 9.05, p < .001, and a negative attitude toward the effectiveness of the criminal justice system (M = 2.71; SD = 1.13), t(179) = ­3.44, p < .001. Furthermore, attitudes toward prisoners were significantly positively correlated with age (r = .19, p = .012). No significant gender or race differences were found.

Bivariate Correlations

A series of bivariate correlations were conducted to explore factors that predicted attitudes toward prisoners and the effectiveness of the criminal justice system. All results are displayed in Table 1. As displayed in Table 1, attitudes toward prisoners were negatively correlated with the effectiveness of the criminal justice system. That is, those with more negative attitudes toward individuals within the criminal justice system felt the criminal justice system itself was more effective. The bivariate correlations relevant to our study are discussed below.

Political Views. Results demonstrated that liberal political views were positively correlated with attitudes toward prisoners and negatively correlated with attitudes toward the effectiveness of the criminal justice system. Living in a democracy was positively related to attitudes toward offenders but was not related to effectiveness.

Personal Opinions and Experience. Results demonstrated that social dominance endorsement was negatively correlated with attitudes toward prisoners and positively correlated with attitudes toward the

effectiveness of the criminal justice system. Furthermore, attitudes toward racial outgroups were positively correlated with attitudes toward prisoners but negatively related to beliefs in effectiveness. Criminal experience was negatively correlated with attitudes toward prisoners but was not related to attitudes toward the effectiveness of the criminal justice system.

Safety Concerns. Results demonstrated that safety concerns were not related to attitudes toward prisoners but were positively related to beliefs in effectiveness.

Multiple Regression

A series of multiple regression analyses were conducted to see if any of our main factors uniquely predicted attitudes toward prisoners and the effectiveness of the criminal justice system, separately. As displayed in Table 2, multiple regression analyses demonstrated that, consistent with the bivariate correlations, social dominance and criminal experience both uniquely predicted attitudes toward prisoners. In addition, also consistent with the bivariate correlations, political views and safety concerns uniquely predicted opinions on the effectiveness of the criminal justice system. However, living in a democracy and attitudes toward racial outgroups were no longer related to either outcome variable.

Discussion

To summarize, participants with more negative attitudes toward the individuals involved with the system expressed

Descriptive Statistics and Bivariate Correlations for Factors Predicting Attitudes Toward Prisoners/Offenders and Effectiveness of the Criminal Justice System for Study 1

Note. N = 180. Correlations with Prisoner and Effectiveness are directly relevant to the study’s objective. Political views were assessed on a scale from conservatism to liberalism with lower values reflecting more conservative views and greater values reflecting more liberal views. For all other constructs, greater positive values reflect greater positive endorsement of the construct. * p < .05. ** p < .01.

TABLE 1

more positive attitudes toward the effectiveness of the criminal justice system. Furthermore, those with more liberal or less conservative political views were more positive toward individuals involved with the system, but more negative toward the criminal justice system’s effectiveness. Those expressing opinions favoring social dominance expressed more negative attitudes toward these individuals and more positive attitudes toward the system’s effectiveness. Individuals who held more positive attitudes toward people of another race also held positive attitudes toward individuals with criminal records, but negative attitudes toward the effectiveness of the criminal justice system. Those with more experience with the criminal justice system held more negative views toward individuals involved with the system. Individuals more concerned for their safety expressed more positive attitudes toward the criminal justice system’s effectiveness. Multiple regression analyses demonstrated that social dominance and experience with the criminal justice system both uniquely predicted attitudes toward individuals involved with the system, and political views and safety concerns uniquely predicted attitudes toward the effectiveness of the criminal justice system. Attitudes Toward the

TABLE 2

Multiple Regression Analyses for Factors Predicting Attitudes Toward Prisoners and Effectiveness of the Criminal Justice System for Study 1

Attitudes Toward Prisoners

Note. N =

Regarding demographics, attitudes toward prisoners were significantly positively correlated with age, but no significant gender or race differences were found. This lack of statistical significance may be due to a lack of diversity in the sample (twice as many women and predominantly White). Therefore, Study 2 was conducted to replicate and extend the results of Study 1.

Study 2

Method

Participants and Procedure

Before beginning data collection, all materials were approved by Quinnipiac University’s Institutional Review Board. Study 2 was conducted to be a replication of Study 1 with improved methodology and participant recruitment. Study 2 included 179 men, 217 women, and 6 unreported (Mage = 47.46, SD = 19.00, range 18–87; 51.5% White, 48.5% Black) who completed an online survey and received monetary compensation through Amazon’s Mechanical Turk (MTurk). As in Study 1, participation in the survey was voluntary, refusal would not have any consequences, and participants could end their participation at any time or skip any questions without penalty.

Measures

Attitudes Toward Offenders

The same measure utilized in Study 1 to assess attitudes toward prisoners was also used in Study 2 (α = .91). However, in Study 2, the word “prisoners” was changed to “criminal offenders.” This was done to broaden the questions and represent attitudes toward general offenders, as opposed to only those who are or have been incarcerated.

Attitudes Toward the Effectiveness of the Criminal Justice System

In Study 2, beliefs in the effectiveness of the criminal justice system were assessed through four items written for this study. This was done to ask about the effectiveness of specific areas of the criminal justice system, along with the criminal justice system as a whole (α = .80; e.g., “In general, I think the police are effective”; see Appendix). Items were rated from 1 (strongly disagree) to 5 ( strongly agree ). Responses were averaged such that greater positive values reflected beliefs in greater effectiveness of the criminal justice system.

Political Views

In Study 2, political ideologies were analyzed through the same two separate items used to measure this construct in Study 1.

Personal Opinions and Experiences

Endorsement of social dominance was assessed by the same measure used to analyze this construct in Study

1 (α = .90). Attitudes toward individuals of another race were assessed by the same measure used to analyze this construct in Study 1 (α = .87). Participants’ criminal records were assessed by the same five items used to analyze this construct in Study 1 (α = .79).

Safety Concerns

The same measures used to assess safety or security concerns and prior victimization used in Study 1 were also used in Study 2 (α = .54).

Results

General Attitudes

As in Study 1, a series of one ­ sample t tests were conducted to explore the general attitudes of our sample using the midpoint (3) of the scale. For Study 2, just as in Study 1, the sample had a positive attitude toward offenders (M = 3.10; SD = 0.52), t(401) = 3.88, p < .001, but unlike Study 1, had a positive attitude toward the effectiveness of the criminal justice system ( M = 3.44; SD = 0.85), t (401) = 10.44, p < .001. Participants who identified as male ( M = 3.54; SD = 0.82) thought the criminal justice system was more effective than participants who identified as female (M = 3.35; SD = 0.86), F(1, 394) = 5.27; p = .022. Additionally, participants who identified as White ( M = 3.52; SD = 0.82) did not significantly indicate that they believed the criminal justice system was more effective compared to participants who identified as Black (M = 3.36; SD = 0.87), F(1, 400) = 3.55; p = .06). Age was not related to our outcome variables.

Bivariate Correlations

A series of bivariate correlations were also conducted to explore factors that predicted attitudes toward offenders and the effectiveness of the criminal justice system, all results are displayed in Table 3. As displayed in Table 3, attitudes toward offenders were negatively correlated with the effectiveness of the criminal justice system. That is, those with more negative attitudes toward individuals within the criminal justice system felt the criminal justice system was more effective. The bivariate correlations relevant to our study are discussed below.

Political Views Results demonstrated that liberal political views were positively correlated with attitudes toward offenders and negatively correlated with attitudes toward the effectiveness of the criminal justice system. Living in a democracy was not related to attitudes toward offenders but was positively related to beliefs in effectiveness.

Personal Opinions and Experience Results demonstrated that endorsement of social dominance was negatively correlated with attitudes toward offenders

and positively correlated with attitudes toward the effectiveness of the criminal justice system. Furthermore, attitudes toward racial outgroups were positively correlated with attitudes toward offenders but negatively related to beliefs in effectiveness. Criminal experience was negatively correlated with attitudes toward offenders but was not related to attitudes toward the effectiveness of the criminal justice system.

Safety Concerns Safety concerns were not related to attitudes toward offenders or effectiveness.

Multiple Regression

A series of multiple regression analyses were conducted to see if any of our main factors uniquely predicted attitudes toward offenders and the effectiveness of the criminal justice system, separately. As displayed in Table 4, multiple regression analyses demonstrated that, consistent with the bivariate correlations, social dominance and criminal experience both uniquely predicted attitudes toward offenders. Also consistent with the bivariate correlations, political views, living in a democracy and social dominance uniquely predicted attitudes toward the effectiveness of the criminal justice system. However, attitudes toward racial outgroups were no longer related to either outcome variable. In addition, once controlling for the other predictors, safety concerns were positively correlated with attitudes toward the effectiveness of the criminal justice system.

Discussion

To summarize, similar to the results of Study 1, the results of Study 2 indicated that participants with more

Descriptive Statistics and Bivariate Correlations for Factors Predicting Attitudes Toward Prisoners/Offenders

Note. N = 180. Correlations with Prisoner and Effectiveness are directly relevant to the study’s objective. Political views were assessed on a scale from conservatism to liberalism with lower values reflecting more conservative views and greater values reflecting more liberal views. For all other constructs, greater positive values reflect greater positive endorsement of the construct. * p < .05. ** p < .01.

TABLE 3

negative attitudes toward individuals involved with the system expressed more positive attitudes toward the effectiveness of the criminal justice system. Furthermore, participants with more liberal/ less conservative political views expressed more positive attitudes toward individuals involved with the system and more negative attitudes toward the criminal justice system’s effectiveness. Those endorsing the importance of living in a democracy also endorsed more positive attitudes toward the effectiveness of the system. Participants with opinions favoring social dominance expressed more negative attitudes toward individuals with criminal records and more positive attitudes toward the system’s effectiveness. Those with positive attitudes toward individuals of another race reported positive attitudes toward individuals involved with the system, but negative attitudes toward the effectiveness of the system. Individuals with more experience with the criminal justice system expressed more negative views toward others involved with the system. Multiple regression analyses revealed social dominance and experience with the criminal justice system both uniquely predicted attitudes toward individuals involved with the system. Political views, the importance of living

TABLE 4

Multiple Regression Analyses for Factors Predicting Attitudes Toward Prisoners and Effectiveness of the Criminal Justice System for Study 2 Attitudes Toward Offendeers

in a democracy, social dominance and safety concerns uniquely predicted attitudes toward the effectiveness of the criminal justice system. Regarding demographics, age and race were not significantly correlated with any variables, but participants who identified as male indicated that they thought the criminal justice system was more effective.

General Discussion

Mass incarceration is a major issue in the United States, and individuals with criminal records face many barriers that threaten their success in society (Li, 2018; Wakefield & Uggen, 2010). With millions of people facing these barriers, it is important to understand how they are viewed by the general public, as the public’s attitudes can have a huge influence on these individuals’ likelihood of success after their involvement with the system (Hirschfield & Piquero, 2010; Rade et al., 2016). Previous literature has tended to lean toward evaluating the attitudes of individuals currently working within the criminal justice system or seeking to work within the system (e.g., Cunha et al., 2021; Na & Loftus, 1998). The literature also seems to place a focus on those with psychological or physiological extenuating circumstances (e.g., Avery et al., 2018; Weaver et al., 2019), along with specific types of individuals involved with the system that are highly discriminated against (e.g., sex offenders and violent offenders; e.g., Atkin­Plunk, 2020; de Vel­Palumbo et al., 2019). Additionally, the literature tends to focus on attitudes toward the punishment of individuals over attitudes directly toward the individuals (e.g., Côté­Lussier, 2016; Martin et al., 2017).

The present study replicated and extended this prior research (Hirschfield & Piquero, 2010; Na & Loftus, 1998; Rade et al., 2016; Rosselli & Jeglic, 2017; Wevodau et al., 2016) by demonstrating that liberal political views were significantly related to a more positive view of individuals with criminal records (therefore, more conservative political views were related to more negative views of these individuals). Also, consistent with the findings of Côté­Lussier (2016), our study found that the belief that certain groups of people are inferior to others was negatively correlated with attitudes toward individuals involved with the criminal justice system. This indicates that individuals who endorse dominant social groups and inequality among different groups view those with criminal records negatively and believe them to be inferior. Interestingly, we found that living “in a country that is governed democratically” was not a strong predictor of attitudes toward the individuals or the effectiveness of the criminal justice system (only in Study 2). This may be due, in part, to how the rise in more populist ideologies in the current political climate

has influenced how being “governed democratically” is understood (e.g., Morgan, 2022). That is, the definition of democracy may be more self­serving for those who endorse a more populist ideology (Keenan & de Zavala, 2021). This proposition is also supported in that social dominance was consistently a unique predictor of attitudes toward individuals involved in the criminal justice system and the effectiveness of the criminal justice system. Therefore, even though many of our participants endorsed the importance of being “governed democratically,” the perceived demographics of the “people” living in that democracy may be influenced by other factors.

However, not consistent with prior research (Boag & Wilson, 2014; Hirschfield & Piquero, 2010; Rade et al., 2016; Rosselli & Jeglic, 2017; Weaver et al., 2019), our study found that experience with the criminal justice system, and in turn other individuals involved with the system, uniquely predicted attitudes toward these individuals. This discrepancy could be due to prior research utilizing participants who have experience with this population but have not been part of the criminal justice system themselves (e.g., Kerr et al., 2018). Perhaps individuals with criminal records have more negative views of others who also have criminal records because of individuals they may have encountered during their experiences within the criminal justice system. Therefore, they may choose to emotionally distance themselves from others who have a criminal record. Courtesy stigma (Goffman, 1963), or stigma by association, suggests that individuals themselves may feel stigmatized if they are perceived as being part of, related to, or within proximity of someone else who already may be stigmatized by society. The effects of courtesy stigma as it relates to criminal offenders has been found among women romantically involved with men who are incarcerated (Deshay et al., 2021) and mothers of school shooters (Melendez et al., 2016). Therefore, those with experience with the criminal justice system may socially distance themselves from other criminals as a way to avoid courtesy stigma and potentially lessen negative feelings about their own behavior.

Finally, not consistent with previous research (Côté­Lussier, 2016; Gerber & Jackson, 2016), our results showed that safety concerns were positively correlated to attitudes toward the effectiveness of the criminal justice system only in Study 1 (not the individuals within the system, per se). More interestingly, safety concerns uniquely predicted attitudes toward the effectiveness of the criminal justice system in both Study 1 and Study 2. That is, when the influence of other factors is removed (e.g., social dominance, experience with the criminal justice system), individuals who have been victimized or fear being victimized have trust in the system to do what it is supposed to do.

Furthermore, prior evidence has shown significant demographic differences in attitudes (Hirschfield & Piquero, 2010). Our results only partially replicated previous research by demonstrating that age was significantly related to attitudes toward prisoners in Study 1 but was not related to any of the variables in Study 2. Study 2 also found significant gender differences regarding beliefs in the effectiveness of the criminal justice system (this was not found in Study 1). Race was not significantly correlated with any variables in either study. The differences in attitudes found in Study 1 and Study 2 could be explained by a multitude of reasons. For racial differences, Study 1 consisted of an almost entirely White sample, but Study 2 had a more even split among Black and White participants. This increased the statistical power in Study 2. We were interested in seeing if there were differences in attitudes between Black and White participants, but our study did not yield any significant results. Despite this study not finding significant differences in attitudes, it is still important to study considering the differences in the impact of racial disparities within the criminal justice system. Consistent with data from prior years, data from 2022 shows that Black individuals were imprisoned at a much higher rate: 911 per 100,000, but for White individuals, the rate was 188 per 100,000 (Carson & Kluckow, 2023). From 2012–22, more Black individuals than people of any other race were imprisoned, and around 3.5% of all Black residents in the U.S. were under some form of community supervision in 2022. For White and Hispanic individuals this number was 1.5%, and 0.5% for all other races (Buehler & Kluckow, 2024). Overall, Black Americans are disproportionately represented all throughout the criminal justice system (Bull Kovera, 2019; Sawyer, 2020). They are also disproportionately impacted by the consequences of a criminal record (Alexander, 2010; Pager, 2003; Wakefield & Uggen, 2010). Although this explanation cannot account for why Black and White participants did not differ in their attitudes in the present study, it clearly supports the rationale that Black individuals would have less faith in the criminal justice system as a whole (Cooper et al., 2020).

For gender differences, Study 2 also consisted of a greater and more even number of men and women compared to Study 1. Research has demonstrated that men and women may not differ in concerns about being the target of a crime but do differ in the extent to which they feel they could protect themselves if it happened (with men feeling less vulnerable; Adams & Serpe, 2000). This distinction may help to explain why there was a gender difference in beliefs in the effectiveness of the criminal justice system, but not toward individuals

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with criminal records specifically. That is, even though both men and women are aware of the prevalence of crime, men may feel less powerless over it. Furthermore, women, compared to men, may feel that the system is less likely to support them when they are victimized and may not be taken seriously by law enforcement (Gover et al., 2013). This can lead to both negative attitudes toward the criminal justice system and its effectiveness in curtailing criminal activity.

Future research should explore other factors that may influence attitudes toward individuals involved with the criminal justice system and the effectiveness of the system. For example, socioeconomic status can influence both perceptions of the criminal justice system (e.g., wealthier individuals are less likely to be involved in the criminal justice system; Dennison & Demuth, 2018) and perceptions of criminal offenders (e.g., low­status criminal offenders are viewed more harshly compared to high­status criminal offenders; Côté­Lussier, 2016). In addition, both neighborhood disadvantages (e.g., a greater number of residents living on public assistance or unemployed) and neighborhood affluence (e.g., a greater ratio of wealthy families, compared to poorer families) both uniquely predict rearrest within 12 months after release even after controlling for other individual factors (e.g., type of crime, number of arrests; Kubrin & Stewart, 2006). Therefore, because poorer socioeconomic status increases the likelihood of being involved with the criminal justice system (Lind et al., 2021), it also alters perceptions of who qualifies as a ‘criminal’ and the effectiveness of the system in controlling them.

Finally, the current study has implications for both the treatment of individuals caught up in the criminal justice system during incarceration and their life after release. For example, those with more negative attitudes toward these individuals and the effectiveness of the criminal justice system may have less tolerant attitudes toward the treatment of these individuals during incarceration. For example, when COVID vaccines first became available, incarcerated individuals had access to the vaccines before most of the general public due to their close­contact living situations (Berk et al., 2021). Additionally, many prisoners can take classes and obtain degrees free of charge while they are incarcerated (Easton et al., 2022). Those with more negative attitudes toward individuals involved with the system due to more conservative political views and populist ideologies may see these privileges as preferential treatment toward criminals and may even view these individuals as threats to their own ability to succeed (Côté­Lussier, 2016).

Limitations

The present study is not without limitations. Foremost,

these studies focused on the attitudes of White and Black individuals specifically because these two racial groups represent the greatest differences in attitudes toward criminal offenders (e.g., Cooper et al. 2021) and disparities within the criminal justice system (Bull Kovera, 2019). However, future research should include other racial minorities to increase the generalizability of our results. In addition, the present studies utilized a self­report questionnaire to collect data. Even though the use of self­report data is common in the research literature, there are concerns about social desirability and dishonesty with self­report measures (van de Mortel, 2008). However, precautions were taken to reduce these influences. Specifically, the survey was voluntary, participants could skip any questions they did not feel comfortable answering, and attention check items were used. Furthermore, concerns of social desirability may have less of an impact when expressing attitudes toward some social groups (Axt, 2018). That is, most people would be willing to express their opinions of individuals they see as criminal offenders. Additionally, the selfreport questionnaire consisted of several single­item measures and items created or adapted for this study. For example, the effectiveness of the criminal justice system was assessed with a single item in Study 1 and a series of items created for the study in Study 2. This methodological difference could have led to some of the discrepancy in results across these two studies. Study 1 asked about the effectiveness of the criminal justice system in general, and Study 2 expanded on this and asked about the effectiveness of different areas of the system (e.g., police, probation and parole, prisons), as well as asking about the system overall. Also, we did not give a specific definition of “effective” in this study. Self­report questionnaires are beneficial because they allow for more natural responses. We believed it would be better to directly ask participants about their beliefs on the effectiveness of the criminal justice system, as opposed to asking them questions in a less direct way (e.g., “The police make me feel protected” or “I believe prisons help reduce crime”). However, allowing for each individual’s definition of “effectiveness” limits the ability to clearly interpret the results. Furthermore, adapting existing items for our specific measure of safety concerns could have contributed to the poor reliability of this measure found in both studies. Therefore, future research should seek to replicate the overall findings of the current study by either altering the self­report items used or measuring these attitudes differently (e.g., rating the viability of job applicants with a criminal history, or monitoring participants’ behavior while interacting with a confederate posing as a person who has recently been incarcerated).

However, a major limitation to the present study is that it is exploratory in nature. Variables were selected based on previous research. Future research should offer a theoretical basis for these variables and thus increase their predictive validity. For example, Nayfeld (2022) has proposed a variety of reasons to explain attitudes toward criminal offenders, such as paternalism (an oppressive belief that the offender is incapable of knowing what is right) or hostility (a feeling that the offender is an enemy who must be removed from society). These types of attitudes may help to explain why social dominance and safety concerns are stronger predictors of attitudes and effectiveness, respectively. Similarly, the Moral Foundations Theory (Atari et al., 2023; Haidt & Joseph, 2004) focuses on six attributes such as proportionality (punishments or rewards are proportional to guilt or merit) and authority (tradition, power) which may also help to explain predictors such as political views and social dominance. Future research should use these and other theoretical models to better predict the general public’s attitudes toward criminal offenders and the criminal justice system.

Most importantly, this study demonstrated how political views, social dominance orientation, experiences with the criminal justice system, and safety concerns uniquely related to attitudes toward individuals involved with the criminal justice system and the effectiveness of the system. Therefore, the results of this study and previous research can be applied to reduce negative attitudes toward those with a criminal record. For example, conservative political views have been connected to more prejudice toward these individuals, along with various other groups such as LGBT+ individuals (e.g., Choe et al., 2019) and immigrants (e.g., Caricati et al., 2017). Beliefs in social dominance have been connected to more discriminatory views as well (e.g., Puckett et al., 2020; Zhirkov, 2021). Knowing this, it is important to look at models of prejudice reduction and implement prejudice reduction strategies, such as contact (e.g., Maunder et al., 2020) or perspective taking (e.g., Tompkins et al., 2015). Both of these methods seek to humanize the stigmatized groups in question through positive interactions and have been shown to reduce prejudice and stigma. In the context of our results, developing interventions that increase contact with individuals who have been involved with the criminal justice system, and allowing people to understand the circumstances of their involvement, may lead to more empathy and understanding, along with an increase in positive attitudes. Additionally, the results demonstrated that attitudes toward racial outgroups are associated with attitudes toward individuals involved with the system, which could indicate implicit or explicit biases in regard

to race and having a criminal record. This emphasizes the importance of reducing racial disparities within the criminal justice system and making efforts to reduce biases, both implicit and explicit.

Over 5.4 million individuals in the United States face serious discrimination and barriers to success due to their criminal records, and that number only includes the people currently part of the incarcerated and community supervision populations. The present study increased the understanding of the public’s attitudes toward these individuals and the criminal justice system itself. This is crucial because it can help influence public policy to increase empathy, decrease stigmatization and discrimination, and overall make the criminal justice system more effective for both the public and those cycling through the system.

References

Adams, R. E., & Serpe, R. T. (2000). Social integration, fear of crime, and life satisfaction. Sociological Perspectives, 43(4), 605–629. https://doi.org/10.2307/1389550

Alexander, M. (2010). The new Jim Crowe: Mass incarceration in the age of colorblindness. The New Press.

Arriola, K. R. J., & Cole, E. R. (2001). Framing the affirmative-action debate: Attitudes toward out-group members and White identity. Journal of Applied Social Psychology, 31(12), 2462–2483. https://doi.org/10.1111/j.1559-1816.2001.tb00185.x

Ashworth, C. D., & Feldman-Summers, S. (1978). Perceptions of the effectiveness of the criminal justice system: The female victim’s perspective. Correctional Psychologist, 5(3), 227–240. https://doi.org/10.1177/009385487800500303

Atari, M., Haidt, J., Graham, J. Koleva, S., Stevens, S.T., & Dehghani, M. (2023). Morality beyond the WEIRD: How the nomological network of morality varies across cultures. Journal of Personality and Social Psychology: Personality Processes and Individual Differences, 125(5), 1157–1188. https://doi.org/10.1037/pspp0000470

Atkin-Plunk, C. A. (2020). Should all violent offenders be treated equally? Perceptions of punishment and rehabilitation for violent offenders with varying attributes. Victims & Offenders 15(2), 218–242. https://doi.org/10.1080/15564886.2019.1711277

Avery, J. J., Starck, J., Xu, Y., Avery, J. D., & Cooper, J. (2018). Attitudes toward defendants with substance‐related charges: An analysis of a national sample of criminal defense attorneys. The American Journal on Addictions, 27(8), 639–645. https://doi.org/10.1111/ajad.12825

Axt, J. R. (2018). The best way to measure explicit racial attitudes is to ask about them. Social Psychological and Personality Science, 9(8), 896–906. https://doi.org/10.1177/1948550617728995

Baumer, E. P., Messner, S. F., & Rosenfeld, R. (2003). Explaining spatial variation in support for capital punishment: A multilevel analysis. American Journal of Sociology, 108(4), 844–875. https://doi.org/10.1086/367921

Berk, J., Rich, J. D., & Brinkley-Rubinstein, L. (2021). Why we vaccinate incarcerated people first. EClinicalMedicine, 35. https://doi.org/10.1016/j.eclinm.2021.100864

Boag, E. M., & Wilson, D. (2014). Inside experience: Engagement empathy and prejudice towards prisoners. Journal of Criminal Psychology, 4(1), 33–43. https://doi.org/10.1108/JCP-06-2013-0016

Buehler, E. D., & Kluckow, R. (2024, May). Correctional Populations in the United States, 2022 – Statistical Tables U.S. Department of Justice, Bureau of Justice Statistics. https://bjs.ojp.gov/document/cpus22st.pdf

Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s Mechanical Turk: A new source of inexpensive, yet high-quality data? Perspectives on Psychological Science, 6(1), 3–5. https://doi.org/10.1037/14805-009

Bull Kovera, M. (2019). Racial disparities in the criminal justice system: Prevalence, causes, and a search for solutions. Journal of Social Issues, 75(4), 1139–1164. https://doi.org/10.1111/josi.12355

Caricati, L., Mancini, T., & Marletta, G. (2017). The role of ingroup threat and conservative ideologies on prejudice against immigrants in two samples Creighton and Jellison | Attitudes Toward

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Attitudes Toward the Criminal Justice System and Offenders | Creighton and Jellison

of Italian adults. The Journal of Social Psychology, 157(1), 86–97. https://doi.org/10.1080/00224545.2016.1176552

Carlson, H., Thayer, R. E., & Germann, A. C. (1971). Social attitudes and personality differences among members of two kinds of police departments (innovative vs traditional) and students. Journal of Criminal Law, Criminology & Police Science, 62(4), 564–567. https://doi.org/10.2307/1141714

Carson, E. A., & Kluckow, R. (2023, November). Prisoners in 2022 – Statistical Tables. U.S. Department of Justice, Bureau of Justice Statistics. https://bjs.ojp.gov/document/p22st.pdf

Choe, E., Srisarajivakul, E., Davis, D. E., DeBlaere, C., Van Tongeren, D. R., & Hook, J. N. (2019). Predicting attitudes towards lesbians and gay men: The effects of social conservatism, religious orientation, and cultural humility. Journal of Psychology and Theology, 47(3), 175–186. https://doi.org/10.1177/0091647119837017

Cook, P. J. (1979). The clearance rate as a measure of criminal justice system effectiveness. Journal of Public Economics, 11(1), 135–142. https://doi.org/10.1016/0047-2727(79)90050-1

Cooper, M., Updegrove, A. H., Gabbidon, S. L., Higgins, G. E., & Potter, H. (2020). Examining the effects of race/ethnicity and race relations on public opinion about the overrepresentation of Black people in U.S. prisons. Victims & Offenders, 16(1), 1–19. https://doi.org/10.1080/15564886.2020.1850579

Côté-Lussier, C. (2016). The functional relation between social inequality, criminal stereotypes, and public attitudes toward punishment of crime. Psychology, Public Policy, and Law, 22(1), 46–57. https://doi.org/10.1037/law0000073

Cunha, O., Carvalho, F., de Castro Rodrigues, A., Cruz, A. R., & Gonçalves, R. A. (2021). Attitudes of police recruits towards offenders: The impact of the police training on attitudinal change. Journal of Police and Criminal Psychology. https://doi.org/10.1007/s11896-021-09484-5

Dandurand, Y. (2014). Criminal justice reform and the system’s efficiency. Criminal Law Forum, 25, 383–440. https://doi.org/10.1007/s10609-014-9235-y

Dennison, C. R., & Demuth, S. (2018). The more you have, the more you lose: Criminal justice involvement, ascribed socioeconomic status, and achieved SES. Social Problems, 65(2), 191–210. https://doi.org/10.1093/socpro/spw056

DeShay, R. A., Vieraitis, L. M., Copes, H., Powell, Z. A., & Medrano, J. (2021). Managing courtesy stigma: Women and relationships with men in prison. Criminal Justice Studies: A Critical Journal of Crime, Law & Society, 34(3), 251–267. https://doi.org/10.1080/1478601X.2021.1966628

de Vel-Palumbo, M., Howarth, L., & Brewer, M. B. (2019). ‘Once a sex offender always a sex offender’? Essentialism and attitudes towards criminal justice policy. Psychology, Crime & Law, 25(5), 421–439. https://doi.org/10.1080/1068316X.2018.1529234

Easton, S. (2022). Not just another brick in the wall? Protecting prisoners’ right to education. International Journal of Law, Crime & Justice, 69 https://doi.org/10.1016/j.ijlcj.2022.100530

Gerber, M. M., & Jackson, J. (2016). Authority and punishment: On the ideological basis of punitive attitudes towards criminals. Psychiatry, Psychology and Law, 23(1), 113–134. https://doi.org/10.1080/13218719.2015.1034060

Goffman, E. (1963). Stigma: Notes on the management of spoiled identity. Prentice-Hall.

Gover, A. R., Welton-Mitchell, C., Belknap, J., & Deprince, A. P. (2013). When abuse happens again: Women’s reasons for not reporting new incidents of intimate partner abuse to law enforcement. Women & Criminal Justice, 23(2), 99–120. https://doi.org/10.1080/08974454.2013.759069

Grossi, L. M. (2017). Sexual offenders, violent offenders, and community reentry: Challenges and treatment considerations. Aggression and Violent Behavior, 34, 59–67. https://doi.org/10.1016/j.avb.2017.04.005

Haidt, J., & Joseph, C. (2004). Intuitive ethics: How innately prepared intuitions generate culturally variable virtues. Daedalus, 133(4), 55–66. https://doi.org/10.1162/0011526042365555

Hirshfield, P. J., & Piquero, A. R. (2010). Normalization and legitimation: Modeling stigmatizing attitudes toward ex-offenders. Criminology, 48(1), 27–55. https://doi.org/10.1111/j.1745-9125.2010.00179.x

Inglehart, R., C. Haerpfer, A. Moreno, C. Welzel, K. Kizilova, J. Diez-Medrano, M. Lagos, P. Norris, E. Ponarin & B. Puranen et al. (Eds.) (2014). World Values Survey: Round Six - Country-Pooled Datafile Version JD Systems Institute. https://www.worldvaluessurvey.org/WVSDocumentationWV6.jsp.

Keenan, O., & de Zavala, A. G. (2021). Collective narcissism and weakening of American democracy. Analyses of Social Issues & Public Policy, 21(1), 237–258. https://doi.org/10.1111/asap.12274

Kerr, N., Tully, R. J., & Völlm, B. (2018). Volunteering with sex offenders: The attitudes of volunteers toward sex offenders, their treatment, and rehabilitation. Sexual Abuse: Journal of Research and Treatment, 30(6),

659–675. https://doi.org/10.1177/1079063217691964

Kubrin, C. E., & Stewart, E. A. (2006). Predicting who reoffends: The neglected role of neighborhood context in recidivism studies. Criminology, 44(1), 165–197. https://doi.org/10.1111/j.1745-9125.2006.00046.x

Leverentz, A. (2011). Neighborhood context of attitudes toward crime and reentry. Punishment & Society, 13(1), 64–92. https://doi.org/10.1177/1462474510385629

Li, M. (2018, March). From prisons to communities: Confronting re-entry challenges and social inequality. American Psychological Association. https://www.apa.org/pi/ses/resources/indicator/2018/03/prisons-to-communities Lind, K., Hellman, M., Obstbaum, Y., & Salonen, A. H. (2021). Associations between gambling severity and criminal convictions: Implications for the welfare state. Addiction Research & Theory, 29(6), 519–530. https://doi.org/10.1080/16066359.2021.1902995

Martin, N. D., Rigoni, D., & Vohs, K. D. (2017). Free will beliefs predict attitudes toward unethical behavior and criminal punishment. PNAS Proceedings of the National Academy of Sciences of the United States of America, 114(28), 7325–7330. https://doi.org/10.1073/pnas.1702119114

Maunder, R. D., Day, S. C., & White, F. A. (2020). The benefit of contact for prejudiceprone individuals: The type of stigmatized outgroup matters. The Journal of Social Psychology, 160(1), 92–104. https://doi.org/10.1080/00224545.2019.1601608

Melendez, M. S., Lichtenstein, B., & Dolliver, M. J. (2016). Mothers of mass murderers: Exploring public blame for the mothers of school shooters through an application of courtesy stigma to the Columbine and Newtown tragedies. Deviant Behavior, 37(5), 525–536. https://doi.org/10.1080/01639625.2015.1060754

Melvin, K. B., Gramling, L. K., & Gardner, W. M. (1985). A scale to measure attitudes toward prisoners. Criminal Justice and Behavior, 12(2), 241–253. https://doi.org/10.1177/0093854885012002006

Moak, S. C., Walker, J. T., Earwood, M., & Towery, G. (2020). Using reentry simulations to promote changes in attitude toward offenders: Experiential learning to promote successful reentry. American Journal of Criminal Justice, 45(1), 126–144. https://doi.org/10.1007/s12103-019-09500-9

Morgan, M. (2022). A cultural sociology of populism. International Journal of Politics, Culture & Society, 35(2), 179–199. https://doi.org/10.1007/s10767-020-09366-4

Na, E.-Y., & Loftus, E. F. (1998). Attitudes toward law and prisoners, conservative authoritarianism, attribution, and internal–external locus of control: Korean and American law students and undergraduates. Journal of Cross-Cultural Psychology, 29(5), 595–615. https://doi.org/10.1177/0022022198295001

Nayfeld, N. (2022). The varieties of attitudes towards offenders. Criminal Justice Ethics, 41(2), 95–120. https://doi.org/10.1080/0731129X.2022.2101591

Pager, D. (2003). The mark of a criminal record. American Journal of Sociology, 108(5), 937–975. https://doi.org/10.1086/374403

Pratto, F., Sidanius, J., Stallworth, L. M., & Malle, B. F. (1994). Social dominance orientation: A personality variable predicting social and political attitudes. Journal of Personality and Social Psychology, 67(4), 741–763. https://doi.org/10.1037/0022-3514.67.4.741

Puckett, J. A., Zachary DuBois, L., McNeill, J. N., & Hanson, C. (2020). The association between social dominance orientation, critical consciousness, and gender minority stigma. Journal of Homosexuality, 67(8), 1081–1096. https://doi.org/10.1080/00918369.2019.1603493

Rade, C. B., Desmarais, S. L., & Mitchell, R. E. (2016). A meta-analysis of public attitudes toward ex-offenders. Criminal Justice and Behavior, 43(9), 1260–1280. https://doi.org/10.1177/0093854816655837

Rosselli, M. K., & Jeglic, E. L. (2017). Factors impacting upon attitudes toward sex offenders: The role of conservatism and knowledge. Psychiatry, Psychology and Law, 24(4), 496–515. https://doi.org/10.1080/13218719.2016.1254562

Sawyer, W. (2020, July 27). Visualizing the racial disparities in mass incarceration. Prison Policy Initiative. https://www.prisonpolicy.org/blog/2020/07/27/disparities/ Shannon, S. K. S., & Page, J. (2014). Bureaucrats on the cell block: Prison officers’ perceptions of work environment and attitudes toward prisoners. Social Service Review, 88(4), 630–657. https://doi.org/10.1086/678448

Smith, D. J. (2005). The effectiveness of the juvenile justice system. British Society of Criminology, 5(2). https://doi.org/10.1177/146680250505349

Tompkins, T. L., Shields, C. N., Hillman, K. M., & White, K. (2015). Reducing stigma toward the transgender community: An evaluation of a humanizing and perspective-taking intervention. Psychology of Sexual Orientation and Gender Diversity, 2(1), 34–42. https://doi.org/10.1037/sgd0000088

van de Mortel, T. F. (2008). Faking it: Social desirability response bias in selfreport research. Australian Journal of Advanced Nursing, 25, 40–48. https://researchportal.scu.edu.au/esploro/outputs/journalArticle/Faking-itsocial-desirability-response-bias/991012821838002368/filesAndLinks?index=0 Wagner, P., & Bertram, W. (2020, January 16). What percent of the U.S. is

Creighton and Jellison | Attitudes Toward the Criminal Justice System and Offenders

incarcerated? (And other ways to measure mass incarceration). Prison Policy Initiative. https://www.prisonpolicy.org/blog/2020/01/16/percent-incarcerated/

Wakefield, S., & Uggen, C. (2010). Incarceration and stratification. The Annual Review of Sociology, 36, 387–406. https://doi.org/10.1146/annurev.soc.012809.102551

Weaver, C., Lee, J., Choi, H., Johnson, W. W., & Clements, C. (2019). Offenders living with mental illness: How are they perceived by future professionals? Journal of Social Work, 19(1), 83–101. https://doi.org/10.1177/1468017318757383

Wevodau, A. L., Cramer, R. J., Gemberling, T. M., & Clark, J. W., III. (2016). A psychometric assessment of the Community Attitudes Toward Sex Offenders (CATSO) Scale: Implications for public policy, trial, and research. Psychology, Public Policy, and Law 22(2), 211–220. https://doi.org/10.1037/law0000066

Zhirkov, K. (2021). Social dominance orientation and differential affect toward immigrant origin groups: Evidence from three immigration-receiving countries. International Journal of Intercultural Relations, 85, 170–183. https://doi.org/10.1016/j.ijintrel.2021.09.014

Author Note

Mackenzie L. Creighton https://orcid.org/0000­0002­4487­0097

Mackenzie L. Creighton is now employed at the Department of Psychology at the University of Massachusetts Boston. This research was supported by a student research and experiential learning grant from the Quinnipiac University College of Arts and Sciences. The authors report no conflicts of interest. The results of this study were presented at the 2022 Eastern Psychological Association regional conference. We would like to thank Michael J. Sheehan, PhD, for his helpful feedback on a draft of this manuscript.

Correspondence concerning this article should be addressed to Mackenzie L. Creighton at Mackenzie.Creighton@UMB.edu or William A. Jellison at William.Jellison@Quinnipiac.edu

APPENDIX

The following questions were written for the present set of studies.

Attitudes Toward the Effectiveness of the Criminal Justice System

Directions: Please rate the questions below with how strongly you agree, remember that there are no right or wrong answers. Please respond as honestly as you can on a scale ranging from 1 (strongly disagree) to 5 (strongly agree):

Study 1:

1. Our criminal justice system is effective.

Study 2:

1. In general, I think the police are effective.

2. In general, I think probation/ parole is effective.

3. In general, I think prisons/ jails are effective.

4. In general, I think overall the criminal justice system is effective.

Experience With the Criminal Justice System

Directions: Please answer all questions as honestly and accurately as possible. You may skip any question(s) you do not feel comfortable answering. Please answer ‘yes’ or ‘no’.

Study 1 & Study 2:

1. Do you know anyone who has ever been or is currently incarcerated?

2. Have you ever been arrested?

3. Have you ever been convicted of a crime?

4. Have you ever spent time on probation or parole?

5. Have you ever been incarcerated?

Dating Apps Users Among a Religious College Student Body: Profiles of Emotional and Psychosocial Well-Being

Lindsay Johnson, Robert R. Wright*, Brandon A. Jones, Maren Batman, Lavear Whitney, Kiyomi Miyasaki, and Anna Aho Department of Psychology, Brigham Young University–Idaho

ABSTRACT. Although common among many demographics, dating apps (e.g., Tinder) have become increasingly popular among college students. However, little is known regarding differences in emotional and psychosocial well­being related to their use, particularly within a religious college context. This study examined differences between dating apps users’ and nonusers’ psychological, emotional, and psychosocial well­being among young adult college students who self­identified as members of the Church of Jesus Christ of Latter­day Saints. A total of 818 participants (194 or 23.7% reported using dating apps) comprised mostly of women (62.2%) with an average age of 20.08 years completed an online questionnaire from introductory psychology courses at a large religious institution within a cross­sectional online survey design. Using t­test analyses, several significant differences emerged in dating experience, psychological basic needs (autonomy, competence, relatedness), emodiversity, and psychosocial variables (e.g., loneliness, depressive symptoms) between dating apps users and nonusers with users demonstrating poorer well­being. For instance, autonomy and competence were significantly higher for nonusers whereas global emodiversity and loneliness were higher for users (ps < .01). Gender emerged as an influential factor, such that differences in competence between users and nonusers were more prominent among men, whereas poor dating experiences and subjective health differences were only among women (ps < .05). Results support a marked difference between dating apps users’ and nonusers’ well­being, suggesting well­being deficits for dating apps users. Implications of this pattern of relationships are discussed relative to theory and practical dating application.

Keywords: dating apps, social media, self­determination theory, emodiversity, emerging adults

Recently, dating applications (dating apps) have revolutionized traditional dating into an online, remote context and fundamentally altered the landscape in the search for intimacy as they have become increasingly popular (Vogels & McClain, 2023). Although online dating forums have existed well before dating apps, this new technology has transformed the dating experience into a more commodified process, as users can rapidly sift through profiles and indicate interest by simply “swiping” someone’s profile (Hanson, 2021; Holtzhausen et al., 2020). It is now expected that online dating, including dating apps, will attract more than 452 million users worldwide by the year 2028 (Statista, 2024), and the influence is already profound as, in the United

States alone, three in ten adults have used a dating app, and 42% of those users claim that online dating made the search for a long­term partner easier (Vogels & McClain, 2023). Numerous dating apps exist that cater to specific populations (e.g., Tinder, Bumble, OkCupid, Grindr) including religious contexts (e.g., Mutual), and their use is particularly prominent among emerging adults (e.g., 48% of Americans under 30 years; Anderson et al., 2020). Few studies have investigated how exposure to dating apps might be associated with well­being profiles, particularly in unique contexts such as a religious college setting. As such, the current study examined differences in dating behaviors, psychological needs, emotional experience, and psychosocial variables between dating

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apps users and nonusers among emerging adults, a population known for use of and exposure to dating apps (~40% of undergraduate students; Shapiro et al., 2017).

Dating Apps and Dating Experience

Accumulating evidence has indicated a troubling pattern of social media use (including dating apps) that may correspond with a host of poor well­being indicators (e.g., loneliness, depressive symptoms) among emerging adults in college (e.g., Seabrook et al., 2016; Wright et al., 2020). Additionally, psychological needs such as autonomy, competency, and relatedness (Self­Determination Theory; Deci & Ryan, 2012) and the diversity of emotional experiences (i.e., emodiversity; Quoidbach et al., 2014), alongside other psychosocial variables (e.g., loneliness, depressive symptoms) may offer unique ways in which to evaluate well­being in this population. According to current research within the United States, marriage rates are declining (CDC, 2023), social isolation is increasing, and reports of loneliness are reaching dangerously high levels (U.S. Surgeon General, 2023), which have prompted much research into the use of dating apps (Castro & Barrada, 2020).

Several studies have emerged connecting the use of online dating, the precursor to dating apps, to poorer well­being (e.g., Bonilla­Zorita et al., 2021; Filice et al., 2022; Toma et al., 2008). Prominent theories accounting for the great surge of interest in online dating and dating apps have included commercialized or “marketized love” (Bandinelli & Gandini, 2022; Hobbs et al., 2017; Minina et al., 2022) where the search for intimacy is analogous to shopping behavior that has supplanted more traditional ideas of monogamy and commitment to romantic love. Others have posited that dating apps usage has been spurred on by a need for social compensation, as challenges in traditional dating (e.g., struggles with face­to­face interactions, social skills deficits) lead individuals to other modalities (e.g., computer­mediated) that may compensate for these deficiencies (Toma, 2022; Valkenburg & Peter, 2011). Although specific theories may offer contrasting explanations regarding dating apps use, increased use is clearly observed in emerging adult populations.

Dating valuations (dating importance) and experiences are likely domains for differences between dating apps users and nonusers. Although some studies have found no difference between relationship satisfaction or wellbeing for couples that met using dating apps compared to those that did not (Potarca, 2020), the importance of dating varies according to individual expectations. Moreover, these expectations are often inflated or exaggerated to unrealistic levels when one uses social media more often (Fardouly et al., 2015; Yang, 2016) and frequently leads to lower relationship satisfaction,

investment, and commitment (Vannier & O’Sullivan, 2017, 2018). Furthermore, unrealistic expectations are likely compounded further by the increased use of electronics and online dating forums in the wake of the COVID­19 pandemic restrictions (Vogels & McClain, 2023; Wright et al., 2022). Considering gender, some studies have found that communication over dating apps seems to be more beneficial to men, especially for those who have high dating anxiety (Sumter et al., 2017; Sumter & Vandenbosch, 2019). Nevertheless, the intricacies of how dating apps users may differ from nonusers in dating valuations, frequency, and quality, especially between genders, have not been explored.

Dating Apps and Meeting Psychological Needs

Relatedly, psychological well­being has been implicated in dating apps use, and self­determination theory (SDT) offers some insight. Referred to as “basic psychological needs,” SDT (Deci & Ryan, 2012; Ryan & Deci, 2002) proposes autonomy, competency, and relatedness as inherent human goals, driving behavior to satisfy these needs. SDT has been applied in numerous contexts related to well ­ being and dating with several major studies and meta ­ analyses connecting increased SDT variables to effective health behavior promotion (Ng et al., 2012), improved sexual and relational quality experiences in dating (Brunell & Webster, 2013), and successful romantic relationship processes such as communication and attachment (Knee et al., 2013). Moreover, a growing body of evidence has suggested a link between SDT variables and social media use including friending behavior (Miller & Prior, 2010), the connection between well­being and social media use (Sheldon & Titova, 2023), and how continued use of social media may meet these psychological needs, at least in some cases (Azzahro et al., 2018).

Furthermore, SDT posits that when an individual perceives deficiencies in any of these three domains, they are innately motivated to address them (Deci & Ryan, 2012; Ng et al., 2012). Building on this idea, Sheldon and Titova (2023) employed three studies to uncover the link between SDT constructs to continued social media use, particularly in efforts to meet relatedness and enhance subjective well­being. They concluded that SDT constructs provide a strong theoretical justification, as individuals may be motivated to use dating apps to address these SDT needs. For instance, in relation to dating, if one feels a sense of low autonomy (e.g., powerless to date as frequently as desired), low competency (e.g., lack of successful dating), or low relatedness (e.g., lower quality of dating experience) within their in ­ person dating practices, that individual may be prompted to seek out other avenues, such as dating apps.

Dating Apps and the Emotional Experience

Representing another domain important to well­being, dating apps use may be related to the emotional and psychosocial experience. It is well­documented that dating experiences are related to positive and negative affect (Her & Timmermans, 2021; Wright, Wilson et al., 2024), but much less is known about the diversity of emotional experience (emodiversity) in dating especially involving dating apps. Emodiversity is based on Shannon’s entropy, a biodiversity equation, meant to illustrate how greater diversity provides a healthier ecosystem (Magurran, 2004; Shannon, 1948). The original application of this model into the emotional context was made by Quoidbach and colleagues (2014) where they discovered that a more diverse experience in positive, negative, and global emotional domains, respectively, were associated with improved health and wellness, including lower levels of depression and better objective health (fewer doctor visits).

Subsequent investigations, however, have highlighted that these relationships seem a bit more nuanced. First, Ong and colleagues (2018) and Benson et al. (2018) reported positive emodiversity to be related to improved health (i.e., lower levels of inflammation, better selfreported physical health), but not negative emodiversity, suggesting some inconsistencies. Second, Grossman et al. (2019) reported that global emodiversity (positive and negative combined) is connected to making less biased inferences about the social world, which may have implications for social interactions such as dating. Third, greater daily fluctuations in negative emodiversity, specifically, seem to be related to greater stress and more physical health symptoms across adulthood (Liu et al., 2016). Finally, Urban­Wojcik and colleagues (2022) found that greater positive emodiversity was associated with fewer symptoms of depression, anxiety, and physical health, but was not related to improved eudaimonic (life, growth, flourishing) well­being or improved cognitive functioning. Moreover, greater negative emodiversity was related to more symptoms of depression, anxiety, and physical health with only one positive outcome connection, better executive functioning. Whereas this unique constellation of seemingly contradicting findings highlights a need to further refine the concept and application of emodiversity (Brown & Coyne, 2017; Urban­Wojcik et al., 2022), it also suggests that characteristics of the context may be influential. As a context that is infused with emotion, dating offers another forum in which to explore the emodiversity concept, particularly between in dating apps use.

Dating Apps and the Psychosocial Experience

Several studies have highlighted how the psychosocial experience may be different for dating apps users

compared to nonusers. For instance, depression, anxiety, and perceived stress are typically higher among users (Toma, 2022), particularly for swipe­based dating apps (Holtzhausen et al., 2020). Furthermore, the rapid pace of interactions and the potential for ghosting or deceptive behavior can contribute to feelings of anxiety, distrust, and social isolation or loneliness (Navarro et al., 2020). On this point, another study identified perceived loneliness as a variable that covaries with dating apps use, implying perceptions of social support may be involved as well (Coduto et al., 2020). Moreover, Tinder users, specifically, seem to have an increased negative emotionality and difficulties governing cognitive elements such as self­conscious social comparisons and evaluations (Her & Timmermans, 2021). Thus, other cognitive components such as happiness valuation (relative importance of happiness, Mauss et al., 2011), subjective evaluations of health (Benson et al., 2018), body appreciation, and self­esteem (e.g., Bonilla­Zorita et al., 2023; Fardouly et al., 2015), and satisfaction with life are similarly implicated. Two additional studies on social media use lend substantial support to the investigation of these constructs. First, Wright et al. (2020) found that increased time spent on social media apps was related to poorer well­being in nearly all these constructs among emerging adults in college. In a larger, subsequent study, Wright et al. (2021) noted similar findings among adults across the Western United States. Thus, these studies suggest the need to include these psychosocial variables in an assessment of differences of dating apps use.

Finally, another factor that can be strongly influential in the general dating experience, and use of dating apps, is the religious context. Among certain religious sects (e.g., conservative Protestants, Catholics), dating is held in high regard especially as it is related to religious observances such as marriage. Some estimates place Christian faiths, which encourage marriage among their young adult membership, as representing nearly 66% of college graduates (Pew Research Center, 2023). Furthermore, it is important to note that motivations for dating can vary greatly across young adults as those who are actively religious often seek marriage whereas those who are not religiously active may date with other expectations (e.g., casual sex; Weitbrecht & Whitton, 2020; Whitehead & Popenoe, 2000). This suggests that a religiously active group of college students may approach the use of dating apps with unique motivations.

For instance, the Christian sect of the Church of Jesus Christ of Latter­day Saints (previously known as Mormon, LDS) espouses traditional gender roles (e.g., men ask women on dates; Lever et al., 2015) and values (e.g., abstinence from premarital sex, marriage as the goal of dating to establish families), including

strong encouragement for marriage (Bartkowski et al., 2011). This emphasis likely coincides with observed higher rates of marriage among this group (Bartkowski et al., 2011) and may correspond with a wide spectrum of health benefits including longer life expectancy (Enstrom & Breslow, 2008). Moreover, there is some evidence to suggest that dating behavior in this population may be associated with unique outcomes. For example, in a study on quality dating experiences within this population, Wright, Wilson et al. (2024) discovered that women reported a higher occurrence of poor dating experiences. However, men, demonstrated stronger associations between poor dating experiences and deleterious health outcomes (e.g., depressive symptoms, peer support, conflict), suggesting that when men in this religious context experience a poor dating experience, they may be more likely to internalize this as a personal failure. These findings run counter to prominent findings in the literature, highlighting the prominent influence the religious context may exert. Thus, these studies suggest that a population of religious college students in the Church of Jesus Christ of Latter­day Saints offer an opportunity to examine unique psychosocial and emotional experiences relative to the use of dating apps.

Current Study

The current study examined differences of dating experience and well­being between users and nonusers of dating apps among a group of college students who identified as members of the Church of Jesus Christ of Latter­day Saints. Consistent with the literature reviewed above, we proposed five hypotheses:

H1: The dating experience (i.e., valuation, frequency, quality) would be perceived as lower for those who use dating apps for dating purposes compared to those who do not use dating apps.

H2: Basic psychological needs from self­determination theory (i.e., autonomy, competence, relatedness; Deci & Ryan, 2000) would be lower for those who use dating apps for dating purposes compared to those who do not use dating apps.

H3: The emotional experience (i.e., positive/ negative affect, emodiversity, happiness valuation) of those who use dating apps for dating purposes would be more negative compared to those who do not use dating apps.

H4: Psychosocial well­being (i.e., subjective overall health, body appreciation, satisfaction with life, loneliness, perceived social support, stress, anxiety,

depressive symptoms) of those who use dating apps for dating purposes would be lower (poorer health) than those who do not use dating apps.

H5: Gender differences would exist within dating experience such that, for psychological needs, emotional experience, and psychosocial well­being, those who use dating apps for dating purposes would be lower than those who do not.

Method

Participants

After receiving IRB approval at Brigham Young University – Idaho, participants were solicited from on ­ campus introductory psychology courses. Only those who gave consent for their data to be included in potential research publications, were above age 18, and indicated they provided accurate responses (i.e., attention check) were included in analyses. To further make sure our participants fit the study criteria, we asked whether they were married, engaged to be married, in a committed relationship, or single. After screening for only those who were currently dating (i.e., not married, not engaged to be married), participants (n = 818) were an average of 20.08 years (SD = 2.22) and comprised mostly of women (62.2%), and were primarily White (84.2%) with Hispanic (6.8%), Black (2.6%), Asian American (1.8%), Native American (0.4%) and multiracial (3.4%) also represented. Education level was mostly first­year (59.8%) or sophomore (27.1%) with nearly half of the sample (49.0%) indicating that it was their first semester as a first­year student. Finally, participants reported being currently enrolled in an average of 12.61 ( SD = 2.20) credits, and many were employed part(42.7%) or full­time (3.4%). All self­identified as members of the Church of Jesus Christ of Latter­day Saints.

Procedure

Prospective student participants received an email invitation and followed a link (via Qualtrics) to provide consent and completed an online questionnaire regarding college student life, including dating experiences and other aspects of well­being. Data were collected during three semesters (winter, spring, fall) in 2023. Student participants were given course credit and were allowed to select between several participation options. Completion of the online questionnaire took a median of 52.25 minutes.

Measures

The questionnaire queried a broad range of constructs, all described below by construct category: demographic, dating experience, psychological basic needs and emotional well ­ being, and psychosocial well ­ being.

Johnson, Wright, Jones, Batman, Whitney, Miyasaki, and Aho |

Whenever possible, we selected existing scales with established evidence for validity to increase confidence in our findings.

Demographic Constructs

Demographic data including age, gender, relationship status, ethnicity, education level, credit enrollment, and employment status were collected. For the purposes of the current study, we investigated some additional variables. Using one item each, we asked about the number of close friends they had (i.e., people that you feel at ease with, can talk to about private matters, and can call on for help), and if they had ever been in a prior romantic relationship before (to ascertain whether they had been in a previous relationship break­up). Finally, as dating apps are social media, we queried daily time spent on social media by asking participants to indicate time they spent on all social media each day during the past month on a sliding scale (0 to 10 hours).

Dating Experience Constructs

Dating Apps Use. Dating apps use was captured by using one question, “Do you have a dating app (e.g., Mutual, Tinder) and use it for the purpose of your own dating?” with a dichotomous (yes/no) response format.

Dating Frequency. Dating frequency was assessed by a single item asking how often participants were dating during the past three months.

Dating Valuation Scale. Using a four­item measure (McDonald & McKinney, 1994), we investigated dating valuation on a 4­point agreement scale (1 = strongly disagree, 4 = strongly agree), where higher values represent greater personal importance of dating. A sample item reads “being involved in a steady dating relationship is very important to me.” Internal consistency (α = .74) was like the original study (α = .78).

Dating Quality Scales. Dating quality was assessed using the Quality Dating Experience (QDE) measure that consisted of two 10­item gender­specific scales (Wright, Wilson et al., 2024). This examines quality dating experience for the past three months on a 5­point agreement scale ( 1 = strongly disagree , 5 = strongly agree) such that higher values mean greater quality. Sample items included “Those whom I date “make me feel socially comfortable” and “Those whom I date are respectful” for men and women, respectively. Both exhibited acceptable internal consistency (αs = .87, .92, men and women, respectively), similar to the original study (αs = .89, .92, respectively).

Poor Dating Experience Scales. To represent poor dating experience, we used the Poor Dating Experience (PDE) measure that includes two gender­specific scales (8 items for men, 12 items for women; Wright, Wilson et

al., 2024). This was within the same timeframe as quality dating (past 3 months) and the same agreement scale with higher values representing poorer dating experiences. Sample items include “Those I date are rude or impolite” and “I feel like those I date act or become possessive” for men and women, respectively. Both measures demonstrated acceptable internal consistency (αs = .78, .83, men and women, respectively), comparable to the original study (αs = .83, .84, respectively).

Psychological Basic Needs and Emotional Well-Being Constructs

Psychological Basic Needs. To capture the psychological basic needs (Deci & Ryan, 2000) constructs of autonomy, competence, and relatedness, we used their 21­item measure on a 7­point agreement scale (1 = not at all true, 7 = very true) and created composite scores for each subscale. Specifically, six items represented autonomy (α = .65; e.g., “I feel I am free to decide for myself how to live my life”), six for competence (α = .68; e.g., “People I know tell me I am good at what I do”), and nine for relatedness (α = .83; e.g., “I really like the people I interact with”) regarding their life in general. In one of the original investigations of this measure, the authors noted similar internal consistency estimates for autonomy (αs = .62, .79), competence (αs = .81, .73), and relatedness (αs = .57, .84; Deci et al., 2001).

Differential Emotion Scale. We examined positive and negative emotional experiences using the Differential Emotion Scale (Philippot et al., 2003) modified to be on a 5­point frequency scale. The scale captured nine positive (e.g., awe, joy, hope) and nine negative emotional states (e.g., fear, anxiety, shame) on a 5­point frequency scale (1 = never, 5 = most of the time). Both demonstrated acceptable internal consistency (αs = .81, .84, respectively), though the original publication did not report internal consistency estimates.

Emodiversity. Closely following the method set forth by Quoidbach and colleagues (2014), we computed three emodiversity indexes from the Differential Emotion Scale, which represented the diversity of the emotional experience in positive, negative, and global domains. Specifically, we divided the frequency of an emotional experience by the total number of frequencies of all types of emotion, then multiplied this proportion by its natural log. Next, we repeated this for each emotion captured, then summed all these products and multiplied the total by ­1. This produced a scale so that higher values represent a more diverse emotional experience.

Happiness Valuation Scale. Happiness valuation, or assigning high importance to the experience of happiness, was assessed using a 7­item scale (Mauss et al., 2011) on a 7­point agreement scale (1 = strongly disagree, 7 = strongly

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agree). A sample item reads, “If I don’t feel happy, maybe there is something wrong with me.” Internal consistency was acceptable (α = .76) and comparable to the original scale development findings (α = .76).

Psychosocial Well-Being Constructs

Subjective Overall Health. Subjective overall health was assessed with the single­item EuroQol Fifth Dimension measure (Kind et al., 2005) where participants rated their own physical health on a scale from 0 (worst physical health) to 100 (best physical health).

Body Appreciation. Level of body appreciation (i.e., body image) was examined on a 7­point agreement scale (1 = not at all true, 7 = very true) using the 13­item

Body Appreciation Scale (Avalos et al., 2005; α = .94) such that higher scores meant more appreciation for one’s body. A representative item includes, “I feel good about my body.” Our internal consistency estimate (α = .94) was like the original study (α = .93).

Satisfaction With Life Scale. Also measured on a 7 ­ point scale, satisfaction with life (Diener et al., 1985) was captured with five items where participants indicated their level of agreement regarding their current life condition (1 = strongly disagree, 7 = strongly agree). One of the items was “In most ways, my life is close to my ideal.” Internal consistency in our data (α = .86) was similar to the original study (α = .87) Short Loneliness Scale . Perceived l oneliness

TABLE 1

Correlation Matrix for Study Variables Among Dating Apps Users Only

1. Dating Frequency 6.18 (7.20) –

2.

3.

4.

5.

7. Autonomy

Note. n = 194; Dating Quality and Poor Dating Experience variables are gender specific. Dating frequency is according to prior 3 months. * p < .05. ** p < .01

during the past month was assessed using the 3­item Short Loneliness Scale (Hughes et al., 2004) on a 5­point frequency scale (1 = never, 5 = all the time). A sample item reads, “How often do you feel left out?” Comparable acceptable internal consistency estimates resulted from both our study (α = .83) and the original study (α = .72).

Interpersonal Support Evaluation List. Using the Interpersonal Support Evaluation List (ISEL; Cohen & Hoberman, 1983), general social support was assessed with twelve items on a 4­point agreement scale (1 = definitely false, 4 = definitely true) of perceived availability of social support. One of the items was, “There is someone I can turn to for advice about handling problems with my family.” Acceptable internal consistency estimates resulted in our study data (α = .86) and in the original study (α = .77)

Perceived Stress Scale . Overall life stress was queried using seven items from the Perceived Stress Scale (Cohen et al., 1983) on a 5­point frequency scale (1 = never, 5 = very often). One of the questions in this scale was “How often have you felt that you were unable to control the important things in your life?” Similar internal consistency estimates were found in our study (α = .83) to the original study (α = .84).

Anxiety Scale. Using this same 5­point frequency scale, anxiety during the past three months was assessed using the four­ item measure (anxious, worried, at ease, comfortable) from Butz and Yogeeswaran (2011). Internal consistency was acceptable (α = .81) and like the original study (α = .76).

CES-Depression 5-item Scale. Acute depressive symptomology during the past week was assessed using the CES­D 5­item measure (Bohannon et al., 2003) on a 4­point scale (1 = rarely or none of the time; 4 = most or all of the time). A sample item was “I felt depressed.” Scale internal consistency was acceptable (α = .77) and comparable to the original study (α = .76).

Data Analysis

First, to address differences between users and nonusers of dating apps (H1­4), we analyzed differences in terms of dating experience, psychological basic needs, as well as emotional and psychosocial well­being using independent­samples t tests and reported Cohen’s d main effects. Next, we examined these same constructs within genders (H5) such that men users were compared to men nonusers and women users were compared to women nonusers with the same statistical approach. To protect against possible Type I error, which may be inflated by conducting multiple significance tests, we applied Bonferroni’s correction throughout all t tests of differences between those who use dating apps and those who do not. Finally, we explored a few alternative explanations by examining additional demographic variables.

Results

Means, standard deviations, and correlations for study variables are listed in Tables 1 and 2. A total of 194 participants (23.7%) reported having a dating app and using it for the purpose of their own dating; men (25.9%; n = 80) indicated slightly higher use than women (22.4%; n = 114). Moreover, 62.6% of the entire sample reported having been in a prior relationship (e.g., a “breakup” had occurred), had an average of 5.20 (SD = 1.95) close friends, and spent an average of 183.84 (SD = 166.93)

TABLE 2

Comparison of Dating Apps User and Nonuser Well-Being

Dating Experience

Emotional Well-Being

Psychosocial Well-Being

Note: Bonferroni corrections were applied such that the significance level of .05 was converted by dividing .05 by the number of tests run within each analysis category. As such, each category had a different adjusted p value that represented the .05 significance. Δ represents the difference between dating apps users and nonusers, such that a positive value denotes a higher score for dating users for that variable. Dating frequency is according to prior 3 months history. Dating Quality and Poor Dating Experience variables are not reported here, but in Tables 3 and 4 because they are gender-specific.

* p < .05 ** p < .01. *** p < .001.

Dating Apps and Well-Being | Johnson, Wright, Jones, Batman, Whitney, Miyasaki, and Aho

minutes on social media daily. Participants reported an average of 7.40 (SD = 9.48) dates in the past three months. Correlations among dating apps users revealed many noteworthy relationships between dating and well­being variables for this group (see Table 1).

TABLE 3

Comparison of Dating Apps User and Nonuser Well-Being Among Men Only

.003 .42

Note: Bonferroni corrections were applied such that the significance level of .05 was converted by dividing .05 by the number of tests run within each analysis category. As such, each category had a different adjusted p value that represented the .05 significance. Δ represents the difference between dating apps users and nonusers, such that a positive value denotes a higher score for dating users for that variable. Dating frequency is according to prior 3 months history. * p < .05. ** p < .01. *** p < .001.

Hypothesis 1 (Dating Experience)

First, for H1 among the entire sample (see Table 2), dating valuation differed significantly (p < .05) between the two groups, such that users indicated dating was more important to them. After applying Bonferroni corrections, the difference in dating frequency became nonsignificant, though those who used dating apps reported an average of 1.61 dates less than those who did not use dating apps. Because our measures of dating quality and poor dating experience were both genderspecific, we report those results below for H5 (see Tables 3 and 4). Thus, our results did not support that the general dating experience would be lower for users rather than nonusers, as dating was more important to users.

Hypothesis

2

(Psychological Needs)

In our evaluation of H2 among the entire sample (see Table 2), we found statistically significant differences ( p < .05) across all three psychological basic needs such that dating apps users reported lower levels of autonomy, competency, and relatedness than nonusers. These findings supported our hypothesis that dating apps users have deficits in meeting their psychological needs compared to those who do not use dating apps.

Hypothesis 3 (Emotional Experience)

Similarly, H3 analyses uncovered statistically significant differences in the emotional experience of users versus nonusers for many variables. Specifically, negative affect was higher, suggesting a poorer emotional profile for dating apps users. Regarding emodiversity, positive and negative emodiversity were not related to dating apps use, though global emodiversity was higher among those who used dating apps. This suggests that those who use dating apps have a greater range of emotional experience across all emotions than those who do not use dating apps. Lastly, happiness valuation differences were not statistically significant, but users tended to place a greater value on being happy than nonusers. Thus, these findings were mixed, providing only partial support for poorer emotional health for dating apps users, suggesting a more nuanced interpretation of the emotional profile of dating apps users compared to nonusers.

Hypothesis 4 (Psychosocial Experience)

Results for H4 (see Table 2) were straightforward, as users reported a consistent, statistically significant (p < .05) poorer psychosocial well­being profile for several variables. The strongest differences were observed in depressive symptoms (d = .35), loneliness (d = .33), and perceived social support (d = .27). Collectively, in support of our hypothesis, these results suggest that users’ psychosocial well­being is lower than nonusers.

Hypothesis 5 (Gender Differences)

Comparisons of means for all aforementioned variables between users and nonusers within gender (H5; see Tables 3 and 4) revealed several significant differences. First, among men (see Table 3), lower dating quality for users was the only significant (p < .05) difference among the dating experience variables. Moreover, autonomy and competency were both significantly lower (p < .05) among users, but relatedness was only trending that direction. Among emotional well­being variables, no variables were statistically significant (p < .05) between users and nonusers. Psychosocial well­being was significantly (p < .05) lower among men users than men nonusers across depressive symptoms, loneliness, and perceived social support.

Second, among women (see Table 4), dating frequency and dating quality were significantly (p < .05) lower and poor dating experience higher for users compared to nonusers. Women users reported significantly (p < .05) lower autonomy than women nonusers and no other psychological basic needs differences. Emotional well­ being was lower for women users than women nonusers in terms of higher negative affect, but not emodiversity. Psychosocial well­being was consistently poorer among women users in terms of loneliness, depressive symptoms, and subjective overall health. In sum, these results partially supported our hypothesis that gender­specific differences exist between users and nonusers with users showing health deficits.

Alternative Explanations

Finally, we explored alternative explanations including age, enrolled credits, daily social media time, prior breakup history and number of close friends. First, although age significantly differed between dating apps users ( M = 20.45, SD = 2.91) and nonusers (M = 19.96, SD = 1.96) t(815) = 2.19, p = .029, it was a negligible effect size (d = 0.15). Second, the number of credits enrolled in was significantly higher for nonusers (M = 12.71, SD = 2.19) than users (M = 12.31, SD = 2.23) t(815) = 2.21, p = .028, though this was also negligible (d = 0.15). Third, daily time spent on social media was significantly higher for users (M = 219.65, SD = 188.94) than nonusers (M = 172.49, SD = 158.02) t(815) = 3.15, p = .002, d = 0.22, which could potentially account for health discrepancies, as more time on social media is often associated with poorer well­being outcomes (Nienstedt et al., 2023; Seabrook et al., 2016). Thus, although being older (as a college student), taking less credits, and spending more time on social media may relate to dating apps use, our results suggest these are rather inconsequential relationships, likely not fully explaining the observed differences in our findings. Lastly, prior breakup history ( r = ­ .06, p = .16) and

number of close friends, t(815) = .21, p = .84, d = 0.02, were not associated with dating apps use, suggesting that previous poor dating outcomes (i.e., breakup) or number of close social ties are not good indicators of

TABLE 4

Comparison of Dating Apps User and Nonuser Well-Being Among Women Only

Basic Needs (Self-Determination)

Note: Bonferroni corrections were applied such that the significance level of .05 was converted by dividing .05 by the number of tests run within each analysis category. As such, each category had a different adjusted p value that represented the .05 significance. Δ represents the difference between dating apps users and nonusers, such that a positive value denotes a higher score for dating users for that variable. Dating frequency is according to prior 3 months history.

* p < .05. ** p < .01. *** p < .001.

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dating apps use for dating purposes.

Discussion

Social media has opened numerous possibilities to expand the social environment, including the use of dating apps. However, relatively little is known about differences between dating apps users and nonusers regarding their psychological, emotional, and psychosocial well­being, particularly within a religious college student context. As such, the current study sought to address this gap by exploring potential differences between those who use dating apps and those who do not. Findings highlighted some differences in the overall dating experience and uncovered noteworthy deficits among dating apps users relative to those who do not use dating apps, including lower levels of met psychological basic needs, poorer emotional well­being, and increased detrimental psychosocial indicators (e.g., depressive symptoms, loneliness). Moreover, there were some differences specific to gender, suggesting a few gender­related influences within the religious context. Thus, all these results provide important implications for the use of dating apps among religious college students.

Dating Experience and Psychological Needs

First, and not surprisingly, dating apps users reported a history of dating experience that was perceived as poorer than nonusers with corresponding lower ratings for psychological basic needs. Those who hold greater intrinsic importance for dating may be drawn to use dating apps or, those who use dating apps may rate dating as more important to them for a relationship. Since dating is an important social behavior during emerging adulthood, those holding higher valuations of dating may turn to dating apps to maximize chances for success (Bryant & Sheldon, 2022), particularly for other motivations such as similar morals or external pressures to date, as might be observed in religious settings (Richardson et al., 2020). Moreover, the relative ease of dating apps in searching for intimate relationships may also appeal to those seeking to improve the frequency and quality of dating (Bandinelli & Gandini, 2022; Hobbs et al., 2017; Minina et al., 2022) and correspond with more problematic habitual behavior (e.g., cravings; Bonilla­Zorita et al., 2023). Likely, lower dating frequency and quality may motivate users to investigate the use of dating apps to improve their dating experiences in both domains, particularly in a way to compensate for these perceived social deficits (Toma, 2022; Valkenburg & Peter, 2011) within a religious setting where marriage is emphasized (Wright, Wilson et al., 2024). Indeed, those religious college students who are already suffering from a perceived deficit in the psychological basic needs of autonomy, competence, or relatedness may be adopting dating apps use as a solution, which is congruent

with self­determination theory (Deci & Ryan, 2012). Thus, dating apps users may be trying to address these needs much the same as motivations for social media use (Sheldon & Titova, 2023).

For instance, in the social context of a religious college where one may feel a lack of control, a poor selfconcept, or an inability to connect with others in person, dating apps may provide an appealing outlet to explore and compensate for these deficiencies. However, similar to social media in general (Fardouly et al., 2015; Yang, 2016), dating apps may be seen as a tantalizing option for social remedy, but ultimately not provide as many benefits to the user as originally supposed. This may be further compounded by recent COVID­19 pandemic restrictions, which influenced many to devote more time to technology at the possible expense of in­person social interactions and associated benefits (Gao et al., 2020; Marinucci et al., 2022; Wright et al., 2022). Thus, religious college students drawn to dating apps may have preexisting dating and psychological well­being deficits and, although dating apps could help some, most may be unable to meet their psychological needs.

Emodiversity and Psychosocial Well-Being

Second, and related to the first, dating apps users generally reported poorer emotional and psychosocial well­being than nonusers. This is in line with studies that have demonstrated that increased social media use is associated with poorer emotional experiences (e.g., Huang et al., 2022; Seabrook et al., 2016). This is also consistent with findings that those who encounter poor in­person dating experiences have similar poor associated health (Wright, Wilson et al., 2024) and may, in turn, seek out other methods of dating (e.g., dating apps). However, the emotional diversity findings were mixed, with positive and emodiversity not being related to dating apps use, and global emodiversity being higher among users. Although some studies suggest that emodiversity in all three domains (i.e., positive, negative, global) are associated with improved well­being (e.g., Quoidbach et al., 2014), our findings suggest that global emodiversity may not always be positively associated with health and well­being among young adults who are single and using dating apps. Other studies have shown and postulated that important and influential demographic characteristics may influence the relationship of emodiversity with health and well­being (e.g., UrbanWojcik et al., 2022). Our findings suggest that global emodiversity among religious college students who use dating apps may be an indicator of poor well­being, contrary to other findings (Quoidbach et al., 2014). Indeed, dating apps users may be experiencing a wider range of emotions and this may be an indicator of poorer health for religious college students where the dominant focus may Dating Apps and Well-Being

Johnson,

be on keeping a positive mood all the time such that any negative mood may be viewed as undesirable.

Building on this further, dating apps use within religious contexts may foster unrealistic expectations that are linked to psychosocial well­being deficits. For instance, increased social media use, especially those primarily visually­oriented, is associated with unrealistic expectations, social comparisons, and often connected with poorer well­being (Fardouly et al., 2015; Wright et al., 2020; Wright et al., 2021; Yang, 2016). Furthermore, dating apps may be used akin to online shopping, where users scroll through profiles, which often contain inaccurate representations, until they find something that is appealing (Bandinelli & Gandini, 2022; Hobbs et al., 2017; Minina et al., 2022). Thus, this capability may elicit psychological expectations that are too high or idealistic for any person to meet in­person. Moreover, similar to online shopping, users of dating apps may begin to view profiles as commodities to be selected, traded, or even upgraded and project these thoughts onto dating users through swiping behavior (Alexopoulos et al., 2020; Holtzhausen et al., 2020), the dating process (Bandinelli & Gandini, 2022), and onto themselves (Coduto et al., 2020). In other words, dating apps may encourage users to view and possibly treat other people more like objects, which may directly contradict key religious values and explain the observed well­being deficits.

Although this type of objectification occurs within the typical in ­ person dating milieu (Fredrickson & Roberts, 1997; Pecini et al., 2023), the online format of dating apps may accentuate these tendencies (Filice et al., 2022). Moreover, increased use of dating apps may provide additional opportunities for negative online behavior (e.g., cyberbullying) to occur in addition to the displacement of other in­person and physical activities, which may decrease well­being for the user (Seabrook et al., 2016; Wright, Espinosa et al., 2024). Thus, higher depressive symptoms and loneliness, as well as lower body appreciation, satisfaction with life, and social support may be linked to unrealistic expectations, social comparisons, and commodifying others fostered by dating apps use among the religious college student population.

Gender Differences

Third, there were noteworthy differences between dating apps users and nonusers observed within gender. For instance, although the dating experience was similar between users and nonusers within men and women, only men users reported a lack in the SDT construct of competence. This may be tied to perceived dominant gender roles (Paynter & Leaper, 2016; Wright, Wilson et al., 2024) where men are expected to ask women on dates and, if they are not confident in their in­person

social abilities, they may turn to dating apps as another medium (Toma, 2022; Valkenburg & Peter, 2011). These more “traditional” gender roles may also explain why only men users reported lower satisfaction with life, as they may feel they have failed in this traditional aspect of dating (Wright, Wilson et al., 2024), hence adopting dating apps as a way to cope. Interestingly, only women comparisons yielded a difference in negative affect, suggesting that women may turn to dating apps when confronted with more negative emotions. Equally plausible, women may develop more negative emotion upon use of dating apps, which is consistent with other social media literature (e.g., Song et al., 2014; Tromholt, 2016). Moreover, similar mechanisms may be in operation for the observed differences in subjective health, body appreciation, and anxiety, which only manifested among the women comparisons. Although difficult to determine directionality, dating apps users clearly exhibited poorer psychological, emotional, and psychosocial well­being than nonusers, especially among women within the religious college student context.

Alternative Explanations

Finally, certain alternative explanations should be considered. For instance, our findings highlight that those college students who use dating apps are older and not enrolled in as many credits. Conventional wisdom may suggest that, as college students get older, they may be working more, making social media and dating apps more attractive to facilitate dating (Bryant & Sheldon, 2017) while also providing entertainment. Indeed, the entertainment motive for using any form of social media is often associated with poorer health outcomes (Ewing et al., 2023) and may offer an alternative explanation to these findings. However, the literature has identified that younger religious emerging adults use social media more (e.g., Wright et al., 2020), which is counter to our findings and underscores the importance of the very small effect sizes. Daily time spent on social media offers more insight, as the literature has clearly identified a relationship between social media time and poorer health outcomes (Seabrook et al., 2016; Wright et al., 2021). Moreover, well­being deficits among apps users should not be surprising when considering differential influences of social media platforms themselves (Wright et al., 2020, 2021). Finally, our results suggest that prior relationship history and number of close friends (peer social support) were likely not responsible for the observations of poorer psychological, emotional, and psychosocial well­being for dating apps users.

Potential Limitations and Future Research

The current study has some potential limitations. First,

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the cross­sectional study design prevents any causal conclusions, particularly since the data were collected simultaneously. Second, these data are all self­reported with likely subjective bias that could influence the results, especially if participants ascertained study purposes. Third, measurement methodology may pose some challenges. For instance, a potential measurement issue is the simple dichotomous measure of dating apps use with some vague wording. This measure did not parcel out potential unique effects of individual dating apps (Wright et al., 202; 2021) nor did it specify current versus prior use of dating apps, which may have created some bias in the data.

In a similar vein, the current study did not examine familiarity with or duration of dating apps use, which could potentially moderate results. Moreover, measurement of some variables may have limited ability to represent theoretical constructs such as constraining the assessment of emodiversity to only 18 emotional states (Brown & Coyne, 2017) or asking about the SDT constructs in a general fashion rather than a specific context (e.g., dating). Similarly, some internal consistency estimates were below conventional standards (e.g., autonomy, competence), which may limit confidence in the measures. However, most of these coefficients suggested that our scales were internally consistent. Fourth, our statistical approach of multiple t tests raises the risk of Type I error, though our Bonferroni corrections mitigates this concern. Lastly, there is the possibility that this sample of members of the Church of Jesus Christ of Latter­day Saints may not be representative of college students within all religious sects. Sample and cohort characteristics may pose generalizability difficulties for other groups such as those of different religious affiliation, lower socioeconomic status, different cultural perspectives, or older adults.

Despite these potential limitations, findings from this study offer direction for future research. First, although it may be plausible that the use of dating apps causes poor psychological, emotional and psychosocial well­being, the reverse cannot be ruled out, such that those who have poorer well­being may be drawn to dating apps. Moreover, another variable such as the influence of the COVID­19 pandemic restrictions or increased screen time may be causing both. Thus, future research examining dating apps use over time could address this issue while controlling for several other potentially confounding variables (e.g., differential social media platform influences, socioeconomic status). Second, future studies could exclusively examine those who are married that met through a dating app and investigate longer­term associations with psychological, emotional and psychosocial well­being to determine whether poorer well­being’s association with dating apps is transient or

lasting. Third, examination within other college settings (e.g., online students) could also provide further interesting insights into how well these findings generalize through cross­validation of the current study results. Finally, sophisticated methodological designs could investigate the concepts of psychological basic needs and emodiversity in greater depth among dating apps users by exploring causality, controlling for shared variance between variables, specific dating apps (Her & Timmermans, 2021), dyadic perspectives using behavioral observations (Galliher et al., 2008), more objective outcomes (e.g., dopamine release), or other influential conditions.

Conclusion

In conclusion, the current study findings suggest that those who use dating apps for dating purposes are more likely to report having unsatisfactory dating experiences, psychological basic needs deficits, and poorer emotional and psychosocial well­being compared to those who do not use dating apps. Although the causal direction of these findings is debatable largely due to method characteristics, the differences ranged from small to medium in size, suggesting nontrivial associations. Moreover, some differences emerged in these comparisons according to gender, suggesting that there may be more subtle interactions between these variables, particularly when considering psychological basic needs and emodiversity. Thus, this study offers an empirical glimpse into a complex and dynamic psychological and emotional process underlying dating and technology use among emerging adults in a religious college setting.

References

Alexopoulos, C., Timmermans, E., & McNallie, J. (2020). Swiping more, committing less: Unraveling the links among dating app use, dating app success, and intention to commit infidelity. Computers in Human Behavior, 102, 172–180. https://doi.org/10.1016/j.chb.2019.08.009

Anderson, M., Vogels, E. A., & Turner, E. (2020). The virtues and downsides of online dating. Pew Research Center. https://www.pewresearch.org/ internet/2020/02/06/the-virtues-and-downsides-of-online-dating/ Avalos, L., Tylka, T. L., & Wood-Barcalow, N. (2005). The Body Appreciation Scale: Development and psychometric evaluation. Body Image, 2(3), 285–297. https://doi.org/10.1016/j.bodyim.2005.06.002

Azzahro, F., Hidayanto, A. N., Maulida, R. M., Zhu, Y. Q., & Sandhyaduhita, P. I. (2018). Exploring the influential factors in continuance usage of online dating apps: Gratification, subjective well-being and self-disclosure. https://www.researchgate.net/publication/325527572_Exploring_the_ Influential_Factors_in_Continuance_Usage_of_Online_Dating_Apps_ Gratification_Subjective_Well-Being_and_Self-Disclosure

Bandinelli, C., & Gandini, A. (2022). Dating apps: The uncertainty of marketised love. Cultural Sociology, 16(3), 423–441. https://doi.org/10.1177/17499755211051559

Bartkowski, J. P., Xu, X., & Fondren, K. M. (2011). Faith, family, and teen dating: Examining the effects of personal and household religiosity on adolescent romantic relationships. Review of Religious Research, 248–265. https://www.researchgate.net/publication/259906499_Faith_family_ and_teen_dating_Examining_the_effects_of_personal_and_ household_religiosity_on_adolescent_romantic_relationships

Benson, L., Ram, N., Almeida, D. M., Zautra, A. J., & Ong, A. D. (2018). Fusing biodiversity metrics into investigations of daily life: Illustrations and recommendations with emodiversity. The Journals of Gerontology: Series B,

Johnson, Wright, Jones, Batman, Whitney, Miyasaki, and Aho | Dating Apps and Well-Being

73(1), 75–86. https://doi.org/10.1093/geronb/gbx025

Bohannon, R. W., Maljanian, R., & Goethe, J. (2003). Screening for depression in clinical practice: Reliability and validity of a five-item subset of the CESdepression. Perceptual and Motor Skills, 97(3), 855–861. https://doi.org/10.2466/pms.2003.97.3.855

Bonilla-Zorita, G., Griffiths, M. D., & Kuss, D. J. (2021). Online dating and problematic use: A systematic review. International Journal of Mental Health and Addiction, 19, 2245–2278. https://doi.org/10.1007/s11469-020-00318-9

Bonilla-Zorita, G., Griffiths, M. D., & Kuss, D. J. (2023). Dating app use and wellbeing: An application-based pilot study employing ecological momentary assessment and objective measures of use. International Journal of Environmental Research and Public Health, 20(9), 5631. https://doi.org/10.3390/ijerph20095631

Brown, N. J., & Coyne, J. C. (2017). Emodiversity: Robust predictor of outcomes or statistical artifact? Journal of Experimental Psychology: General, 146(9), 1372. https://doi.org/10.1037/xge0000330

Brunell, A. B., & Webster, G. D. (2013). Self-determination and sexual experience in dating relationships. Personality and Social Psychology Bulletin, 39(7), 970–987. https://doi.org/10.1177/0146167213485442

Bryant, K., & Sheldon, P. (2017). Cyber dating in the age of mobile apps: Understanding motives, attitudes, and characteristics of users. American Communication Journal, 19(2), 1–15. https://www.csus.edu/faculty/m/fred.molitor/docs/cyber-dating.pdf

Butz, D. A., & Yogeeswaran, K. (2011). A new threat in the air: Macroeconomic threat increases prejudice against Asian Americans. Journal of Experimental Social Psychology, 47(1), 22–27. https://doi.org/10.1016/j.jesp.2010.07.014

Centers for Disease Control and Prevention. (2023). National marriage and divorce rate trends for 2000-2021. https://www.cdc.gov/nchs/fastats/marriage-divorce.htm

Coduto, K. D., Lee-Won, R. J., & Baek, Y. M. (2020). Swiping for trouble: Problematic dating application use among psychosocially distraught individuals and the paths to negative outcomes. Journal of Social and Personal Relationships, 37(1), 212–232. https://doi.org/10.1177/0265407519861153

Cohen, S., & Hoberman, H. M. (1983). Positive events and social supports as buffers of life change stress. Journal of Applied Social Psychology, 13(2), 99–125. https://doi.org/10.1111/j.1559-1816.1983.tb02325.x

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385. https://doi.org/10.2307/2136404

Castro, Á., & Barrada, J. R. (2020). Dating apps and their sociodemographic and psychosocial correlates: A systematic review. International Journal of Environmental Research and Public Health, 17(18), 6500. https://doi.org/10.3390/ijerph17186500

Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11, 227–268. https://doi.org/10.1207/S15327965PLI1104_01

Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. Handbook of Theories of Social Psychology, 1(20), 416–436. https://doi.org/10.4135/9781446249215.n21

Deci, E. L., Ryan, R. M., Gagné, M., Leone, D. R., Usunov, J., & Kornazheva, B. P. (2001). Need satisfaction, motivation, and well-being in the work organizations of a former eastern bloc country: A cross-cultural study of selfdetermination. Personality and Social Psychology Bulletin, 27(8), 930–942. https://doi.org/10.1177/0146167201278002

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction With Life Scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13

Enstrom, J. E., & Breslow, L. (2008). Lifestyle and reduced mortality among active California Mormons, 1980–2004. Preventive Medicine, 46(2), 133–136. https://doi.org/10.1016/j.ypmed.2007.07.030

Ewing, C. R., Nienstedt, C., Wright, R. R., & Chambers, S. (2023). Social media use motives: An influential factor in user behavior and user health profiles. Psi Chi Journal of Psychological Research, 28(4), 275–286. https://doi.org/10.24839/2325-7342.JN28.4.275

Fardouly, J., Diedrichs, P. C., Vartanian, L. R., & Halliwell, E. (2015). Social comparisons on social media: The impact of Facebook on young women’s body image concerns and mood. Body Image, 13 38–45. https://doi.org/10.1016/j.bodyim.2014.12.002

Filice, E., Abeywickrama, K. D., Parry, D. C., & Johnson, C. W. (2022). Sexual violence and abuse in online dating: A scoping review. Aggression and violent behavior, 67(2) 101781. https://doi.org/10.1016/j.avb.2022.101781

Fredrickson, B. L., & Roberts, T. A. (1997). Objectification theory: Toward understanding women’s lived experiences and mental health risks. Psychology of Women Quarterly, 21(2), 173–206. https://doi.org/10.1111/j.1471-6402.1997.tb00108.x

Galliher, R. V., Enno, A. M., & Wright, R.R. (2008). Convergence and divergence among multiple methods for assessing adolescent romantic relationships. Journal of Adolescence, 31(6), 747–769. https://doi.org/10.1016/j.adolescence.2007.12.003

Gao, J., Zheng, P., Jia, Y., Chen, H., Mao, Y., Chen, S., Wang, Y., Fu, H., & Dai, J. (2020). Mental health problems and social media exposure during COVID-19 outbreak. PLOS One, 15(4), e0231924. https://doi.org/10.1371/journal.pone.0231924

Grossmann, I., Oakes, H., & Santos, H. C. (2019). Wise reasoning benefits from emodiversity, irrespective of emotional intensity. Journal of Experimental Psychology: General, 148(5), 805–823. https://doi.org/10.1037/xge0000543

Hanson, K. R. (2021). Becoming a (gendered) dating app user: An analysis of how heterosexual college students navigate deception and interactional ambiguity on dating apps. Sexuality & Culture, 25(1), 75–92. https://doi.org/10.1007/s12119-020-09758-w

Her, Y. C., & Timmermans, E. (2021). Tinder blue, mental flu? Exploring the associations between Tinder use and well-being. Information, Communication & Society, 24(9), 1303–1319. https://doi.org/10.1080/1369118X.2020.1764606

Hobbs, M., Owen, S., & Gerber, L. (2017). Liquid love? Dating apps, sex, relationships and the digital transformation of intimacy. Journal of Sociology, 53(2), 271–284. https://doi.org/10.1177/1440783316662718

Holtzhausen, N., Fitzgerald, K., Thakur, I., Ashley, J., Rolfe, M., & Pit, S. W. (2020). Swipe-based dating applications use and its association with mental health outcomes: A cross-sectional study. BMC Psychology, 8(1), 1–12. https://doi.org/10.1186/s40359-020-0373-1

Huang, C. (2022). A meta-analysis of the problematic social media use and mental health. International Journal of Social Psychiatry, 68(1), 12–33. https://doi.org/10.1177/0020764020978434

Hughes, M. E., Waite, L. J., Hawkley, L. C., & Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys. Research on Aging, 26(6), 655–672. https://doi.org/10.1177/0164027504268574

Kind, P., Brooks, R., & Rabin, R. (2005). EQ-5D concepts and methods: A developmental history. Springer. https://doi.org/10.1007/1-4020-3712-0

Knee, C. R., Hadden, B. W., Porter, B., & Rodriguez, L. M. (2013). Selfdetermination theory and romantic relationship processes. Personality and Social Psychology Review, 17(4), 307–324. https://doi.org/10.1177/1088868313498000

Lever, J., Frederick, D. A., & Hertz, R. (2015). Who pays for dates? Following versus challenging gender norms. Sage Open, 5(4), 2158244015613107. https://doi.org/10.1177/2158244015613107

Liu, Y., Bangerter, L. R., Rovine, M. J., Zarit, S. H., & Almeida, D. M. (2016). Intrinsic emotional fluctuation in daily negative affect across adulthood. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 73(1), 100–112. https://doi.org/10.1093/geronb/gbw159

Magurran, A. E. (2004). Measuring biological diversity. Blackwell Science Ltd. https://doi.org/10.2307/4126959

Marinucci, M., Pancani, L., Aureli, N., & Riva, P. (2022). Online social connections as surrogates of face-to-face interactions: A longitudinal study under covid-19 isolation. Computers in Human Behavior, 128, 107102. https://doi.org/10.1016/j.chb.2021.107102

Mauss, I. B., Tamir, M., Anderson, C. L., & Savino, N. S. (2011). Can seeking happiness make people unhappy? Paradoxical effects of valuing happiness. Emotion, 11(4), 807–815. https://doi.org/10.1037/a0022010

McDonald, D. L., & McKinney, J. P. (1994). Dating Value Questionnaire [Database record]. https://dx.doi.org/10.1037/t26269-000

Miller, L. M., & Prior, D. D. (2010). Online social networks and friending behaviour: A self-determination theory perspective. In Proceedings of the Australia and New Zealand Marketing Academy Annual Conference, Christchurch, New Zealand (pp. 1-9). https://anzmac2010.org/proceedings/pdf/anzmac10Final00230.pdf

Minina, A., Masè, S., & Smith, J. (2022). Commodifying love: Value conflict in online dating. Journal of Marketing Management, 38(1–2), 98–126. https://doi.org/10.1080/0267257X.2022.2033815

Navarro, R., Larrañaga, E., Yubero, S., & Víllora, B. (2020). Psychological correlates of ghosting and breadcrumbing experiences: A preliminary study among adults. International Journal of Environmental Research and Public Health, 17(3), 1116. https://doi.org/10.3390/ijerph17031116

Ng, J. Y., Ntoumanis, N., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R. M., Duda, J. L., & Williams, G. C. (2012). Self-determination theory applied to health

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contexts: A meta-analysis. Perspectives on Psychological Science, 7(4), 325–340. https://doi.org/10.1177/1745691612447309

Nienstedt, C., Smith, N., Braithwaite, H., Gilbert, B., & Wright, R. R. (2023). Swiping away your well-being? Examining well-being indicators among TikTok account holders. Psi Chi Journal of Psychological Research, 28(2), 96–106. https://doi.org/10.24839/2325-7342.JN28.2.96

Ong, A. D., Benson, L., Zautra, A. J., & Ram, N. (2018). Emodiversity and biomarkers of inflammation. Emotion, 18(1), 3–14. https://doi.org/10.1037/emo0000343

Paynter, A., & Leaper, C. (2016). Heterosexual dating double standards in undergraduate women and men. Sex Roles, 75(1), 393–406. https://doi.org/10.1007/s11199-016-0628-8

Pecini, C., Ruzzante, D., Valtorta, R. R., Crapolicchio, E., Cocco, V. M., Andrighetto, L., & Di Bernardo, G. A. (2023). Why might women justify dating violence? The role of men’s sexual objectification of their romantic partners within heterosexual relationships. Journal of Interpersonal Violence, 38(19–20), 10664–10685. https://doi.org/10.1177/08862605231175515

Pew Research Center. (2023). Religious landscape study: Religious composition of college graduates https://www.pewresearch.org/religion/religiouslandscape-study/educational-distribution/college/

Philippot, P., Schaefer, A., & Herbette, G. (2003). Consequences of specific processing of emotional information: Impact of general versus specific autobiographical memory priming on emotion elicitation. Emotion, 3(3), 270–283. https://doi.org/10.1037/1528-3542.3.3.270

Potarca, G. (2020). The demography of swiping right. an overview of couples who met through dating apps in Switzerland. PLOS ONE, 15(12). https://doi.org/10.1371/journal.pone.0243733

Quoidbach, J., Gruber, J., Mikolajczak, M., Kogan, A., Kotsou, I., & Norton, M. I. (2014). Emodiversity and the emotional ecosystem. Journal of Experimental Psychology: General, 143(6), 2057–2066. https://doi.org/10.1037/a0038025

Richardson, M., Cannon, S., Teichert, L., Vance, A., Kramer, I., Barter, M., King, J., & Callahan, C. (2020). Religion-focused dating apps: A Q methodology study on the uses of Mutual. Telematics and Informatics, 55, 101448. https://doi.org/10.1016/j.tele.2020.101448

Ryan, R. M., & Deci, E. L. (2002). Overview of self-determination theory: An organismic dialectical perspective. Handbook of Self-Determination Research, 2, 3–33. https://www.elaborer.org/cours/A16/lectures/Ryan2004.pdf

Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016). Social networking sites, depression, and anxiety: A systematic review. JMIR Mental Health, 3(4), e50. https://mental.jmir.org/2016/4/e50/

Shannon, C. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Shapiro, G., Tatar, O., Sutton, A., Fisher, W., Naz, A., Perez, S., & Rosberger, Z. (2017). Correlates of Tinder use and risky sexual behaviors in young adults. Cyberpsychology, Behavior, and Social Networking. 20(12), 727–734. https://doi.org/10.1089/cyber.2017.0279

Sheldon, K. M., & Titova, L. (2023). Social media use and well-being: Testing an integrated self-determination theory model. Media Psychology, 26(6), 1–23. https://doi.org/10.1080/15213269.2023.2185259

Song, H., Zmyslinski-Seelig, A., Kim, J., Drent, A., Victor, A., Omori, K., & Allen, M. (2014). Does Facebook make you lonely? A meta analysis. Computers in Human Behavior, 36, 446–452. https://doi.org/10.1016/j.chb.2014.04.011

Statista. (2024) Online Dating – Worldwide. Statista Market Forecast [online]. https://www.statista.com/outlook/372/100/online-dating/worldwide

Sumter, S. R., & Vandenbosch, L. (2019). Dating gone mobile: Demographic and personality-based correlates of using smartphone-based dating applications among emerging adults. New Media & Society, 21(3), 655–673. https://doi.org/10.1177/1461444818804773

Sumter, S. R., Vandenbosch, L., & Ligtenberg, L. (2017). Love me tinder: Untangling emerging adults’ motivations for using the dating Application Tinder. Telematics and Informatics, 34(1), 67–78. https://doi.org/10.1016/j.tele.2016.04.009

Toma, C. L. (2022). Online dating and psychological wellbeing: A social compensation perspective. Current Opinion in Psychology, 46, 101331 https://doi.org/10.1016/j.copsyc.2022.101331

Toma, C. L., Hancock, J. T., & Ellison, N. B. (2008). Separating fact from fiction: An examination of deceptive self-presentation in online dating profiles. Personality and Social Psychology Bulletin, 34(8), 1023–1036. https://doi.org/10.1177/0146167208318067

Tromholt, M. (2016). The Facebook Experiment: Quitting Facebook leads to higher levels of well-being. Cyberpsychology, Behavior, and Social Networking,19(11), 661–666. https://doi.org/10.1089/cyber.2016.0259

Urban-Wojcik, E. J., Mumford, J. A., Almeida, D. M., Lachman, M. E., Ryff, C. D.,

Davidson, R. J., & Schaefer, S. M. (2022). Emodiversity, health, and wellbeing in the midlife in the United States (MIDUS) daily diary study. Emotion, 22(4), 603–615. https://doi.org/10.1037/emo0000753

U.S. Surgeon General. (2023). Our epidemic of loneliness and isolation: The U.S. Surgeon General’s advisory on the healing effects of social connection and community https://www.hhs.gov/sites/default/files/surgeon-generalsocial-connection-advisory.pdf

Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121–127. https://doi.org/10.1016/j.jadohealth.2010.08.020

Vannier, S. A., & O’Sullivan, L. F. (2017). Passion, connection, and destiny: How romantic expectations help predict satisfaction and commitment in young adults’ dating relationships. Journal of Social and Personal Relationships, 34(2), 235–257. https://doi.org/10.1177/0265407516631156

Vannier, S. A., & O’Sullivan, L. F. (2018). Great expectations: Examining unmet romantic expectations and dating relationship outcomes using an investment model framework. Journal of Social and Personal Relationships, 35(8), 1045–1066. https://doi.org/10.1177/0265407517703492

Vogels, E.A., & McClain, C. (2023). Key findings about online dating in the U.S. https://www.pewresearch.org/short-reads/2023/02/02/key-findingsabout-online-dating-in-the-u-s/

Weitbrecht, E. M., & Whitton, S. W. (2020). College students’ motivations for “hooking up”: Similarities and differences in motives by gender and partner type. Couple and Family Psychology: Research and Practice, 9(3), 123–143. https://doi.org/10.1037/cfp0000138

Whitehead, B. D., & Popenoe, D. (2000). Sex without strings, relationships without rings. The state of our unions, 2000 (pp. 6–20). The National Marriage Project, Rutgers University Press.

Wright, R. R., Evans, A., Schaeffer, C., Mullins, R., & Cast, L. (2021). Social networking site use: Implications for health and wellness. Psi Chi Journal of Psychological Research, 26(2), 165–175. https://doi.org/10.24839/2325-7342.JN26.2.165

Wright, R. R., Larson, J., Richards, S., Hoffmann, S., & Nienstedt, C. (2022). The COVID-19 pandemic: Electronic media use and health among U.S. college students. Journal of American College Health, 1–16. https://doi.org/10.1080/07448481.2022.2155463

Wright, R. R., Schaeffer, C., Mullins, R., Evans, A., & Cast, L. (2020). Comparison of student health and wellbeing profiles and social media use. Psi Chi Journal of Psychological Research, 25(1), 14–21. https://doi.org/10.24839/2325-7342.JN25.1.14

Wright, R. R., Espinosa, A. A. R., Adair, M. E., & Marrott, D. (2024). Sticks and stones: Cyberbullying victimization, instigation and associated health among online college students. Journal of Technology in Behavioral Science, 9, 784–796. https://doi.org/10.1007/s41347-024-00395-w

Wright, R. R., Wilson, M., Nienstedt, C., Ewing, C., Rodriguez, A., Anderson, C., Johnson, N., & Johnson, L. (2024). Quality dating and wellness among a religious college student population: A mixed-methods approach. Psi Chi Journal of Psychological Research, 29(3), 213–226. https://doi.org/10.24839/2325-7342.JN29.3.213

Yang, C. (2016). Instagram use, loneliness, and social comparison orientation: Interact and browse on social media, but don’t compare. Cyberpsychology, Behavior, and Social Networking, 19(12), 703–708. https://doi.org/10.1089/cyber.2016.0201

Author Note

Robert R Wright https://orcid.org/0000­0002­4101­7840

This research was supported by internal funding from Brigham Young University–Idaho for student­ and faculty­directed research. The data used to support the findings of the current study are available from the corresponding author, upon reasonable request. We would like to thank (in alphabetical order) Samuel Clay, Paizly Diep, and Marie Vincent for their assistance throughout the conduction of this study.

The authors declare no conflict of interest. The data that we used for the current study are available from the second author, upon reasonable request.

Correspondence concerning this article should be addressed to Robert R. Wright, Department of Psychology, Brigham Young University–Idaho, 210 West 4th South Rexburg, ID 83460­2140. Telephone: 208­496­4085. E­mail: wrightro@byui.edu

State of the First Semester Freshman: Health and Wellness Through the COVID-19 Pandemic, Years 2018–2023

Robert R. Wright*, Skyler Brough, Joshua Castro, McKenna Osborne, Lindsay Johnson, and Spencer Johnson

Department of Psychology, Brigham Young University–Idaho

ABSTRACT. First semester freshmen (FSF) are a specific group at risk for developing deleterious wellness outcomes including physical, mental, emotional, and social health as well as corresponding poor health behaviors. This is particularly concerning within the COVID­19 pandemic, which exerted formidable pressures on beginning, continuing, and completing educational pursuits. The current study examined differences before, during, and after COVID­19 pandemic restrictions among FSF in demographics, health behavior, and health (i.e., physical, mental, emotional, social) as well as comparisons to students who are beyond their first semester or non­first semester freshmen (NFSF) during the same time period. University students (n = 2,500) from a large Intermountain West university participated by completing an online self­report questionnaire as part of a multiple cross­sectional research design spanning the years 2018–2023 in 3 time periods (Pre­COVID, COVID, Post­COVID). Results showed that FSF have returned to prepandemic levels for many metrics, but remain in poorer health conditions, generally, compared to NFSF at postpandemic. For example, FSF reported more physical health symptoms and consistently used social media more than NFSF across all 3 time points. These group differences suggest that the COVID­19 pandemic might have impacted FSF more strongly than NFSF and highlight this population as vulnerable to dramatic social changes that might have implications for college enrollment and retention. Recommendations to aid FSF are given, including proactive supportive practices that universities can implement before another pandemic arises.

Keywords: first­year students, COVID­19, health, wellness, retention

First­year undergraduate students represent an important and integral part of the university system. They provide important revenue, revitalize programs of study, and offer substantial benefits to society at large through the future benefits of their educational and vocational contributions (Veenstra, 2009). However, to successfully complete their years of higher education, first­year college students must actively attend to their own health and wellness, often requiring perseverance and resiliency in the face of environmental and personal health challenges. It is estimated that, within the United States, for instance, nearly 30% of college first­year students drop out before their sophomore year and nearly $16.5 billion is lost in tuition revenue from student attrition, which does

not include other loss of revenue or opportunity cost (e.g., future alumni contributions, attendance, other fees; Bustamante, 2024). This places great importance on understanding the typical first semester freshman’s (FSF) early experiences (Stephenson et al., 2022), including their health and wellness. Although many challenges confront FSF (Tinto, 2006), health and wellness represent a domain that can be improved through both individual and institutional means. However, FSF health and wellness is subject to change over time according to maturation and the sociopolitical context. One recent societal change is the COVID­19 pandemic, as this pandemic has had noteworthy impacts for undergraduate students, including demographics, mental health, physical health, and technology use

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(Birmingham et al., 2021; Dixon et al., 2023; Huckins et al., 2020; Wright et al., 2022). However, the literature has yet to uncover how FSF might have changed in these key aspects from before and after the imposed pandemic restrictions, which may provide vital clues for aiding current and future FSF. Moreover, exploring differences between FSF and non­first semester freshmen (NFSF) or students who are beyond their first semester (e.g., sophomores) could yield additional clues for resiliency. As such, the current study sought to elucidate the typical FSF health and wellness condition, explore differences with NFSF, and do this across three cohorts: before, during, and after the pandemic restrictions were imposed during March 2020.

First-Year Student Demographics, Behaviors, Health, and Wellness

Historically, first ­ year college students are younger (18–19 years), of mostly White ethnicity, comprised of more women than men, have a higher socioeconomic background (SES), and ~82% of them are considered full ­ time students (Hanson, 2024). Although these statistics have changed somewhat over the years, this has generally been consistent up when to the COVID­19 pandemic struck (Hanson, 2024). Moreover, first­year students struggle with several health behaviors such as substance use (e.g., alcohol, smoking), sexual activity, exercise, diet, and sleep (Sax, 1997), particularly when compared with national recommendations (Racette et al., 2008; Wright et al., 2016). Several studies have shown that health behaviors (e.g., exercise, diet, sleep) are associated with important student outcomes such as academic performance (Reuter, 2021; Ruthig et al., 2011), suggesting that these behaviors can influence first­year student outcomes. Moreover, first­year student attitudes toward health behaviors such as diet are often poor, creating formidable barriers (Wright et al., 2023), and health behavioral patterns during the first year of college often have long­lasting impacts for behaviors like physical activity (Wilson et al., 2022) and outcomes like weight gain (Kasparek et al., 2008; Racette et al., 2008) or increased body fat and waist circumference (Gropper et al., 2009).

Other health challenges often emerge, as many studies have noted that college students, particularly first­year students, deal with high levels of mental health challenges (e.g., stress, depression, anxiety, suicidality; Acharya et al., 2018; Liu et al., 2019). Compounding this further, first­year students often have a skewed sense of perception regarding their own health, as they often rate their health and wellness higher than some objective standards would indicate. For instance, one study (Wright et al., 2018) of primarily first­year college students that self­reported their body mass index (BMI)

found that students underreported their BMI, reflecting a tendency to perceive their own health in optimistic ways that may distort reality, such as believing they are healthier than they are or denial/avoidance of potential health concerns. Moreover, the authors noted that mental wellness factors such as lower perceived body appreciation, poor subjective health evaluations, and neurotic tendencies all contributed to this difference, suggesting mental health may exacerbate first ­ year perceptions. Furthermore, first­year students often suffer from similar body image distortions that adolescents experience, particularly when using self­objectifying social media (Salomon & Brown, 2018).

Social interactions, both in­person and technologymediated, are an integral part of the first­year experience and inexorably tied to health and wellness (Bryan et al., 2013). For example, a recent mixed ­ methods study on in ­ person dating among predominately first ­ year students uncovered links between quality dating experiences and several health and wellness indicators, including loneliness, depressive symptoms, stress, social support, and life satisfaction (Wright et al., 2024). As quality dating experiences increased, these also improved, suggesting these social experiences were related to health and well­being. Using technology to establish and nurture social relationships has surfaced as another important aspect of emerging adulthood. Indeed, studies among college students have identified potent and robust relationships between poorer health outcomes and prolonged use of technology, including time spent watching TV (Spruance et al., 2017; Walton­Pattison et al., 2018), overall time spent on any screen (Neophytou et al., 2021; Twenge et al., 2018), and time using social media (Hunt et al., 2018; Wright, Schaeffer et al., 2020). Social media, in particular, has garnered a great deal of attention in the literature as motives for social media use (Ewing et al., 2023) and specific social media platform (Nienstedt et al., 2023; Wright, Schaeffer et al., 2020) have been linked to health concerns. All this supports the social context (e.g., in­person, technology­mediated) as especially important for first­year college students.

The Sociopolitical Influence of the COVID-19 Pandemic

As the COVID­19 pandemic unfolded across the nation, first­year college students were more vulnerable to the adverse effects of social distancing and restrictions. Recent statistics suggested that demographics might have changed profoundly among college students during COVID ­ 19 restrictions and perhaps afterwards, as nontraditional and minority students enrolled in greater numbers (Hamilton, 2024). Moreover, several studies have identified problematic trends and changes for

general college student health and wellness from before to during the COVID­19 pandemic (following March 2020). Health behaviors such as sleep, physical activity, and diet all digressed (Birmingham et al., 2021; Huckins et al., 2020; Kowalsky et al., 2021). Moreover, other troubling mental health problems emerged including rising rates of anxiety, depression, loneliness, and perceived stress (Dixon et al., 2023; Elharake et al., 2022; Frazier et al., 2023; Liu et al., 2022; Wang et al., 2020) coupled with lower levels of life satisfaction (Colby et al., 2023). One study identified the risk of clinical depression for first­year students to have increased by 41% following the pandemic restrictions (De Coninck et al., 2023). Another study, using longitudinal ecological momentary assessment, reported increased student anxiety and depression following the pandemic restrictions linked to their use of smartphones (Huckins et al., 2020).

Increased technology use following the imposed pandemic restrictions, especially social media, has been well­documented. A recent review (Vargo et al., 2021) noted that education became the second largest group of digital users during the pandemic, and another literature review (Sultana et al., 2021) found that increased time spent on digital screen media during the COVID­19 pandemic was related to a rise in noncommunicable disease (e.g., obesity, hypertension, depression, sleep disorders). Time spent viewing TV, social media, or any screen increased substantially after the onset of the pandemic among college students (Kowalsky et al., 2021; Wright et al., 2022). One recent study examined how these three technology use behaviors were related to various health and wellness variables both before and during the pandemic restrictions among college students (Wright et al., 2022). All three technology use behaviors increased significantly during the pandemic and were related to key health behaviors (e.g., diet, sleep, exercise) and other wellness indicators (e.g., interpersonal conflict, social integration, body appreciation, stress, satisfaction with life). As such, technology use behavior might have played a key role in health and wellness shifts through the past six years within college settings. Although all these studies have included college students in their samples, none have exclusively focused on FSF, a student group which may be most at­risk of detrimental fallout from the COVID­19 pandemic restrictions.

Research Questions

Relatively little is known about how FSF have fared during the past several years through the pandemic within the domains of health behavior; physical, mental, emotional, and social health; and technology use behavior. Moreover, few if any studies have examined how FSF may be fundamentally different from students who have continued

beyond the first semester (NFSF) during this same time period. As such, the current study addressed two primary research questions. Our first research question (RQ1) asked what differences have occurred for the typical FSF through the COVID­19 pandemic restrictions in terms of demographics, health behaviors, and well­being (physical, mental, emotional, social), and technology use variables? Examining differences between cohorts that were prior to, during, and following the COVID­19 pandemic restrictions may offer a glimpse into how FSF have responded to or been impacted by these restrictions. Our second research question (RQ2), was how have the differences between the typical FSF and the typical NFSF differed through the COVID­19 pandemic restrictions in terms of demographics, health behaviors, well­being (physical, mental, emotional, social), and technology use behaviors? Similarly, we anticipated there would be differences between FSF and NFSF within these three cohorts and determined that an examination of these differences over these time periods would provide insight into how these two student groups fared throughout the pandemic restrictions.

Method

Participants and Procedure

Participants were recruited from Introductory Psychology courses as a convenience sample. Potential student participants selected from among several research study options, of which the current study was one option, to participate and receive class credit. Upon receipt of approval from the research ethics board at Brigham Young University–Idaho, students were contacted via email that contained a link to the online questionnaire (via Qualtrics), assessing a range of variables including demographics, health behaviors, wellness outcomes, and technology use with psychometrically validated measures. All identifying information was kept strictly confidential. Data were collected over three time periods, resulting in three datasets with a total of 2,802 participants. Only those who gave consent for their data to be included in potential research publications, were above age 18, and indicated they provided accurate responses (i.e., attention check) were included in analyses, which resulted in a final sample of 2,500 for analyses. Time periods were designated as pre­COVID (before pandemic restrictions were in full effect; November 2019 to March 2020; n = 409), COVID (during pandemic restrictions; April 2020 to July 2021; n = 1,059), and post­COVID (following pandemic restrictions being eased/lifted; August 2021 to July 2023; n = 1,032) groups. Data were collected from those attending on­campus courses for all three periods. For the COVID cohort, data came from those

attending via remote synchronous access (e.g., Zoom) due to pandemic restrictions in order to maintain measurement equivalency, as these students would have

TABLE 1

Demographic Characteristics of All Participants Across Study Samples

Note. FSF = first semester freshman; SES socioeconomic status. Age, Credit Load, Homework Time, SES Family Education, and SES Family Income are reported in averages with standard deviations. Homework time is hours spent per week. SES Family Education represents the education level of parents and SES Family Income is family income per past 12 months, with higher values representing greater education and income, respectively. Many respondents did not know or declined to respond for SES Family Income (n = Pre-COVID 112, COVID 261, Post-COVID 276).

otherwise been enrolled in on­campus courses. FSF were those in their first semester of college education while NFSF were comprised of all students who were not in their first semester of college, including secondsemester first­year students, sophomores, juniors, and seniors. Completion of the online questionnaire took an average of 173.55 minutes (SD = 595.91), though a more accurate representation is the median (39.54 minutes) because participants could pause their questionnaire at any time. All other sample descriptive characteristics are presented in Table 1.

Measures

Demographic Constructs

The questionnaire queried a broad range of demographic information including age, gender, ethnicity, education level, relationship status, credit enrollment, and employment status. In addition to these, daily time spent on homework (as another measure of student behavior) was assessed with one question “On average, about how many hours do you spend on homework each week (time spent in your classes does not count)?”

Socioeconomic status (SES) was assessed using two questions. The first question asked about parental education with six categories of increasing education (1 = both mother and father have no college education, 6 = mother or father received advanced training, e.g., medical school, law school ). The second question, focused on household income for the past 12 months on a 7­point scale (1 = <$25,000, 7 = >$150,000). For both SES questions, decline to respond and do not know options were provided but were not included in analyses.

Health Behavior Variables

Daily frequency of consumption of fruit, vegetables, sugary snacks (e.g., brownies, donuts), drinks with added sugar (e.g., regular soda, sports drinks, coffee, iced tea, lemonade, or fruit punch) and fast food over the previous month were assessed using one item for each on a 10­point serving frequency scale (0 = never to 10 = 5 or more times a day; Wright et al., 2017). Sleep quantity was reported using one open­ended item for average hours per night of sleep over the past month. Sleep quality was measured using a single item (“During the past month, how would you rate your sleep quality overall?”) on a 5­point scale ranging from very good to very bad. To represent aerobic activity, we used the Stanford Patient Education Research Center’s (SPERC) 6­item exercise measure, which assessed typical weekly minutes of aerobic exercise (e.g., walk/run, swim, bike) in the past 30 days by taking an average score of the five aerobic exercise items (Lorig et al., 1996). As a measure of sedentary behavior, we used a 10­item adapted

Wright, Brough, Castro, Osborne, Johnson, and Johnson

version of the Sedentary Behavior Questionnaire (SBQ; Rosenberg et al., 2010) on a 9­point frequency scale (0 = none, 9 = 6 hours or more) in which participants indicated how much time, in general, they spend in a week on a variety of activities that involve little to no physical exertion (e.g., watching television, playing video games, sitting listening to music, sitting while reading a book, riding in a car, and doing homework). Given that all these behaviors were either assessed with single­item measures or behavior checklists, where the behaviors are not equivalent and not a variation on the same theme, it is inappropriate to examine internal consistency of these measures (Spector & Jex, 1998).

Physical Wellness Variables

Physical health included two variables: subjective overall physical health and physical symptoms. Subjective overall physical health was measured using a single item on a scale of 0 (worst physical health) to 100 (best physical health ). We measured common physical symptom complaints using a dichotomous 18­item scale (Spector & Jex, 1998), asking participants if they had experienced a variety of symptoms in the past 30 days such as fever or headache. We then created a sum score such that possible scores ranged from 0 to 18. As a behavioral checklist, internal consistency estimation was not appropriate, as explained above (Spector & Jex, 1998)

Mental Wellness Variables

Across all scaled measures where internal consistency is appropriate, Cronbach’s alphas (α) are reported for the pre­COVID, COVID, and post­COVID groups in that order. General life stress was captured using seven items from the Perceived Stress Scale (α = .86, .84, .82; Cohen et al., 1983) on the same 5 ­ point frequency scale (e.g., “In the past 3 months, how often have you found that you could not cope with all the things that you had to do?”). Anxiety during the last 3 months was captured using a 5­point frequency scale (1 = never to 5 = very often; α = .83, .83, .82) with the four items in the scale (Butz & Yogeeswaran, 2011). Life satisfaction was measured using the 5­item Satisfaction with Life Scale (α = .86, .87, .88; Diener et al., 1985) on a 5­point agreement scale (e.g., “In most ways, my life is close to my ideal”). Finally, body appreciation was examined on a 7­point scale (1 = not at all true, 7 = very true) using the 13­item Body Appreciation Scale (e.g., “I respect my body”; α = .95, .95, .94; Avalos et al., 2005).

Emotional Wellness Variables

Affect was assessed using an 8­item scale where participants indicated how much each of four positive mood adjectives (happy, enthusiastic, relaxed, alert; α = .69, .67, .62) and four negative mood adjectives (sad, irritable, bored, nervous;

α = .66, .66, .67) described their mood over the last 30 days (Wright et al., 2017). Although these internal consistency estimates were suboptimal, they are consistent with prior studies (Wright et al., 2017). Depressive symptomology was captured using the 5­item, CES­D frequency (α = .78, .77, .78) measure (e.g., “I could not ‘get going’”; Bohannon et al., 2003).

Social Wellness Variables

Perceived loneliness frequency during the past three months was assessed using a 3­item, 5­point loneliness scale (e.g., “How often do you feel that you lack companionship?” α = .84, .85, .83; Hughes et al., 2004). Interpersonal conflict (α = .85, .89, .87) was queried with a 6­item, 5­point frequency scale (Wright et al., 2017; e.g., “In the past 3 months, how often have you felt like you were treated unfairly by other people?”). In­person social interaction/integration during the past month was measured using a 6­item behavioral checklist, 4­point frequency scale (Twenge et al., 2018; e.g., “How often do you get together with friends informally?”).

Technology Use Variables

Daily time spent watching television (TV) was captured with three questions about time spent during the past month watching TV (i.e., watching television, watching videos/shows on the computer, playing video games) on a 9­point frequency scale (0 = none, 8 = 6 hours or more). Daily screen time was represented by taking the average number of minutes spent on screens on a typical day during the past month across four items (Wright et al., 2022) including time on a smartphone, tablet (or other small device with a screen), computer, and television. Daily time on social media during the past month was assessed by a single item where participants provided self­report estimates (in minutes) across all social media on a typical day.

Data Analysis

First, to address differences between first FSF at each time period (RQ1), we analyzed demographic variables, both categorical and continuous; the nine health behaviors; two physical, four mental, three emotional, and three social wellness variables; as well as the three technology use behaviors using independent­samples t tests and Cohen’s d main effects. Next, we used a similar analytic approach to examine differences between FSF and NFSF at each time period (RQ2). Finally, to protect against possible Type I error, which may be inflated by conducting multiple significance tests, we applied Bonferroni’s correction (which reduces the alpha level to be considered statistically significant) to the t tests within categories containing more than three separate analyses (i.e., health behaviors, mental health).

Results

Demographic information (e.g., gender, relationship status) for all three samples is listed in Table 1. Among all participants within all samples, first­year students were the largest educational group represented with FSF comprising large portions of the samples, but especially during the post ­ COVID period (54.1%). Gender proportions fluctuated somewhat with a low of 31.8% for men in the COVID group and a high of 39.4% in

TABLE 2

Demographic Characteristics of FSF Only Across Study Samples

Note. FSF = first semester freshman; SES = socioeconomic status. Age, Credit Load, Homework Time, SES Family Education, and SES Family Income are reported in averages with standard deviations. Homework time is hours spent per week. SES Family Education represents the education level of parents and SES Family Income is family income per past 12 months, with higher values representing greater education and income, respectively. Many respondents did not know or declined to respond for SES Family Income (n = Pre-COVID 51, COVID 100, Post-COVID 135).

the post­COVID group. Similarly, during the COVID period, ethnicity, relationship status, and employment status proportions differed, such that minority ethnic groups (Hispanic, Latinx), married, and full ­ time employment all were higher. All three of these categories were at pre­COVID levels at post­COVID.

Research Question 1

First, relative to RQ1 (differences in FSF between time cohorts), demographic differences among FSF­only were noteworthy across both categorical (see Table 2) and continuous variables (see Tables 3–5). Gender representation remained consistent across all three time points, and ethnicity consisted of mostly White with a notable increase in Hispanics (Latinx) during COVID. However, relationship and employment status fluctuated greatly as both married FSF and full­time FSF increased more than six times pre­COVID levels during the COVID period and then returned to pre­COVID levels during post­COVID. Continuous demographic variables demonstrated a consistent pattern (see Tables 3–5). Specifically, FSF age increased significantly (p < .05); credit load decreased at COVID and remained lower than pre­COVID levels at post­COVID; weekly time spent on homework increased during COVID, but returned to pre ­ COVID levels. Moreover, SES Family Education decreased significantly during COVID but did not differ between pre­COVID and post­COVID, whereas SES Family Income increased from pre­COVID to post­COVID, rising significantly (p < .05) from lower COVID levels.

Independent ­ samples t tests between the preCOVID and COVID groups among only FSF (see Table 3), identified statistically significant ( p < .05) increased vegetable consumption, physical symptom complaints, and daily screen time along with decreased physical activity and social integration. Comparisons of Pre­COVID and Post­COVID (see Table 4) uncovered only two differences: increased vegetable consumption and physical symptom complaints. In the examination of differences between COVID and Post­COVID groups (see Table 5), physical activity and social integration were higher whereas both interpersonal conflict and daily screen time were lower for Post­COVID. All other variable comparisons did not differ significantly (see Tables 3–5).

Combined, these results identified 11 distinct variables that differed significantly (p < .05) at some point among FSF between the three time period cohorts. Only these 11 variables that exhibited statistical significant differences have been graphically depicted (see Figures 1–2), so that any difference in variables not displayed in these figures was likely due to chance. Although most demographic variables seem to have returned to prepandemic levels, SES (both education and income) for

FIGURE 1

Continuous Demographics for First Semester Freshmen Over Time

Wright, Brough, Castro, Osborne, Johnson, and Johnson | First-Year Health COVID-19 FSF

Note. FSF = first semester freshmen. Time points 1, 2 and 3 represent Pre-COVID, COVID, and Post-COVID groups, respectively. Only these variables that demonstrated statistical significant differences (p < .05) have been graphically depicted here along with corresponding effect size. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d> .80. Homework time is hours spent per week. SES Education represents education level of parents and SES Income is family income per past 12 months, with higher values representing greater education and income, respectively. Effect sizes are reported relative to differences from the Pre-COVID time period, thus there are no calculations made for difference between COVID and Post-COVID. The general pattern of results demonstrates FSF have returned to pre-pandemic restriction levels regarding age and homework time, but are taking fewer credits and come from families of higher SES.

TABLE 3

Differences Between

and

First Semester Freshmen

12.72 (7.06) 14.46 (7.89) +1.74 2.37 (533)* .018 0.21

SES: Family Education 3.26 (1.23) 3.19 (1.29) -0.07 0.57 (533) .567 0.05

SES: Family Income 5.61 (2.00) 5.60 (2.00) -0.01 0.05 (533) .958 0.00 Health Behavior

Fruit 0.79 (0.76) 0.96 (0.95) +0.15 1.84 (533) .066 0.16 Vegetable 0.74 (0.84) 1.03 (1.03) +0.29 3.09 (533)* .002 0.27 Sugary Snack 0.61 (0.85) 0.65 (0.88) +0.04

(1.14) 6.69 (1.20) +0.15 1.32 (533) .187 0.11 Sleep

FSF were significantly higher following the COVID pandemic restrictions. Moreover, many other FSF variables were different when the COVID pandemic restrictions took effect (e.g., social integration, daily screen time), but only two of them (vegetable consumption, physical symptoms) exhibited sustained significant differences after the COVID restrictions were lifted. Thus, FSF, in many ways, seem to have returned to prepandemic levels in demographics, health behaviors, health outcomes, and technology use behaviors.

Research Question 2

Affect 3.34 (0.73) 3.25 (0.72) -0.09 1.30 (533) .195 0.11

Affect 2.67 (0.80) 2.77 (0.84) +0.10 1.26 (533) .209 0.11

Depress Symptoms 8.88 (3.33) 9.12 (3.37) +0.24 0.75 (533) .456 0.07

Social Health

Loneliness 2.77 (0.91) 2.73 (1.03) -0.04 0.42 (533) .676 0.04 Interpersonal Conflict 2.08 (0.71) 2.12 (0.85) +0.04 0.51 (533) .608 0.04 Social Integration 0.38 (0.20) 0.18 (0.18) -0.20 11.22 (533)*** <.001 0.97

Technology Use TV Time 66.91 (61.64) 70.98 (55.42) +4.07 0.74 (533) .459 0.06 Screen Time 174.58 (141.74)

Note. * p < .05, ** p < .01. Δ represents average differences between Pre-COVID and COVID, such that positive numbers reflect a higher value for the COVID cohort. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Homework time is hours spent per week. SES Education represents education level of parents and SES Income is family income per past 12 months, with higher values representing greater education and income, respectively. Fruit and vegetable consumption, sugary snacks, sugary drinks, and fast food, are all reported in daily units; sleep quantity is in hours; and physical activity is reported in number of minutes per day. Technology use variables are all reported in minutes per day. Bonferroni corrections were applied within the health behavior and mental health categories, given the concern for high numbers of significance testing raising chance of Type I error. Differences that were statistically significant and at least a small effect size (d > .20) have been bolded for ease of identification.

We addressed RQ2 similarly by examining differences between FSF and NFSF within each time period cohort (see Tables 6–8). Not surprisingly, in the pre­COVID group, age differed significantly (p < .05) as FSF were much younger than NFSF, and time spent on homework was significantly higher for NFSF (see Table 6). Only three other statistically significant (p < .05) differences emerged in the pre­COVID group as FSF demonstrated lower subjective physical health ratings, higher loneliness perceptions, and higher daily time spent on social media than NFSF. Within the COVID group (see Table 7), age and homework time both remained significantly higher among NFSF, but credit load was significantly higher for FSF. Another nine differences emerged among the other variables as FSF reported lower fruit and vegetable consumption, sleep quality, subjective health, satisfaction with life, and loneliness along with higher physical symptoms, negative affect, and daily social media time than NFSF. Finally, among the post­COVID group (see Table 8), NFSF were older, spent more time on homework, but enrolled in fewer credits than FSF. However, the effect sizes were very small, suggesting a negligible difference (ds = .13, .14, respectively). Six other differences were discovered among FSF including higher physical symptoms, anxiety, depressive symptoms, and daily social media time while reporting lower sleep quantity than NFSF. All other variable comparisons were not statistically different between FSF and NFSF (see Tables 6–8).

Collectively, 16 distinct variables were statistically different (p < .05) between FSF and NFSF at least once across the three time periods. Only these variables that demonstrated statistically significant differences have been graphically depicted (see Figures 3–5), so that any difference in those variables not portrayed in these figures was likely due to chance. Demographically, FSF were younger, took more credits, and spent less time on homework. General patterns highlight that FSF seem to be lower than NFSF in each health metric, except for social integration, suggesting that social interactions are not lacking among FSF. Interestingly, subjective health

FIGURE 2

Health and Wellness Variables for First Semester Freshmen Over Time

Note. FSF = first semester freshmen. Time points 1, 2 and 3 represent Pre-COVID, COVID, and Post-COVID groups, respectively. Only these variables that demonstrated statistical significant differences (p < .05) have been graphically depicted here along with corresponding effect size. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Effect sizes are reported relative to differences from the Pre-COVID time period, thus there are no calculations made for difference between COVID and Post-COVID. Fruit and vegetable consumption, sugary snacks, sugary drinks, and fast food, are all reported in daily units; sleep quantity is in hours; and physical activity is reported in number of minutes per day. Technology use variables are all reported in minutes per day. Generally, these results show that while FSF digressed in many variables during the restrictions, they are overall healthier after the restrictions, though reporting more physical symptoms.

TABLE 4

Differences Between Pre-COVID and Post-COVID First Semester Freshmen

Vegetable 0.74 (0.84) 0.91 (0.97) +0.17 1.97 (715) .049 0.15

Sugary Snack 0.61 (0.85)

Appreciation 4.96 (1.38) 5.01 (1.35) +0.05 0.40 (715) .687 0.03

Emotional Health

Positive Affect 3.34 (0.73) 3.33 (0.66) -0.01 0.16 (715) .871 0.01

Negative Affect 2.67 (0.80) 2.81 (0.81) +0.14 1.90 (715) .058 0.14

Depress Symptoms 8.88 (3.33) 9.35 (3.43) +0.47 1.51 (715) .132 0.11

Social Health

Loneliness 2.77 (0.91) 2.76 (0.95) -0.01 0.05 (715) .957 0.00

Interpersonal Conflict 2.08 (0.71) 1.99 (0.72) -0.09 1.37 (715) .171 0.10

Social Integration 0.38 (0.20) 0.41 (0.22) +0.03 1.52 (715) .129 0.11

Technology Use TV Time 66.91 (61.64) 74.37 (61.57)

Note. * p < .05, ** p < .01. Δ represents average differences between Pre-COVID and Post-COVID, such that positive numbers reflect a higher value for the Post-COVID cohort. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Homework time is hours spent per week. SES Education represents education level of parents and SES Income is family income per past 12 months, with higher values representing greater education and income, respectively. Fruit and vegetable consumption, sugary snacks, sugary drinks, and fast food, are all reported in daily units; sleep quantity is in hours; and physical activity is reported in number of minutes per day. Technology use variables are all reported in minutes per day. Bonferroni corrections were applied within the health behavior and mental health categories, given the concern for high numbers of significance testing raising chance of Type I error. Differences that were statistically significant and at least a small effect size (d > .20) have been bolded for ease of identification. First-Year Health COVID-19 | Wright, Brough, Castro, Osborne, Johnson, and Johnson

ratings were lower for FSF in the first two time points, although FSF reported higher physical symptoms during and following COVID­19 pandemic restrictions. Finally, higher social media was consistently observed across all three time points for FSF compared to NFSF.

Discussion

FSF represent a population of college students who undergo dramatic change as they come to college and often show signs of the difficulties of this acclimation in poor health and wellness. Recent events over the course of the past several years related to the COVID­19 pandemic have ushered in a period of unprecedented changes, which might have impacted vulnerable populations, such as FSF (Tinto, 2006), in terms of their health and wellness. The current study sought to explore possible differences over the past six years among FSF and between FSF and NFSF in three student cohorts (pre­COVID, COVID, post­COVID). Results suggest that FSF seem to have returned to prepandemic levels in most domains, but not all. Furthermore, FSF reported poorer outcomes than NFSF across nearly every metric and time period, suggesting that FSF may be susceptible to poorer health and behavioral habits. These findings highlight implications for the health and wellness of students, which may play a role in the overall retention of college students, which is an important issue facing higher education following the pandemic.

First, FSF differed across the three time points (preCOVID, COVID, post­COVID) in many ways. COVID19 pandemic restrictions had observable effects on FSF, as average age was higher, more ethnic minorities and married students were represented, and credit load and daily time spent on homework was lower than before or after the pandemic. This is consistent with other studies identifying that many people started college for the first time during the pandemic as in­person courses became more available via technology (e.g., synchronous video conferencing) to those who had been unable to enroll such as nontraditional students (e.g., older, full­time workers; Hamilton, 2024; Vargo et al., 2021). Following the pandemic restrictions, however, many of these metrics returned to prepandemic levels for FSF, suggesting that the pandemic restrictions provided a brief opportunity for a different demographic. Whereas most of these metrics returned to prepandemic levels, it is interesting to note that SES (income, education) increased at the post­COVID period, suggesting that prospective college students from families of higher SES might have delayed their entrance into college until after the pandemic restrictions had passed. Moreover, those who are older, married, employed full­time or of minority status might have found starting college under

postpandemic conditions to be not as attractive (Sweet & Swayze, 2023). Regardless, this might suggest that FSF who are lower in SES may be taking higher credit loads, which could contribute to poorer health and retention rates in the longer term (Burke, 2019; Wilson et al., 2020).

Other observed differences in behavior and health among the FSF shed more light on the potential influence of the COVID­19 pandemic. For instance, pandemic restrictions noticeably heightened public awareness of health, as the public was encouraged to socially isolate, actively monitor symptoms, and seek treatment, as applicable. With this heightened awareness, an increase in health behavior such as vegetable consumption coinciding with an increased reporting of physical symptoms would seem logical. Moreover, the social isolation measures in effect during COVID would account for the dramatic decrease in social interactions while explaining the decrease in physical activity. Indeed,

these findings suggest that the COVID­19 pandemic restrictions were effective at reducing social interactions, but might have had the unintended consequence of increasing daily exposure to screens and technology, which may have negative health implications (Wright et al., 2022). However, it is also important to note the absence of differences in FSF among the mental and emotional health constructs, which is counter to other findings in the literature (De Coninck et al., 2023; Dixon et al., 2023; Frazier et al., 2023; Liu et al., 2022). Although it is possible that the self­report methodology was not sufficiently sensitive to capture these differences, a likely explanation is tied to the demographic differences: such that those who are older, married, and employed full­time might not have exhibited as dramatic differences in their psychological well­being among the COVID cohort.

Second, differences between FSF and NFSF between the three time periods provide additional insight.

FIGURE 3

Continuous Demographics Differences Between FSF and NFSF Over Time

Note. FSF = to first-semester freshmen; NFSF = non-first semester freshmen. Time points 1, 2 and 3 represent Pre-COVID, COVID, and Post-COVID groups, respectively. Only these variables that demonstrated statistical significant differences (p < .05) have been graphically depicted here along with corresponding effect size to represent differences at each time point. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Homework time is hours spent per week. These results portray FSF as consistently younger, taking more courses, and spending less time on homework than their NFSF counterparts.

TABLE 5 Differences

First Semester Freshmen

Homework Time 14.46 (7.89) 13.07 (6.93) -1.39 2.86 (946)** .004 0.19

SES: Family Education 3.19 (1.29) 3.44 (1.29) +0.25 2.93 (946)** .004 0.19

SES: Family Income 5.60 (2.00) 6.22 (1.76) +0.62 5.03 (946)*** <.001 0.33 Health Behavior

Fruit 0.96 (0.95) 0.95 (0.99) -0.01 0.16 (946) .877 0.01

Vegetable 1.03 (1.03) 0.91 (0.97) -0.12 1.82 (946) .069 0.12

Sugary Snack 0.65 (0.88) 0.72 (0.86) +0.07 1.22 (946) .223 0.08

Sugary Drink 0.54 (0.86) 0.51 (0.83) -0.03 0.54 (946) .591 0.04

Fast Food 0.21 (0.34) 0.24 (0.49) +0.03 1.04 (946) .299 0.07

Sleep Quantity 6.69 (1.20) 6.63 (1.05) -0.06 0.81 (946) .416 0.05

Sleep Quality 3.64 (0.83) 3.73 (0.78) +0.09 1.70 (946) .090 0.11

Physical Activity 29.75 (27.76) 39.37 (26.88) +9.62 5.34 (946)*** <.001 0.35

Subjective Overall Health 76.37 (16.64) 78.41 (15.18) +2.04 1.95 (946) .051 0.13

Physical Symptoms 5.88 (3.72) 5.94 (3.86) +0.06 0.24 (946) .812 0.02

Mental Health

Perceived Stress 2.70 (0.73) 2.76 (0.69) +0.06 1.28 (946) .200 0.08

Anxiety 2.95 (0.74) 2.98 (0.77) +0.03 0.60 (946) .550 0.04

Satisfaction w Life 4.61 (1.29) 4.54 (1.34) -0.07 0.80 (946) .423 0.05

Body Appreciation 4.97 (1.45) 5.01 (1.35) +0.04 0.43 (946) .664 0.03

Emotional Health

Positive Affect 3.25 (0.72) 3.33 (0.66) +0.08 1.76 (946) .078 0.11

Negative Affect 2.77 (0.84) 2.81 (0.81) +0.04 0.74 (946) .463 0.05

Depress Symptoms 9.12 (3.37) 9.35 (3.43) +0.23 1.02 (946) .308 0.07

Social Health

Loneliness 2.73 (1.03) 2.76 (0.95) +0.03 0.46 (946) .645 0.03

Interpersonal Conflict 2.12 (0.85) 1.99 (0.72) -0.13 2.53 (946)* .011 0.16

Social Integration 0.18 (0.18) 0.41 (0.22) +0.23 16.97 (946)*** <.001 1.10

Technology Use

Note. * p < .05, ** p < .01. Δ represents average differences between COVID and Post-COVID, such that positive numbers reflect a higher value for the Post-COVID cohort. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Homework time is hours spent per week. SES Education represents education level of parents and SES Income is family income per past 12 months, with higher values representing greater education and income, respectively. Fruit and vegetable consumption, sugary snacks, sugary drinks, and fast food, are all reported in daily units; sleep quantity is in hours; and physical activity is reported in number of minutes per day. Technology use variables are all reported in minutes per day. Bonferroni corrections were applied within the health behavior and mental health categories, given the concern for high numbers of significance testing raising chance of Type I error. Differences that were statistically significant and at least a small effect size (d > .20) have been bolded for ease of identification. First-Year Health COVID-19

For instance, along with the observation that more nontraditional students started as FSF during COVID (Hamilton, 2024), many returned to take college courses as NFSF. Moreover, FSF consistently took higher credit loads and spent less time on homework across all three time periods, suggesting that FSF may need help in taking an appropriate number of credits initially whereas NFSF have learned to take fewer credits to manage workload. It may be that other factors are contributing to this observation, however, as many scholarships and financial aid packages require a full­time load, which may account for the observation that FSF were taking more credits than NFSF. Regardless, consistent with a recent study highlighting the need for early identification of risk factors for first­year attrition (Stephenson et al., 2020), these results suggest that credit workload may be one of these factors. Furthermore, FSF are likely to be inexperienced in time management and unfamiliar with the demands of homework, studying, and applying themselves to achieve good grades, particularly when taking multiple courses concurrently. Thus, out of inexperience, FSF are likely to enroll in too many credits and have poor time management skills, which corresponds with both poorer health and higher attrition rates.

TV Time 70.98 (55.42) 74.37 (61.57) +3.39 0.87 (946) .387 0.06

Time

(135.68) 193.49 (127.94) -20.95 2.41 (946)* .016 0.16

Media Time 213.78 (185.69) 204.67 (177.03) -9.11 0.76 (946) .446 0.05

Other findings support a similar conclusion that FSF seem at a disadvantage compared to their NFSF counterparts. Most notably, FSF reported poorer ratings than NFSF in every health metric, save social interaction, between the three time periods. As newly integrated members of the higher educational system, many FSF may not know how best to manage their time, incorporate hygiene behaviors, or to self­regulate, which may explain the poorer diet and sleep observations. Building on that further, the lack of experience and/ or self­regulation ability may lead to an overemphasis on social aspects with friends, explaining how this one health measure may be the only metric in which FSF seem to rate better than NFSF. Moreover, FSF’s elevated anxiety, depression, negative affect and lower satisfaction with life relative to NFSF may be a compound of college life inexperience, poor self­regulation skills, and a lasting impact of the COVID­19 pandemic, as studies have uncovered poorer mental health after the pandemic restrictions (De Coninck et al., 2023; Dixon et al., 2023; Frazier et al., 2023).

These findings imply that the COVID­19 pandemic might have shifted the social comparison baseline for health, impacting FSF more than other academic groups. Interestingly, after the pandemic, FSF reported a seeming paradoxical relationship such that both their subjective physical health ratings and physical symptom complaints were higher following the onset of the pandemic. NFSF reports were more consistent

FIGURE 4

Health Behavior and Physical Health Differences Between FSF and NFSF Over Time

Note. FSF = first semester freshmen; NFSF = non-first semester freshmen. Time points 1, 2 and 3 represent Pre-COVID, COVID, and Post-COVID groups, respectively. Only these variables that demonstrated statistical significant difference (p < .05) have been graphically depicted here along with corresponding effect size to represent differences at each time point. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Fruit and vegetable consumption are reported in daily units; sleep quantity is in hours. Generally, these results show FSF have consistent poorer health metrics than NFSF counterparts during the study period, especially during COVID restrictions, though subjective health and fruit/vegetable consumption seems to be similar afterwards.

SPRING 2025

PSI CHI

TABLE 6 Differences Between FSF and NFSF Pre-COVID

Demographics (Continuous)

SES: Family Education 3.26 (1.23) 3.23 (1.25) +0.03 0.23 (389) .816 0.02

SES: Family Income 5.61 (2.00) 5.80 (1.85) -0.19 0.90 (328) .371 0.09

Health Behavior

Sugary Snack 0.61 (0.85) 0.61 (0.73) +0.00 0.01 (391) .992 0.00

Sugary Drink 0.42 (0.77) 0.39 (0.56) +0.03 0.50 (391) .615 0.05

between cohorts, suggesting that the pandemic might have had a more profound impact on FSF, lowering thresholds of awareness for their symptoms while simultaneously providing a point of reference to others who had worse health complaints. As another example, FSF reported more loneliness during the pandemic and more social integration following the pandemic than their NFSF counterparts, suggesting that FSF were placing a high value on social interactions and when they were not available, exhibited poorer concurrent health. Establishing this point further, daily social media use was higher for FSF than NFSF across every time period, but especially during the pandemic restrictions. Prior research has demonstrated that increased time on social media is associated with poorer health outcomes and behaviors (Hunt et al., 2018; Wright et al., 2020), suggesting that this link between social constraint and increased social media use may be maladaptive for health and wellness. Indeed, the overall pattern of results where FSF seem to have poorer health outcomes and behaviors than NFSF seem to fit the conclusion that FSF might have been impacted disproportionately more than others by the social restrictions. Thus, in this light, social constraints such as those imposed by the pandemic restrictions may place FSF at greater risk for poorer health outcomes and behaviors, as they seem particularly sensitive to these conditions.

Perceived Stress 2.67 (0.68) 2.65 (0.73) +.02 0.02 (389) .986 0.00

2.85 (0.72) 2.85 (0.76) +.00 0.01 (389) .994 0.00 Satisfaction w Life 4.60 (1.28) 4.82 (1.31) -.22 1.63 (389) .105 0.16 Body Appreciation 4.96 (1.38) 5.19 (1.28) -.23

(0.76) +.00 0.03 (390) .973 0.00

Depress Symptoms 8.88 (3.33) 8.54 (3.14) +.34 1.03 (390) .305 0.10

Social Health

Loneliness 2.77 (0.91)

Integration

(0.20) 0.34 (0.18) +.04 0.15 (389) .878 0.02

Technology Use TV Time 66.91 (61.64) 59.21 (53.78) +7.70 1.30 (389) .193

(389)* .045 0.20

Note. * p < .05, ** p < .01. FSF = first semester freshmen; NFSF = non-first semester freshmen. Δ represents difference between FSF and NFSF, such that positive values mean FSF are higher than NFSF. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Homework time is hours spent per week. SES Education represents education level of parents and SES Income is family income per past 12 months, with higher values representing greater education and income, respectively. Fruit and vegetable consumption, sugary snacks, sugary drinks, and fast food, are all reported in daily units; sleep quantity is in hours; and physical activity is reported in number of minutes per day. Technology use variables are all reported in minutes per day. Bonferroni corrections were applied within the health behavior and mental health categories, given the concern for high numbers of significance testing raising chance of Type I error. Differences that were statistically significant and at least a small effect size (d > .20) have been bolded for ease of identification.

Third, by identifying these differences between cohorts that were prior to, during, and following the COVID ­ 19 pandemic restrictions, this study offers some insight regarding how FSF might have changed through this pandemic and how academic institutions may best respond now and in the future to help them. To address deficits in FSF health behavior (e.g., sleep, diet, physical activity) and time management practices, universities can institute required first semester courses specific to these behaviors to help students realize the benefits (e.g., Wright, Nelson et al., 2020). Going a step further, universities and institutions could ensure that mental health support is more readily available for their students, especially FSF, by including counselors and therapists within student health centers. Next, even as these results suggest that NFSF are engaging in better health behavior, time management practices, and reporting higher mental health, implementing a mentoring program where NFSF are paired with FSF may also yield health and wellness benefits. This social approach may be particularly effective for FSF who report more social interactions and likely view NFSF as role models of appropriate behavior, coping strategies, and possible friendship. Finally, as these health and wellness differences through the COVID­19 pandemic restrictions have shed light on challenges for FSF, they further

FIGURE 5

Mental, Emotional, and Social Health Differences Between FSF and NFSF Over Time

Note. FSF = first semester freshmen; NFSF = non-first semester freshmen. Time points 1, 2 and 3 represent Pre-COVID, COVID, and Post-COVID groups, respectively. Only these variables that demonstrated statistical significant difference (p < .05) have been graphically depicted here along with corresponding effect size to represent differences at each time point. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d> .80. Daily social media time is reported in minutes per day. With only the one exception of social integration, FSF demonstrated poorer health metrics over time, particularly during and after the pandemic restrictions.

SPRING 2025

OF

TABLE 7

(0.95)

(1.12) -0.22 3.24 (1057)** .001 0.20 Vegetable 1.03 (1.03) 1.28 (1.21) -0.25 3.41 (1057)*** <.001 0.21 Sugary Snack 0.65 (0.88) 0.67 (0.87) -0.02 0.28 (1057) .783 0.02

Sugary Drink 0.54 (0.86) 0.46 (0.79) +0.08 1.19 (1057) .234 0.07

Fast Food 0.21 (0.34) 0.19 (0.32) +0.02 0.54 (1057) .592 0.03

Sleep Quantity 6.69 (1.20) 6.74 (1.14) -0.05 0.68 (1043) .500 0.04

Sleep Quality 3.64 (0.83) 3.76 (0.81) -0.12 2.30 (1057) .022 0.14

Physical Activity 29.75 (27.76) 32.08 (26.33) -2.33 1.39 (1057) .164 0.09 Sedentary Behavior 115.32 (47.66) 112.04 (54.95) +3.28 0.98 (1057) .393 0.06

Physical Health Subjective Overall Health 76.37

Symptoms 5.88 (3.72) 5.34 (3.65) +0.54 2.30 (1057)* .022 0.14 Mental Health

Perceived Stress 2.70 (0.73) 2.65 (0.66) +0.05 1.13 (1057) .260 0.07 Anxiety 2.95 (0.74) 2.86 (0.80) +0.09 1.81 (1057) .070 0.11

w Life 4.61 (1.29) 4.78 (1.32) -0.17 2.08 (1057) .038 0.13

Appreciation 4.97 (1.45) 5.07 (1.36) -0.10 1.14 (1057) .254 0.07

Emotional Health

Affect 3.25 (0.72) 3.31 (0.69) -0.06 1.49 (1057) .137 0.09

Affect 2.77 (0.84) 2.59 (0.80) +0.18 3.23 (1057)** .001 0.20 Depress Symptoms 9.12 (3.37) 8.72 (3.18) +0.40 1.92 (1057) .055 0.12

Social Health Loneliness 2.73 (1.03) 2.54 (1.01) +0.19 2.90 (1057)** .004 0.19

Conflict 2.12 (0.85) 2.11 (0.75) +0.01 0.11 (1057) .916 0.01 Social Integration 0.18 (0.18) 0.16 (0.20) +0.02 1.14 (1057) .255 0.07 Technology Use

(55.42)

(185.69) 174.68 (158.41) +39.10 3.75 (1057)*** <.001 0.23

Note. * p < .05, ** p < .01. FSF = first semester freshmen; NFSF = non-first semester freshmen. Δ represents difference between FSF and NFSF, such that positive values mean FSF are higher than NFSF. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Homework time is hours spent per week. SES Education represents education level of parents and SES Income is family income per past 12 months, with higher values representing greater education and income, respectively. Fruit and vegetable consumption, sugary snacks, sugary drinks, and fast food, are all reported in daily units; sleep quantity is in hours; and physical activity is reported in number of minutes per day. Technology use variables are all reported in minutes per day. Bonferroni corrections were applied within the health behavior and mental health categories, given the concern for high numbers of significance testing raising chance of Type I error. Differences that were statistically significant and at least a small effect size (d > .20) have been bolded for ease of identification. First-Year Health COVID-19 | Wright, Brough, Castro, Osborne, Johnson, and Johnson

highlight a clear need for these kinds of initiatives in a proactive manner to provide supportive infrastructure for FSF when other pandemics arise in the future.

Potential Limitations and Future Research

This study has some potential limitations. First, although this study has employed a multicross­sectional study design of three separate student cohorts over an extended time of six years to establish some temporal comparisons, causal conclusions cannot be drawn as this was not a true longitudinal experimental design with multiple observations within persons over time. Moreover, the demographic differences noted between the cohorts warrant caution in interpretation of other differences, as these differences may confound other comparisons. For instance, the observed fewer differences between FSF and NFSF in the post ­ COVID cohort than the COVID cohort may be due to declines in NFSF, improvements in FSF, or potentially attributed to something else such as the observed differences in demographics. Second, all data collected were subjective self­report and, as such, likely to involve subjective biases that could impact results. Third, other methodological issues such as single­item measures for many of the health behaviors and our operationalization of FSF based on a simple dichotomous measure may not comprehensively represent these constructs despite our use of validated measures in the literature. Moreover, all data come from students enrolled in an introductory psychology course, which may limit the ability to make accurate comparisons with a full spectrum of those who are NFSF, as those who are nearing graduation are much less likely to enroll in this course. Similarly, for the technology use items, motives and uses for social media, in particular, may be very different across apps/ platforms, which may confound our results. Fourth, an inability to isolate or control confounding alternative explanations (e.g., differences in health during COVID could be age­related) may limit confidence in explanations tied to the COVID pandemic restrictions. Finally, demographic characteristics of the sample may not be representative of the larger college population, although our large overall sample size mitigates this concern.

In conclusion, our study extends the literature to consider health and wellness of a vulnerable population, FSF, which may have implications for the university setting including college student retention. Future research should investigate this important issue by using sophisticated longitudinal designs to ascertain causation, where FSF health and wellness are used as predictors of attrition. Additional studies may expand on our findings by including other indicators of health, particularly objective measures of blood pressure, BMI, or body

Wright, Brough, Castro, Osborne, Johnson, and Johnson | First-Year Health COVID-19

composition. Moreover, ethnic minorities and other more vulnerable subpopulations within FSF could be investigated to determine factors that may cause further risk of health deterioration and potential college dropout (Liu et al., 2022). Further investigation could be made within other populations, including online students or within other cultural settings for more nuanced relationships. Finally, these findings highlight the importance of examining potential changes of health and wellness among FSF within the past six years (2018–2023), and point towards efforts that should be made to address health deficiencies while proactively providing support to aid this vulnerable population.

References

Acharya, L., Jin, L., & Collins, W. (2018). College life is stressful today: Emerging stressors and depressive symptoms in college students. Journal of American College Health, 66(7), 655–664. https://doi.org/10.1080/07448481.2018.1451869

Avalos, L., Tylka, T. L., & Wood-Barcalow, N. (2005). The Body Appreciation Scale: Development and psychometric evaluation. Body Image, 2(3), 285–297. https://doi.org/10.1016/j.bodyim.2005.06.002

Birmingham, W. C., Wadsworth, L. L., Lassetter, J. H., Graff, T. C., Lauren, E., & Hung, M. (2021). COVID-19 lockdown: Impact on college students’ lives. Journal of American College Health, 69(8), 1–15. https://doi.org/10.1080/07448481.2021.1909041

Bohannon, R. W., Maljanian, R., & Goethe, J. (2003). Screening for depression in clinical practice: Reliability and validity of a five-item subset of the CESDepression. Perceptual and Motor Skills, 97(3), 855–861. https://doi.org/10.2466/pms.97.7.855-861

Bryan, K. S., Puckett, Y. N., & Newman, M. L. (2013). Peer relationships and health: From childhood through adulthood. In M. L. Newman & N. A. Roberts (Eds.), Health and social relationships: The good, the bad, and the complicated (pp. 167–188). American Psychological Association. Burke, A. (2019). Student retention models in higher education: A literature review. College and University, 94(2), 12–21. Bustamante, B. (2024, March 5). College dropout rates. EducationData.Org. https://educationdata.org/college-dropout-rates/

Butz, D. A., & Yogeeswaran, K. (2011). A new threat in the air: Macroeconomic threat increases prejudice against Asian Americans. Journal of Experimental Social Psychology, 47(1), 22–27. https://doi.org/10.1016/j.jesp.2010.07.014

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396. https://doi.org/10.2307/2136404

Colby, A., Fereday, B., Le, N. Q., & Malin, H. (2023). A longitudinal study of US college students before and during the Covid-19 pandemic. Journal of American College Health, 1–11. https://doi.org/10.1080/07448481.2023.2220391

De Coninck, D., Matthijs, K., Van Bavel, J., & Luyten, P. (2023). To be a freshman during the COVID-19 pandemic: A cross-lagged model of depression, mentalizing, and epistemic trust. Personality and Mental Health, 18, 80–89. https://doi.org/10.1002/pmh.1598

Dixon, L. J., Witcraft, S. M., & Schadegg, M. J. (2023). COVID-19 anxiety and mental health among university students during the early phases of the US pandemic. Journal of American College Health, 71, 1152–1160. https://doi.org/10.1080/07448481.2021.1923504

Diener, E., Emmons, R. A., Larsen, R. J., & Griffin, S. (1985). The Satisfaction With Life Scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13

Elharake, J. A., Akbar, F., Malik, A. A., Gilliam, W., & Omer, S. B. (2022). Mental health impact of COVID-19 among children and college students: A systematic review. Child Psychiatry & Human Development, 54(3), 913–925. https://doi.org/10.1007/s10578-021-01297-1

Ewing, C. R., Nienstedt, C., Wright, R. R., & Chambers, S. (2023). Social media use motives: An influential factor in user behavior and user health profiles. Psi Chi Journal of Psychological Research, 28(4), 375–386. https://doi.org/10.24839/2325-7342.JN28.4.275

Frazier, P., Liu, Y., Asplund, A., Meredith, L., & Nguyen-Feng, V. N. (2023). US college

Outcome

TABLE 8

Differences Between FSF and NFSF Post-COVID FSF NFSF

Note. * p < .05, ** p < .01. FSF = first semester freshmen; NFSF =non-first semester freshmen. Δ represents difference between FSF and NFSF, such that positive values mean FSF are higher than NFSF. Effect size (d) is interpreted as: small is d > .20, medium is d > .50, and large is d > .80. Homework time is hours spent per week. SES Education represents education level of parents and SES Income is family income per past 12 months, with higher values representing greater education and income, respectively. Fruit and vegetable consumption, sugary snacks, sugary drinks, and fast food, are all reported in daily units; sleep quantity is in hours; and physical activity is reported in number of minutes per day. Technology use variables are all reported in minutes per day. Bonferroni corrections were applied within the health behavior and mental health categories, given the concern for high numbers of significance testing raising chance of Type I error. Differences that were statistically significant and at least a small effect size (d > .20) have been bolded for ease of identification.

First-Year Health COVID-19 | Wright, Brough, Castro, Osborne, Johnson, and Johnson

student mental health and COVID-19: Comparing pre-pandemic and pandemic time points. Journal of American College Health, 71(9), 2686–2696. https://doi.org/10.1080/07448481.2021.1987247

Gropper, S. S., Simmons, K. P., Gaines, A., Drawdy, K., Saunders, D., Ulrich, P., & Connell, L. J. (2009). The freshman 15—A closer look. Journal of American College Health, 58(3), 223–231. https://doi.org/10.1080/07448480903295334

Hamilton, I. (2024). By the numbers: The rise of online learning in the U.S. Forbes. https://www.forbes.com/advisor/education/student-resources/online-learning-stats/ Hanson, M. (2024, January 10). College enrollment and student demographic statistics EducationData.Org. https://educationdata.org/college-enrollment-statistics

Hughes, M. E., Waite, L. J., Hawkley, L. C., & Cacioppo, J. (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on Aging, 26(6), 655–672. https://doi.org/10.1177/0164027504268574

Huckins, J. F., daSilva, A. W., Wang, W., Hedlund, E., Rogers, C., Nepal, S. K., Wu, J., Obuchi, M., Murphy, E. I., Meyer, M. L., Wagner, D. D., Holtzheimer, P. E., & Campbell, A. T. (2020). Mental health and behavior of college students during the early phases of the COVID-19 pandemic: Longitudinal smartphone and ecological momentary assessment study. Journal of Medical Internet Research, 22(6), e20185. https://doi.org/10.2196/20185

Hunt, M. G., Marx, R., Lipson, C., & Young, J. (2018). No more FOMO: Limiting social media decreases loneliness and depression. Journal of Social and Clinical Psychology, 37(10), 751–768. https://doi.org/10.1521/jscp.2018.37.10.751

Kasparek, D. G., Corwin, S. J., Valois, R. F., Sargent, R. G., & Morris, R. L. (2008). Selected health behaviors that influence college freshman weight change. Journal of American College Health, 56, 437–444. https://doi.org/10.3200/JACH.56.44.437-444

Kowalsky, R. J., Farney, T. M., Kline, C. E., Hinojosa, J. N., & Creasy, S. A. (2021). The impact of the Covid-19 pandemic on lifestyle behaviors in US college students. Journal of American College Health, 69, 1–6. https://doi.org/10.1080/07448481.2021.1923505

Liu, C. H., Stevens, C., Wong, S. H., Yasui, M., & Chen, J. A. (2019). The prevalence and predictors of mental health diagnoses and suicide among US college students: Implications for addressing disparities in service use. Depression and Anxiety, 36(1), 8–17. https://doi.org/10.1002/da.22830

Liu, Y., Frazier, P. A., Porta, C. M., & Lust, K. (2022). Mental health of US undergraduate and graduate students before and during the COVID-19 pandemic: Differences across sociodemographic groups. Psychiatry Research, 309, 114428. https://doi.org/10.1016/j.psychres.2022.114428

Lorig, K., Stewart, A., Ritter, P., González, V., Laurent, D., & Lynch, J. (1996). Outcome measures for health education and other health care interventions. Sage Publications.

Neophytou, E., Manwell, L. A., & Eikelboom, R. (2021). Effects of excessive screen time on neurodevelopment, learning, memory, mental health, and neurodegeneration: A scoping review. International Journal of Mental Health and Addiction, 19(3), 724–744. https://doi.org/10.1007/s11469-019-00182-2

Nienstedt, C., Smith, N., Braithwaite, H., Gilbert, B., & Wright, R. R. (2023). Swiping away our wellbeing? Examining TikTok use among college students. Psi Chi Journal of Psychological Research, 28(2), 96–106. https://doi.org/10.24839/2325-7342.JN28.2.96

Racette, S. B., Deusinger, S. S., Strube, M. J., Highstein, G. R., & Deusinger, R. H. (2008). Changes in weight and health behaviors from freshman through senior year of college. Journal of Nutrition Education and Behavior, 40(1), 39–42. https://doi.org/10.1016/j.jneb.2007.01.001

Reuter, P. R., & Forster, B. L. (2021). Student health behavior and academic performance. PeerJ, 9, e11107. https://doi.org/10.7717/peerj.11107

Rosenberg, D. E., Norman, G. J., Wagner, N., Patrick, K., Calfas, K. J., & Sallis, J. F. (2010). Reliability and validity of the Sedentary Behavior Questionnaire (SBQ) for adults. Journal of Physical Activity and Health, 7(6), 697–705. https://doi.org/10.1123/JPAH.7.6.697

Ruthig, J. C., Marrone, S., Hladkyj, S., & Robinson-Epp, N. (2011). Changes in college student health: Implications for academic performance. Journal of College Student Development, 52(3), 307–320. https://doi.org/10.1353/csd.2011.0038

Salomon, I., & Brown, C. S. (2018). The selfie generation: Examining the relationship between social media use and early adolescent body image. The Journal of Early Adolescence, 39(4), 539–560. https://doi.org/10.1177/0272431618770809

Sax, L. J. (1997). Health trends among college freshmen. Journal of American College Health, 45(6), 252–264. https://doi.org/10.1080/07448481.1997.9936895

Spector, P. E., & Jex, S. M. (1998). Development of four self-report measures of

job stressors and strain: Interpersonal conflict at work scale, organizational constraints scale, quantitative workload inventory, and physical symptoms inventory. Journal of Occupational Health Psychology, 3(4), 356–367. https://doi.org/10.1037/1076-8998.3.4.356

Spruance, L. A., Karmakar, M., Kruger, J. S., & Vaterlaus, M. (2017). “Are you still watching?” Correlations between binge TV watching, diet and physical activity. Journal of Obesity and Weight Management, 2, 1–8.

Stephenson, A. L., Yerger, D. B., & Heckert, D. A. (2020). Recalibrating our understanding of retention. Journal of College Student Retention: Research, Theory & Practice, 22(1), 17–26. https://doi.org/10.1177/1521025117721852

Sultana, A., Tasnim, S., Hossain, M. M., Bhattacharya, S., & Purohit, N. (2021). Digital screen time during the COVID-19 pandemic: A public health concern. F1000Research, 10, 81. https://doi.org/10.12688/f1000research.50880.1

Sweet, J., & Swayze, S. (2023). Academic psychological capital: A novel approach to freshmen retention. Journal of College Student Retention: Research, Theory & Practice, 25(2), 235–253. https://doi.org/10.1177/1521025120980372

Tinto, V. (2006). Research and practice of student retention: What next? Journal of College Student Retention: Research, Theory & Practice, 8(1), 1–19. https://doi.org/10.2190/4YNU-4TMB-22DJ-AN4W

Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 3–17. https://doi.org/10.1177/2167702617723376

Vargo, D., Zhu, L., Benwell, B., & Yan, Z. (2021). Digital technology use during COVID-19 pandemic: A rapid review. Human Behavior and Emerging Technologies, 3(1), 13–24. https://doi.org/10.1002/hbe2.242

Veenstra, C. P. (2009). A strategy for improving freshman college retention. Journal of Quality Participation, 31, 19–23.

Walton-Pattison, E., Dombrowski, S. U., & Presseau, J. (2018). ‘Just one more episode’: Frequency and theoretical correlates of television binge watching. Journal of Health Psychology, 23(1), 17–24. https://doi.org/10.1177/1359105316643379

Wang, X., Hegde, S., Son, C., Keller, B., Smith, A., & Sasangohar, F. (2020). Investigating mental health of US college students during the COVID-19 pandemic: Crosssectional survey study. Journal of Medical Internet Research, 22(9), e22817. https://doi.org/10.2196/22817

Wilson, O. W. A., Bopp, C. M., Papalia, Z., Duffey, M., & Bopp, M. (2022). Freshman physical activity constraints are related to the current health behaviors and outcomes of college upperclassmen. Journal of American College Health, 70(4), 1112–1118. https://doi.org/10.1080/07448481.2020.1785475

Wright, R. R., Broadbent, C., Graves, A., & Gibson, J. (2016). Health behavior change promotion among Latter-day Saint college students. Psi Chi Journal of Psychological Research, 21(3), 200–215. https://doi.org/10.24839/b21.3.200

Wright, R. R., Larson, J., Richards, S., Larson, S., & Nienstedt, C. (2022). The COVID-19 pandemic: Electronic media use and health among US college students. Journal of American College Health, 1–16. https://doi.org/10.1080/07448481.2022.2155463

Wright, R. R., Nelson, R., Garcia, S., & Butler, A. (2020). Health behavior change in the classroom: A means to a healthy end? Journal of Primary Prevention, 41(5), 445–472 https://doi.org/10.1007/s10935-020-00605-0

Wright, R. R., Nixon, A. E., Peterson, Z. B., Thompson, S. V., Olson, R., Martin, S., & Marrott, D. (2017). The Workplace Interpersonal Conflict Scale: An alternative in conflict assessment. Psi Chi Journal of Psychological Research, 22(3), 163–180. https://doi.org/10.24839/2325-7342.JN22.3.163

Wright, R. R., Perkes, J. L., Schaeffer, C., Woodruff, J. B., Waldrip, K., & Dally, J. L. (2018). Investigating BMI discrepancies in subjective and objective reports among college students. Journal of Human Health Research, 1, 106–115. Wright, R. R., Schaeffer, C., Mullins, R., Evans, A., & Cast, L. (2020). Comparison of student health and well-being profiles and social media use. Psi Chi Journal of Psychological Research, 25(1), 14–21. https://doi.org/10.24839/2325-7342.JN25.1.14

Wright, R. R., Shuai, S., Maldonado, Y., & Nelson, C. (2023). The CENTS program: Promoting healthy eating by addressing perceived barriers. Psychology & Health, 38(9), 1254–1272. https://doi.org/10.1080/08870446.2021.2011281

Wright, R. R., Wilson, M., Nienstedt, C., Ewing, C., Rodriguez, A., Anderson, C., Johnson, N., & Johnson, L. (2024). Quality dating and wellness among a religious college student population: A mixed methods approach. Psi Chi Journal of Psychological Research, 29(3), 213–226. https://doi.org/10.24839/2325-7342.JN29.3.213

Author Note

Robert R. Wright https://orcid.org/0000­0002­4101­7840

Skyler Brough is now at the Department of Informational Technology and Supply Chain Management at Boise State University, Boise, ID.

Lindsay Johnson is now at the School of Education and Human Sciences at University of Kansas, Lawrence, KS.

Materials and data used to support the findings of the current study are available from the corresponding author, upon reasonable request. This research was supported by internal

funding from Brigham Young University­Idaho for student­ and faculty­directed research. The authors declare no conflict of interest. We would like to thank (in alphabetical order) Tristan Bolinger, Samuel Clay, Natalie Johnson, and Brandon Jones for their assistance throughout the conduction of this study and manuscript preparation.

Correspondence concerning this article should be addressed to Robert R. Wright, Department of Psychology, Brigham Young University–Idaho, 210 West 4th South Rexburg, ID 83460­2140. Telephone: 208­496­4085. E­mail: wrightro@byui.edu

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Causal Pathways From Child Maltreatment to Peer Popularity

Keith T. Jennings and Connie M. Tang* Department of Psychology, Stockton University

ABSTRACT. We investigated the causal pathways from child abuse and child neglect to peer popularity through peer relationship. We hypothesized that child abuse and neglect would negatively affect peer popularity. Participants were 322 adolescents from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) database. Path analyses were conducted to analyze the data. The outcome of primary interest was peer popularity, whose determinant with the largest total causal effect was peer relationship (β = .23), followed by child neglect (β = ­.13) and child abuse (β = ­.02). This model explained approximately 6.5% of the variance in peer popularity. The primary determinant of peer relationship was child neglect (β = ­.23), followed by child abuse (β = ­.09). Approximately 6.2% of the variance in peer relationship was explained by this model. Therefore, compared to child abuse, child neglect appeared to impact adolescents’ socioemotional development more adversely.

Keywords: child maltreatment, child abuse, child neglect, peer relationship, peer popularity

Child maltreatment is a rampant problem facing societies worldwide, with some estimating over one in three children facing some form of maltreatment at some point in their life (Kim et al., 2017). It is often defined by the act(s) of a parent or caretaker that result in serious physical, emotional, or sexual harm that will often lead to maladaptation in the child’s psychological development (Cicchetti & Toth, 2016). According to the U.S. Department of Health and Human Services (2024), approximately 1.9 million children were victimized, with neglect (74.3%) being the primary mode of maltreatment followed by physical abuse (17.0%), sexual abuse (10.6%), and psychological maltreatment (6.8%). Maltreatment has been shown to cause a range of socioemotional developmental problems ranging from diminished emotional recognition, emotional understanding, and emotional regulation (Demers et al., 2021; Dodge & Pettit, 2003; English et al., 2015; Rieder & Cicchetti, 1989; Warmingham et al., 2022). These deleterious effects have long­term consequences, often permeating into adulthood and causing cascading effects that disrupt ordinary life. During the formative years of adolescence, these effects are often associated with poorer peer relationship, although research is unclear on the extent and causal link between child maltreatment and peer

connections (Berzenski, 2018; Cutting & Dunn, 1999; Kim & Cicchetti, 2010; Lindblom et al., 2017).

Child Maltreatment

Extant research has demonstrated that child maltreatment has been associated with high emotional labilitynegativity, which led to diminished emotion regulation and was predictive of heightened levels of internalizing symptomatology (e.g., Kim­Spoon et al., 2012). This is to say that maltreatment leads to maladaptive behavior regarding appropriately managing one’s emotions (e.g., mood swings, high reactivity), which may predispose a child to then have higher levels of internalizing problems such as withdrawal and anxiety.

A longitudinal study utilizing data from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) also found significantly worse internalizing and externalizing problems in maltreated children, which persisted from ages 4 to 16 years (Lewis et al., 2016).

A similar study analyzing the same LONGSCAN dataset reported that maltreated children became susceptible to repeat victimization and that these victimizations tended to vary across the developmental process (Villodas et al., 2012). They purported an interesting evolution of susceptibility to revictimization, which illustrates the unique adverse effects the various forms of maltreatment

have on children. For example, they observed that, in middle to late childhood, the children who were both physically and emotionally abused had the highest rates of externalizing and internalizing problems. These changes in classification across the developmental stages of childhood highlight the amplified adverse effects of multiple types of maltreatment and can inform the formulation of clinical and preventive measures.

Additionally, research has shown that maltreated children exhibited a reduction in leveling­sharpening functioning, which is illustrative of a tendency toward avoiding external information (Rieder & Cicchetti, 1989). This is thought to be an attempt to adapt to their relatively dangerous environment to reduce the harm done to them (Schneider­Rosen et al., 1985; Speidel et al., 2022; Yang & Huang, 2024). However, its consequences are that maltreated children will be less adaptive and receptive in learning environments, which is associated with poor performance in various domains of development. Similarly, research that measured the event ­ related potentials (ERPs) of children when observing facial affects showed maltreated children had heightened amplitude when observing angry faces in two of the three measured occipital components (Curtis & Cicchetti, 2011). In contrast, nonmaltreated children had greater amplitude observing happy faces in one of the three occipital components. This suggests that maltreated children are more familiar with angry faces, whereas nonmaltreated children view angry faces as less familiar.

In a meta­analysis encompassing 35 studies and 11,344 participants, Gruhn and Compas (2020) assessed the effects of child maltreatment on coping and emotional regulation. They observed a consistent association of maltreatment with reduced emotion regulation, increased avoidance, emotional suppression, and emotional expression. Furthermore, they were able to assess shortcomings in the methodology of the field by highlighting the lack of clinical application as it pertains to the measurements assessing children’s coping strategies. However, three exceptions revealed significant effects on the strategy level, which included increased avoidance, emotional suppression, and emotional expression. This increased further understanding of the effects of child maltreatment on children and how maltreated children may respond to such stresses.

Peer Relationship

Research analyzing the most robust theoretical assessments of peer influence on child and adolescent development concluded that adolescents progressively use their peers as a base of support for social and emotional needs while also having their sense of self­concept influenced by peers (Brechwald & Prinstein, 2011). However, it is important to note that one of the key influences on children and

adolescents’ social interactions originates from their relationship with their parents (Hajal & Paley, 2020; Lee et al., 2016; Shinohara et al., 2012; Sroufe & Fleeson, 1986). This may function such that the child will form attitudes through their socializing with their parents and create expectations that fit said attitudes onto social interactions beyond the household.

Additional research has differentiated between same and other­sex peer acceptance, illustrating that social skills mediated the effect of emotion knowledge on both same­ and other­sex social preference (e.g., Mostow et al., 2002). Moreover, Mostow et al. (2002) observed that social skills and verbal ability were strongly associated with other­sex peer acceptance. Consequently, social skills (e.g., cooperation, assertion, and self­control) are the key ingredient in emotion knowledge and influence sex­based social preference. Regarding social skills and verbal ability being linked to other­sex peer acceptance, it could be that due to sex­based differences in interests and vocabulary, a heightened level of aptitude in conversing and prosocial behavior could bridge the other­sex peer acceptance gap.

A meta­analysis reviewing 233 studies concerning youth’s friendship experiences and quality with emotional adjustment along a longitudinal basis reports marginal but significant associations, particularly for the youngest youth (Schwartz ­ Mette et al., 2020). The researchers found that friendship experiences, particularly negative friendship quality (relative to number of friends and positive friendship quality), were more strongly associated with loneliness than depressive symptoms. SchwartzMette et al. (2020) went on to posit that the increased significance observed among the younger cohorts was perhaps a demonstration of autonomy­related differences, whereby the older adolescents have more voluntary social engagement outside of the school environment. This difference allowed for the older adolescents to reduce the proportionality of their negative friendship experiences by transcending the restrictive elements of classroom settings and freely choosing healthier relationships.

Peer Popularity

Although few studies have pertained to the causal relationship between child maltreatment and peer popularity, numerous studies have showcased correlations between maltreatment and poor peer relationship factors such as reduced social effectiveness, increased aggression, and increased peer rejection and victimization (Alink et al., 2012; Duprey et al., 2023; Martin­Babarro et al., 2021; Rogosch et al., 2009). Evidence of the pervasive adverse effects of child maltreatment on peer relationship presents itself by way of poorer emotion regulation (Gruhn & Compas, 2020; Kim & Cicchetti, 2010), increased rates of depressive symptoms and reduced

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self­esteem (Alto et al., 2018; Bolger et al., 1998), and increased internalizing and externalizing problems (Yoon et al., 2020; Yoon et al., 2021; Yoon et al., 2023).

A comprehensive analysis of the effects of maltreatment (episodic and chronic) on negative perception of peer relationship showed that relationship perception mediated the effect of maltreatment on adverse developmental outcomes (Ross et al., 2023). The results showed that adolescents who were maltreated recently and/ or early in their life had significantly higher levels of internalizing symptomatology and disruptive behavior relative to nonmaltreated adolescents. Additionally, maltreated adolescents reported higher levels of negative perceptions of themselves and peers within social contexts, compared to nonmaltreated adolescents. What is more, Ellis and Wolfe (2009) found that the motivation to be popular among peers exacerbated the negative effect of physical abuse on adolescent delinquent behavior. Along the same vein, adolescents surrounded by severely antisocial peers were more likely to exhibit risky behaviors (Yoon, 2020). The importance of peer popularity was further revealed by Yoon et al. (2018), who found that physically or sexually abused young adolescents exhibited increased internalizing and externalizing symptoms. These increased symptoms predicted lower peer popularity two years later. The lowered peer popularity in turn forecasted greater physical and sexual peer victimization at a later age. In sum, researching the contributory factors to peer popularity has the potential to uncover ways to end the cycle of maltreatment and improve the quality of life for children and adolescents.

Bronfenbrenner’s Bioecological Systems Theory

Taken within the context of Bronfenbrenner’s bioecological systems theory, which posits the existence of an interlinking environmental system that influences the individual ranging from closer systems (e.g., family, friends) to progressively more distant systems (e.g., school, neighborhood, societal norms, government), we observe an apt analysis of the happenings of children in their socioemotional development (Bronfenbrenner, 1977; Bronfenbrenner, 1979; Bronfenbrenner & Morris, 2006).

To be precise, the theory posits five interrelated systems: microsystem, mesosystem, exosystem, macrosystem, and chronosystem. For the purposes of this study, we predominately focused on the microsystem and mesosystem, wherein the microsystem encompasses direct influences on the child while the child is an active participant, such as the parents physically maltreating the child and perhaps the child acting out more, which may create a feedback loop. Whereas the mesosystem is effectively the interaction between different microsystems of the child’s life, conflicts such as the parents having a persistent

hostile disagreement with their neighbors may negatively influence the child’s perception of security, trust, and societal cohesion while perhaps amplifying maltreatment of the parent by way of redirecting frustrations onto the child intentionally or inadvertently.

For example, there is ample evidence that parent–child relationship and general household environment can influence children to form maladaptive social strategies or issues of self­esteem that may lead to the selection of social dynamics outside of the household that are associated with poorer socioemotional outcomes (Ayoub et al., 2014; Hoyne et al., 2022; Kim et al., 1999; Sroufe & Fleeson, 1986). Further, these maladaptive social strategies may differ depending on the type of maltreatment experienced, with children who were sexually abused disproportionally experiencing further sexual revictimization, trouble relating to others, substance abuse, and difficulty raising their own children, contributing to intergenerational trauma (Browne & Finkelhor, 1986; Fereidooni et al., 2024).

Various theories have posited the precise means by which parent–child relationships may influence peer relationships. One such suggests that the negative representations made by the maltreated children of their parents influence their mental structures, which contribute to poorer socioemotional development and peer relationship quality (Shields et al., 2001). Essentially, maltreated children will internalize parts of their deleterious parent–child relationship, form maladaptive mechanisms and poor emotional regulation, leading to social underperformance. Expanding upon that, Crittenden (1990) discussed the classification and practical utility of internal representation regarding parent–child relationships, suggesting a dual internal representational model that consisted of a factual and an affect foundation. Crittenden (1990) proposed that internal representations function as a form of cognitive empathy or theory of mind, insofar as it allows for one to predict an individual’s response to various acts and the meaning thereof. Specifically, nonmaltreating mothers exhibited functional forms of internal representations, such that they were fluid and open to new information and improvement. Maltreating mothers, on the other hand, exhibited more closed models that were rendered comparatively ineffective.

Family background (e.g., socioeconomic status, educational attainment) also contributes significantly to matters of social cognition, such as theory of mind, which may have implications for a host of societal phenomena insofar as the role of political, economic, and educational disparities contribute to variance within socioemotional development (Cutting & Dunn, 1999). Additional research has illustrated the influential

power of one’s neighborhood with results showing that the children categorized as having the most problems pertaining to behavioral and emotional problems also had the worst neighborhood quality (Martinez­Torteya et al., 2017).

Further research has implicated school environments in contributing to the socioemotional troubles faced by children, particularly those already facing issues like depression or maltreatment (Aguilar­Pardo et al., 2022; Cicchetti & Toth, 1998; Li et al., 2021; Ross et al., 2023). In particular, Aguilar­Pardo (2022) found classroom culture to be vital by demonstrating that the adverse effects of low peer likeability were moderated by the overarching culture of prosocial behavior, classroom connectivity, status norm of most visible peers’ norm for prosocial and aggression. In other words, a classroom culture that elevates prosocial norms results in peers of low likeability being significantly less likely to experience peer victimization relative to a low­prosocial class culture. Aguilar­Pardo (2022) also found that the more connected the classroom felt, the more amplified the classroom norms would be, such that a prosocial connective classroom would be more prosocial to low likable peers relative to a similarly prosocial classroom but with low connectivity. There is also evidence that the teacher–parent collaborative relationship significantly empowers the role of the parents and teachers in positively influencing the child’s socioemotional problems by creating a synergistic partnership based off respect for each other’s respective roles in raising the child (Harpaz & Grinshtain, 2020).

The Present Study

The current study aimed to elucidate the causal pathways by which child maltreatment can lead to varied socioemotional development in adolescence, specifically as it relates to peer popularity. Improved understanding of these causal pathways can assist professionals who work with maltreated children to design and implement effective treatment interventions and preventive strategies to combat the consequence of child maltreatment. Drawing upon the LONGSCAN database, we conducted a secondary data analysis study to investigate possible causal pathways from child maltreatment to peer popularity. We hypothesized that maltreated adolescents who experienced more parental abuse and neglect would have lower peer relationship quality and be perceived as less popular amongst peers two years later.

Method

Participants

The current research was a secondary data analysis of the LONGSCAN database. LONGSCAN is a consortium of research studies initiated in 1991 with grants from

the National Center on Child Abuse and Neglect. The coordinating center of LONGSCAN was at the University of North Carolina at Chapel Hill. There were also five data collection sites across the United States: East (EA), Midwest (MW), Northwest (NW), South (SO), and Southwest (SW), representing urban, suburban, and rural communities. The goal of LONGSCAN was to follow the more than 1,300 children and their families until the children themselves became young adults. The dataset used in this study was released in 2015, which included information on children from the beginning of the studies until the children turned 18. The samples of LONGSCAN was selected to represent varying levels of exposure to child maltreatment, but the vast majority came from impoverished backgrounds.

This research included 322 adolescents with complete data on the independent variables of parental abuse and neglect measured at the adolescents’ 12­year­old visit and the dependent variables of peer relationship and peer popularity measured at the adolescents’ 14­year­old visit. Of the participating adolescents, 50.5% were girls and 49.5% were boys. As for race/ethnicity, 53.6% were Black, 28.3% were White, 13% were mixed race, 3.8% were Hispanic, 0.7% were Asian, and 0.7% belonged in the “Other” category.

Materials and Procedure

The first measure was the Conflict Tactics Scale: Parent to Child Scale (Straus, 1979). The goal of this assessment was to measure the degree to which caregivers exhibit various modes of aggression/discipline towards their child in response to conflict. The scale for this assessment was an ordinal variable that ranged from 0 to 6 (0 = this has never happened, 1 = once in the past year, 2 = twice in the past year, 3 = 3–5 times in the past year, 4 = 6–10 times in the past year, 5 = 11–20 times in the past year, 6 = more than 20 times in the past year). Most of the questions out of the total of 22 pertained to the frequency of physical assault (differentiated by severity levels), psychological aggression, and nonviolent discipline frequency. For the purpose of this study, we only utilized questions that constituted aggression (both physical and psychological). Cronbach’s alpha for the two physical and psychological aggression items was .61. We operationalized a higher score on this scale as a higher level of child abuse.

The second measurement that we used in this study was the Revised Neglectful Behavior Scale, which was adapted from The Neglectful Scale (Straus et al., 1997). The goal of this measurement was to assess the neglect levels of parental behavior by adolescents’ self­reporting across four dimensions of neglect: Neglect of basic needs, lack of supervision, emotional, and educational neglect. The scale for this measurement ranged from 0 to 3 (0 = never, 1 = almost never, 2 = sometimes, 3 = a lot).

TABLE 1

Descriptive Statistics of

Three sample questions out of the total of 25 questions were as follows: “How often did your parent(s) have something for you to eat when you were hungry?”, “How often did your parent(s) take care of you when you were sick?”, and “How often did your parent(s) make sure you had somewhere safe to play?” (Runyan, 2014, p.11). Cronbach’s alpha for the 25 neglect items was .88. For ease of interpretation, we reversed coded the questions so that a higher score on this scale was operationalized as a higher level of child neglect.

TABLE 2

Empirical and Reproduced Correlations for the Initial Model and the Revised Model

Note. *Difference between reproduced and observed correlation is greater than .05.

TABLE 3

Summary of Causal Effects for the

Note. *Direct effect trends toward significance at the .10 level, one-tailed. ** Direct effect is significant at the .05 level, one-tailed. *** Direct effect is significant at the .001 level. Total effect may be incomplete due to unanalyzed components.

The third measurement was the Peer Relationships measure (LONGSCAN Investigators, 1998), and its goal was to record the self­reports of the participants’ own peer relationship. The scale for this measurement ranged from 1 to 4 (1 = almost no one, 2 = about half, 3 = most, 4 = almost all) We only used the first three questions as they were the most relevant. These questions were: “How many of the other kids at school are friendly toward you?”, “How many kids at school just ignore you?” and “How many of the other kids at school are unfriendly or mean to you?”. Cronbach’s alpha for the three peer relationship questions was .72. Reverse coding for the second and third questions was completed so that a higher score on this measurement was operationalized as better peer relationship.

The fourth and last measurement was Teacher’s Estimation of Child’s Peer Status scale. The goal of this measurement was to measure the teacher’s evaluation of the child’s peer status. The scale for this measurement ranged from 1 to 5 for questions 2 to 7 (1 = one of the kids with the most nominations, 2 = more than average, 3 = average/right in the middle, 4 = less than average, 5 = one of the kids with the fewest nominations) For the first question, the range was also 1 to 5 (1 = very well liked, 2 = above average liked, 3 = right in the middle, 4 = below average liked, 5 = liked very little). Three of the seven questions were chosen: Questions 1, 2, and 6. The first question was: “Overall, how much is this child liked by classmates?”. Question 2 was a nomination of the child for “play or work partner” and Question 6 was a nomination of the child for “good at leading others.” Cronbach’s alpha for the three peer popularity questions was .84. To facilitate interpretation, these questions were reverse coded such that higher score was operationalized as higher peer popularity.

Results

We predicted that young adolescents who experienced more parental abuse and neglect would have more peer relationship difficulties and be less popular with their peers two years later in middle adolescence. Table 1 presents descriptive statistics of our study variables. The data were analyzed using path analysis, a procedure

that required hand calculations in conjunction with the usage of SPSS version 29. After using SPSS to calculate observed/empirical correlations between the four study variables and run a series of regression analyses, we hand­calculated reproduced correlations for both the initial and the revised models to examine and compare model fit. Thus, a path analysis was conducted to determine the causal pathways among the variables of parental abuse (Abuse), parental neglect (Neglect), peer relationship quality (Peer Relationship), and popularity among peers (Peer Popularity). Prior to analysis, test assumptions were assessed by creating a scatterplot matrix and a residual plot. Both demonstrated fair linearity, normality, and homoscedasticity. The initial model (see Figure 1) was mostly consistent with the empirical data, except for one of the reproduced correlations exceeding a difference of .05 (see Table 2).

Tests of the missing paths in the initial model indicated that one additional path could improve the fit of the model: Peer Popularity on Neglect. Thus, a revised model was generated (see Figure 2). Reproduced correlations for the revised model were calculated and revealed an improvement of fit over the initial model: The revised model was consistent with the empirical data, with no reproduced correlation exceeding a difference of .05 (see Table 2). The direct, indirect, and total causal effects of the revised model are summarized in Table 3.

The outcome of primary interest was peer popularity, whose determinant with the largest total causal effect was peer relationship (β = .23), followed by child neglect (β = ­.13) and child abuse (β = ­.02). This model explained approximately 6.5% of the variance in peer popularity. The primary determinant of peer relationship was child neglect (β = ­.23), followed by child abuse (β = ­.09). Approximately 6.2% of the variance in peer relationship was explained by this model.

Discussion

The current study investigated the effects of child maltreatment on the socioemotional development in adolescence. We examined the causal pathways from child abuse and child neglect to peer popularity through peer relationship. Our findings supported the research hypothesis that adolescents who experienced higher levels of parental abuse and neglect would later report lower peer relationship quality and be perceived as less popular among peers. These findings were consistent with previous research showing significant correlations between child maltreatment and poor peer relationship and lower peer popularity (Cutting & Dunn, 1999; Gruhn & Compas, 2020; Kim & Cicchetti, 2010; Yoon et al., 2018). Our replication of past research is noteworthy given that we used multiple sources of information: The child abuse

variable came from parental report, the child neglect and peer relationship variables were derived from adolescent self­reports, and peer popularity was based on teacher ratings.

A more important contribution of our research was that we revealed contributory factors to peer popularity, a socioemotional development indicator that was not often researched in abused and/or neglected children and adolescents. Specifically, in addition to confirming child maltreatment’s damaging effect on peer relationship and peer popularity (e.g., Ellis & Wolfe, 2009; Yoon, 2020; Yoon et al., 2018), our research showed that child neglect had a direct causal path to both peer relationship and peer popularity, but child abuse only tended to have a direct causal path to peer relationship. Whereas it is known that relative to child abuse, child neglect is worse for children’s cognitive development (Tang, 2019), we have now additionally revealed child neglect’s increased negative impact on children’s socioemotional development, at least in terms of adolescent peer popularity. Furthermore, the literature regarding the adverse effects of school environment on socioemotional factors for maltreated children echoed our findings insofar

FIGURE 1

Path Diagram for the Initial Model (Peer Popularity), Including Path Coefficients

Note **Significant at the .05 level, one-tailed. ***Significant at the .001 level.

FIGURE 2

Path Diagram for the Revised Model (Peer Popularity), Including Path Coefficients

Note *Direct effect trends significant at the .10 level, one-tailed. ** Direct effect is significant at the .05 level, one-tailed. ***Significant at the .001 level. Revised path is shown with dashed arrow.

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as it highlights the fact that maltreated children and adolescents are prone to be victimized in school settings (Aguilar­ Pardo et al., 2022; Cicchetti & Toth, 1998; Li et al., 2021; Ross et al., 2023). Importantly, these findings highlight the crucial influence of maltreatment on maladaptive behaviors that lead to undesirable outcomes such as reduced sociability. These effects create a foothold during the formative years of childhood and adolescence, but the effects may linger into adulthood and cause cascading effects that further disadvantage those who were maltreated. Our findings on child neglect’s direct causal path to peer relationship and peer popularity showcased the need to pay attention to this less obvious form of child maltreatment. Indeed, we now have one more reason not to neglect child neglect. These results have implications for public policy and the helping profession as it illustrates the causal relationship between maltreatment and poorer performance in key areas of socioemotional development. Informing policymakers and practitioners and influencing the general zeitgeist to become more cognizant of the unique struggles faced by maltreated children and adolescents could mitigate some of the more deleterious effects by arming children and adolescents with proper skills to reverse the maladaptive behaviors. Assisting the maltreated children and adolescents by restructuring their internal system to better fit with the realities of the world beyond their domestic maltreatment is paramount in having them integrate and excel postmaltreatment.

Theoretical Implications

Our findings illustrate the robust efficacy of Bronfenbrenner’s bioecological systems theory in informing and predicting the socioemotional developmental impact of child maltreatment. At the microsystem level, parental abuse and neglect significantly negatively influenced children’s ability to form and maintain peer relationships, particularly within the classroom setting. Overall peer relationship quality deteriorated as a direct result of said maltreatment, thus implicating the mesosystem connection such that the microsystem of the parent–child relationship interacts negatively with their microsystem of their peer relationships thereby resulting in a case that is likely bidirectional, with the influence of the child maltreatment having predominant effect on peer relationships.

Limitations

There are two main limitations to the current study. First, our child abuse measure depended on the parent to report on any abusive treatment, which is likely to be underreported and might have contributed in part to the reduced correlations observed relative to our child

neglect measure, which was reported by the adolescent. Second, our peer relationship measure only included three questions, two of which were quite redundant. Also, the questions were solely about peer relationships at school, which might not be reflective of a child’s overall peer relationship experience when considering familial, neighborhood, and extracurricular contexts.

Future Directions

Future work could benefit from including more comprehensive and reliable measurements to assess maltreatment. For example, a more holistic approach could be taken to encompass impactful societal factors as laid out in Bronfenbrenner’s bioecological systems theory, such that the research becomes more cognizant and inclusive of the various factors that influence the children and adolescents at risk, particularly those from historically marginalized groups. Specifically, information pertaining to maltreated children’s neighborhood, city/town, police force presence/relationship, etc. could be included. Most extant research lacked these measures, and those that did, such as that by Martinez­Torteya and colleagues (2017), utilized a neighborhood quality measure that was considerably restrictive in its questioning and did not include potentially important factors. These factors could be whether the children and adolescents reside in a food desert, whether they lack key community building infrastructure (e.g., access to parks, bike paths, sports, entertainment, public transportation, walkability) and quality teacher, and whether their schools have high student­teacher ratio, among others. Additionally, it would be useful to implement more longitudinal studies following at­risk adolescents well into young adulthood while including groups that are treated with various interventions. It is critical that effective intervention programs are supported by scientific evidence. With continued research effort to add to the current study, we are hopeful that the problem of child maltreatment can be greatly reduced, and methods to ameliorate its deleterious effect will be much more effective.

References

Aguilar‐Pardo, D., Martínez‐Fernández, B., Colmenares, F., & Martín‐Babarro, J. (2022). Peer likeability and victimization in young adolescents: Moderating effects of descriptive and status group norms for aggression and prosocial behaviour and network density. British Journal of Educational Psychology, 92(3), 925–954. https://doi.org/10.1111/bjep.12481

Alink, L. R. A., Cicchetti, D., Kim, J., & Rogosch, F. A. (2012). Longitudinal associations among child maltreatment, social functioning, and cortisol regulation. Developmental Psychology, 48(1), 224–236. https://doi.org/10.1037/a0024892

Alto, M., Handley, E., Rogosch, F., Cicchetti, D., & Toth, S. (2018). Maternal relationship quality and peer social acceptance as mediators between child maltreatment and adolescent depressive symptoms: Gender differences. Journal of Adolescence, 63(1), 19–28. https://doi.org/10.1016/j.adolescence.2017.12.004

Ayoub, C. C., Bartlett, J. D., & Swartz, M. I. (2014). Parenting and early

intervention: The impact on children’s social and emotional skill development. In C. Cooper & S. H. Landry (Eds.), Wellbeing: A complete reference guide (pp. 1–32). Wiley-Blackwell.

Berzenski, S. R. (2018). Distinct emotion regulation skills explain psychopathology and problems in social relationships following childhood emotional abuse and neglect. Development and Psychopathology, 31(2), 483–496. https://doi.org/10.1017/s0954579418000020

Bolger, K. E., Patterson, C. J., & Kupersmidt, J. B. (1998). Peer relationships and self-esteem among children who have been maltreated. Child Development, 69(4), 1171–1197. https://doi.org/10.2307/1132368

Brechwald, W. A., & Prinstein, M. J. (2011). Beyond Homophily: A decade of advances in understanding peer influence processes. Journal of Research on Adolescence, 21(1), 166–179. https://doi.org/10.1111/j.1532-7795.2010.00721.x

Bronfenbrenner, U. (1977). Toward an experimental ecology of human development. American Psychologist, 32(7), 513–531. https://doi.org/10.1037/0003-066x.32.7.513

Bronfenbrenner, U. (1979). The ecology of human development. Harvard University Press.

Bronfenbrenner U., & Morris P. A. (2006). The bioecological model of human development. In W. Damon (Series Ed.) & R. M. Lerner (Vol. Ed.), Handbook of child psychology: Theoretical model of human development (pp. 793–828). John Wiley.

Browne, A., & Finkelhor, D. (1986). Impact of child sexual abuse: A review of the research. Psychological Bulletin, 99(1), 66–77. https://doi.org/10.1037/0033-2909.99.1.66

Cicchetti D., & Toth S. L. (1998). Perspectives on research and practice in developmental psychopathology. In W. Damon (Series Ed.), I. Sigel, & K. A. Renninger (Vol. Eds.), Handbook of child psychology: Vol. 4. Child psychology in practice (5th ed., pp. 479–583). Wiley

Cicchetti, D., & Toth, S., (2016). Child maltreatment and developmental psychopathology: A multilevel perspective (3rd ed.) In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology, Vol. 3. Wiley. Crittenden, P. M. (1988b). Family and dyadic patterns of functioning in maltreating families. In K. Browne, C. Davies, & P. Stratton (Eds.), Early prediction and prevention of child abuse (pp. 161–189). John Wiley & Sons. Curtis, W. J., & Cicchetti, D. (2011). Affective facial expression processing in young children who have experienced maltreatment during the first year of life: An event-related potential study. Development and Psychopathology, 23(2), 373–395. Https://doi.org/10.1017/s0954579411000125

Cutting, A. L., & Dunn, J. (1999). Theory of mind, emotion understanding, language, and family background: Individual differences and interrelations. Child Development, 70(4), 853–865. https://doi.org/10.1111/1467-8624.00061

Demers, L. A., Hunt, R. H., Cicchetti, D., Cohen-Gilbert, J. E., Rogosch, F. A., Toth, S. L., & Thomas, K. M. (2021). Impact of childhood maltreatment and resilience on behavioral and neural patterns of inhibitory control during emotional distraction. Development and Psychopathology, 34(4), 1–12. https://doi.org/10.1017/s0954579421000055

Dodge, K. A., & Pettit, G. S. (2003). A biopsychosocial model of the development of chronic conduct problems in adolescence. Developmental Psychology, 39(2), 349–371. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755613/ Duprey, E. B., Ross, A. J., Russotti, J., Handley, E. D., & Cicchetti, D. (2023). Interpersonal mechanisms between child maltreatment timing and young adult internalizing and externalizing symptomology. Journal of the American Academy of Child and Adolescent Psychiatry, 63(8), 813–824. https://doi.org/10.1016/j.jaac.2023.12.008

Ellis, W. E., & Wolfe, D. A. (2009). Understanding the association between maltreatment history and adolescent risk behavior by examining popularity motivations and peer group control. Journal of Youth and Adolescence, 38(9), 1253–1263. https://doi.org/10.1007/s10964-008-9318-3 English, D., Thompson, R., White, C. R., & Wilson, D. (2015). Why should child welfare pay more attention to emotional maltreatment? Children and Youth Services Review, 50, 53–63. https://doi.org/10.1016/j.childyouth.2015.01.010

Fereidooni, F., Daniels, J. K., & Lommen, M. J. J. (2024). Childhood maltreatment and revictimization: A systematic literature review. Trauma, Violence, & Abuse, 25(1), 291–305. https://doi.org/10.1177/15248380221150475

Gruhn, M. A., & Compas, B. E. (2020). Effects of maltreatment on coping and emotion regulation in childhood and adolescence: A meta-analytic review. Child Abuse & Neglect, 103, 104446. https://doi.org/10.1016/j.chiabu.2020.104446

Hajal, N. J., & Paley, B. (2020). Parental emotion and emotion regulation: A critical target of study for research and intervention to promote child emotion socialization. Developmental Psychology, 56(3), 403–417.

https://doi.org/10.1037/dev0000864

Harpaz, G., & Grinshtain, Y. (2020). Parent–teacher relations, parental selfefficacy, and parents’ help-seeking from teachers about children’s learning and socio-emotional problems. Education and Urban Society, 52(9), 1397–1416. https://doi.org/10.1177/0013124520915597

Hoyne, C. (2022). ABC’s and 123’s: The tole of the home learning environment in cognitive and socioemotional development in early childhood [Thesis, Mary Immaculate College]. https://dspace.mic.ul.ie/handle/10395/3039

Kim, J., & Cicchetti, D. (2010). Longitudinal pathways linking child maltreatment, emotion regulation, peer relations, and psychopathology. Journal of Child Psychology and Psychiatry, 51(6), 706–716. https://doi.org/10.1111/j.1469-7610.2009.02202.x

Kim, J. E., Hetherington, E. M., & Reiss, D. (1999). Associations among family relationships, antisocial peers, and adolescents’ externalizing behaviors: Gender and family type differences. Child Development, 70(5), 1209–1230. https://doi.org/10.1111/1467-8624.00088

Kim, H., Wildeman, C., Jonson-Reid, M., & Drake, B. (2017). Lifetime prevalence of investigating child maltreatment among US children. American Journal of Public Health, 107(2), 274–280. https://doi.org/10.2105/ajph.2016.303545

Kim-Spoon, J., Cicchetti, D., & Rogosch, F. A. (2012). A longitudinal study of emotion regulation, emotion lability-negativity, and internalizing symptomatology in maltreated and nonmaltreated children. Child Development, 84(2), 512–527. https://doi.org/10.1111/j.1467-8624.2012.01857.x

Lee, C.-T., Padilla-Walker, L. M., & Memmott-Elison, M. K. (2016). The role of parents and peers on adolescents’ prosocial behavior and substance use. Journal of Social and Personal Relationships, 34(7), 1053–1069. https://doi.org/10.1177/0265407516665928

Lewis, T., McElroy, E., Harlaar, N., & Runyan, D. (2016). Does the impact of child sexual abuse differ from maltreated but non-sexually abused children? A prospective examination of the impact of child sexual abuse on internalizing and externalizing behavior problems. Child Abuse & Neglect, 51, 31–40. https://doi.org/10.1016/j.chiabu.2015.11.016

Li, D., Ng, N., Chu, C. M., Oei, A., Chng, G., & Ruby, K. (2021). Child maltreatment and protective assets in the development of internalising and externalising problems: A study of youth offenders. Journal of Adolescence, 91(1), 35–47. https://doi.org/10.1016/j.adolescence.2021.07.002

Lindblom, J., Vänskä, M., Flykt, M., Tolvanen, A., Tiitinen, A., Tulppala, M., & Punamäki, R.-L. (2017). From early family systems to internalizing symptoms: The role of emotion regulation and peer relations. Journal of Family Psychology, 31(3), 316–326. https://doi.org/10.1037/fam0000260

LONGSCAN Investigators. (1998). LONGSCAN peer relationships [Instrument]. Chapel Hill, NC: University of North Carolina at Chapel Hill, Injury Prevention Research Center. http://longscan.researdh.unc.edu

Martín-Babarro, J., Toldos, M. P., Paredes-Becerra, L., Abregu-Crespo, R., FernándezSánchez, J., & Díaz-Caneja, C. M. (2021). Association of different forms of child maltreatment with peer victimization in Mexican children and adolescents. Frontiers in Psychology, 12 https://doi.org/10.3389/fpsyg.2021.662121

Martín-Babarro, J., Toldos, M. P., Paredes-Becerra, L., Abregu-Crespo, R., FernándezSánchez, J., & Díaz-Caneja, C. M. (2021). Association of different forms of child maltreatment with peer victimization in Mexican children and adolescents. Frontiers in Psychology, 12 https://doi.org/10.3389/fpsyg.2021.662121

Martinez-Torteya, C., Miller-Graff, L. E., Howell, K. H., & Figge, C. (2017). Profiles of adaptation among child victims of suspected maltreatment. Journal of Clinical Child and Adolescent Psychology, 46(6), 840–847. https://doi.org/10.1080/15374416.2015.1072822

Mostow, A. J., Izard, C. E., Fine, S., & Trentacosta, C. J. (2002). Modeling emotional, cognitive, and behavioral predictors of peer acceptance. Child Development, 73(6), 1775–1787. https://doi.org/10.1111/1467-8624.00505

Rieder, C., & Cicchetti, D. (1989). Organizational perspective on cognitive control functioning and cognitive affective balance in maltreated children. Developmental Psychology, 25(3), 382–393. https://doi.org/10.1037/0012-1649.25.3.382

Rogosch, F. A., Cicchetti, D., & Aber, J. L. (1995). The role of child maltreatment in early deviations in cognitive and affective processing abilities and later peer relationship problems. Development and Psychopathology, 7(4), 591–609. https://doi.org/10.1017/s0954579400006738

Ross, A. J., Handley, E. D., Toth, S. L., Manly, J. T., Cicchetti, D. (2023). The role of peer- and self-appraisals in the association between maltreatment and symptomatology. Research on Child and Adolescent Psychopathology, 51, 1289–1301. https://doi.org/10.1007/s10802-023-01083-8

Ross, A. J., Handley, E. D., Toth, S. L., & Cicchetti, D. (2023). Negative perceptions of Jennings and

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JOURNAL OF PSYCHOLOGICAL RESEARCH

peer relationships as mechanisms in the association between maltreatment timing and the development of psychopathology. Merrill-Palmer Quarterly, 69(1), 30–55. https://muse.jhu.edu/pub/27/article/909258/summary

Runyan, D., Dubowitz, H., English, D., Kotch, J., Litrownik, A., & Thompson, R. (2014). Longitudinal studies on child Abuse and neglect (LONGSCAN) ages 0-18, Version 1.4 [Dataset]. National Data Archive on Child Abuse and Neglect. https://doi.org/10.34681/CXPJ-5M96

Schneider-Rosen, K., Braunwald, K. G., Carlson, V., & Cicchetti, D. (1985). Current perspectives in tatachment theory: Illustration from the study of maltreated infants. Monographs of the Society for Research in Child Development, 50(1/2), 194–210. https://doi.org/10.2307/3333833

Schwartz-Mette, R. A., Shankman, J., Dueweke, A. R., Borowski, S., & Rose, A. J. (2020). Relations of friendship experiences with depressive symptoms and loneliness in childhood and adolescence: A meta-analytic review. Psychological Bulletin, 146(8), 664–700. https://doi.org/10.1037/bul0000239

Shields, A., Ryan, R. M., & Cicchetti, D. (2001). Narrative representations of caregivers and emotion dysregulation as predictors of maltreated children’s rejection by peers. Developmental Psychology, 37(3), 321–337. https://doi.org/10.1037/0012-1649.37.3.321

Shinohara, R., Sugisawa, Y., Tong, L., Tanaka, E., Watanabe, T., Onda, Y., Kawashima, Y., Hirano, M., Tomisaki, E., Mochizuki, Y., Morita, K., Amarsanaa, G., Yato, Y., Yamakawa, N., & Anme, T. (2012). Influence of maternal praise on developmental trajectories of early childhood social competence.” Creative Education, 3(4), 533–539. https://doi.org/10.4236/ce.2012.34081

Speidel, R., Behrens, B., Lawson, M., Mark Cummings, E., & Valentino, K. (2022). Latent classes in preschoolers’ internal working models of attachment and emotional security: Roles of family risk. Development and Psychopathology, 35(3), 1–18. https://doi.org/10.1017/s0954579422000293

Sroufe, L. A., & Fleeson, J. (1986). Attachment and the construction of relationships. In W. Hartup, & Z. Rubin (Eds.), Relationships and development (pp. 57–71). Lawrence Erlbaum Associates. Tang, C. M. (2019). Children and crime. Rowman & Littlefield.

U.S. Department of Health & Human Services, Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. (2024). Child Maltreatment 2022. https://www.acf.hhs.gov/cb/data-research/child-maltreatment

Villodas, M. T., Litrownik, A. J., Thompson, R., Roesch, S. C., English, D. J., Dubowitz, H., Kotch, J. B., & Runyan, D. K. (2012). Changes in youth’s

experiences of child maltreatment across developmental periods in the LONGSCAN consortium. Psychology of Violence, 2(4), 325–338. https://doi.org/10.1037/a0029829

Warmingham, J. M., Duprey, E. B., Handley, E. D., Rogosch, F. A., & Cicchetti, D. (2022). Patterns of childhood maltreatment predict emotion processing and regulation in emerging adulthood. Development and Psychopathology, 35(2), 1–16. https://doi.org/10.1017/s0954579422000025

Yang, L, & Huang, M. (2024). Childhood maltreatment and mentalizing capacity: A meta-analysis.” Child Abuse & Neglect, 149, 106623. https://doi.org/10.1016/j.chiabu.2023.106623

Yoon, D. (2020). Peer-relationship patterns and their association with types of child abuse and adolescent risk behaviors among youth at-risk of maltreatment. Journal of Adolescence, 80(1), 125–135. https://doi.org/10.1016/j.adolescence.2020.02.008

Yoon, D., Snyder, S. M., Yoon, S., & Coxe, K. A. (2020). Longitudinal association between deviant peer affiliation and externalizing behavior problems by types of child maltreatment. Child Abuse & Neglect, 109, 104759. https://doi.org/10.1016/j.chiabu.2020.104759

Yoon, D., Yoon, S., Park, J., & Yoon, M. (2018). A pernicious cycle: Finding the pathways from child maltreatment to adolescent peer victimization. Child Abuse & Neglect, 81, 139–148. https://doi.org/10.1016/j.chiabu.2018.04.024

Yoon, D., Yoon, S., Pei, F., & Ploss, A. (2021). The roles of child maltreatment types and peer relationships on behavior problems in early adolescence. Child Abuse & Neglect, 112, 104921. https://doi.org/10.1016/j.chiabu.2020.104921

Yoon, D., Yoon, M., Wang, X., & Robinson-Perez, A. A. (2023). A developmental cascade model of adolescent peer relationships, substance use, and psychopathological symptoms from child maltreatment. Child Abuse & Neglect, 137, 106054. https://doi.org/10.1016/j.chiabu.2023.106054

Author Note

Keith T. Jennings https://orcid.org/0009­0000­7728­2067

Connie M. Tang https://orcid.org/0000­0002­7647­6166 This study was a secondary data analysis project that pulled data from Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) database. We have no known conflict of interests to disclose.

Correspondence concerning this article should be addressed to Keith T. Jennings. Email: jennin40@go.stockton.edu

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