The Forgotten 90%: Adult Nonparticipation in Education

731810 research-article2017

AEQXXX10.1177/0741713617731810Adult Education QuarterlyPatterson

Article

The Forgotten 90%: Adult Nonparticipation in Education

Adult Education Quarterly 2018, Vol. 68(1) 41­–62 © The Author(s) 2017 Reprints and permissions: sagepub.com/journalsPermissions.nav https://doi.org/10.1177/0741713617731810 DOI: 10.1177/0741713617731810 journals.sagepub.com/home/aeq

Margaret Becker Patterson1

Abstract Despite a highly developed U.S. adult education system, 90% of adults aged 20 years and older considered the least educated did not participate recently in formal or nonformal education. What are nonparticipants’ characteristics, learning backgrounds, and skill levels? What predicts their likelihood of not participating in recent formal or nonformal education? The author analyzed 2012/2014 Program for the International Assessment of Adult Competencies–USA data. Situational deterrents of increasing age, parental education, low income, and work and family responsibilities contribute to nonparticipation. Dispositional deterrents include health and disability challenges, low social trust, and difficulties relating new ideas to real life. Institutional deterrents are education costs and little work schedule flexibility. Supports reported by nonparticipants are liking to learn new things, use of computers, and getting information from television and people they trust. Results from Program for the International Assessment of Adult Competencies–USA analyses inform adult and postsecondary educators and policy makers on what happened to—and how to reach—the forgotten 90%.

Keywords adult education, nonparticipation, recruitment, retention, postsecondary education, PIAAC, deterrents, skill levels, health, disabilities

Introduction In the wake of the 2008 U.S. recession, the need for adults to be prepared for familysustaining careers is acute (Reder, 2010). Carnevale, Smith, and Strohl (2013) project that two thirds of 54.8 million jobs the U.S. economy creates by 2020 will require 1Research

Allies for Lifelong Learning, Vienna, VA, USA

Corresponding Author: Margaret Patterson, Research Allies for Lifelong Learning, 2710 Chanbourne Way, Vienna, VA 22181, USA. Email: margaret@researchallies.org

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workers with some postsecondary education (PSE). To gain skills for these new jobs, career-ready adults can no longer end initial education with a secondary credential or less (“the least educated”; Zhang, Guison-Dowdy, Patterson, & Song, 2011).

The least educated include high school (HS) graduates with low skills or disabilities, HS early leavers, and immigrants (Patterson & Paulson, 2016). Nearly 29 million adults did not complete HS, according to the 2012 Program for the International Assessment of Adult Competencies–USA’s (PIAAC-USA) Survey of Adult Skills (Patterson & Paulson, 2016). Of those aged 25 to 65 years (i.e., beyond traditional PSE entry age), 10% participated in formal education in the year before PIAAC. Additionally, approximately one million annually leave HS early (“Diplomas Count 2013,” 2013) and an estimated seven million cannot read English well or at all (Patterson & Paulson, 2014).

Logical questions educators and policy makers might ask are as follows: What about the other 90%? What are nonparticipants’ backgrounds and which deterrents do they face—and what might trigger them to reengage in education? What are their skill levels? How do a propensity to graduate from HS and deterrent covariates predict their likelihood of not participating in recent formal or nonformal education? The author analyzes 2012/2014 PIAAC-USA data to address these questions.

Conceptual Framework and Literature Review Researchers know little about the forgotten 90%, since nonparticipants are seldom included in studies of PSE barriers (Quigley, 2006). Skill level is a factor; 2012 PIAAC-USA data indicate significantly large skill gaps between nonparticipants and participants, and gaps are widest for least educated adults (Patterson & Paulson, 2014).

In what ways do skill levels differ? In a review of national assessments of adult skills from 1985 through 2003, Smith (2009) pointed not only to stagnant literacy skills across time but also to skill differences by education attainment and age. Smith attributed stagnant skill levels in part to an aging population and increases in nonEnglish speaker immigration. More recently, initial 2012 PIAAC-USA findings indicate substantial differences by education attainment and age (Organization for Economic Cooperation and Development [OECD], 2013; Patterson & Paulson, 2014).

Increasing numbers at nontraditional ages, 25 years and older (Ryu, 2010) are in PSE. Least educated older adults pursuing PSE tend to persist at higher rates than young adults, even though their PSE graduation rates are minimal (Zhang et al., 2011). In 2012, PIAAC-USA, least educated nonparticipants are middle-aged, on average, with the majority having dependents. The least educated sometimes face deterrents associated with visual or hearing difficulties and tend to report a high rate of learning disabilities (Patterson & Paulson, 2014).

Another disparity is by income. Autor (2014) notes an earnings “inequality” between U.S. postsecondary and HS graduates has more than doubled in 30 years.

This inequality leads to “literacy classism,” in which least educated adults having the most “need to know” tend to be marginalized (Quigley, 2017). Those in poverty have the least access to learning (Patterson & Paulson, 2014).

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Figure 1.  Conceptual model.

A conceptual model for nonparticipation is displayed in Figure 1. The model begins with two pathways from initial education to a high-skilled U.S. workforce. In the first pathway, adult learners participate in education or workplace training; in the second, they become nonparticipants.

Often, adults have an incomplete initial education, in that they left school early (Zhang et al., 2011). Others completed initial education, yet find themselves in need of basic skills, English language skills, or HS equivalency credentials that permit further education. Approximately 10% with the need take the narrow path to adult education.

The forgotten 90% find themselves along a broader path facing numerous potential deterrents. Factors in adult nonparticipation can be divided into three clusters of deterrents: situational, institutional, and dispositional (Quigley, 2006). Situational deterrents result from circumstances as adults balance multiple roles in their lives (Reder, 1999; Ross-Gordon, 2011) or deal with health conditions or disabilities (Patterson, 2014). Needing child care, for example, can deter participation (McAnnaney, 2009; Patterson, 2014). Institutional deterrents occur when educational or employment procedures, policies, or practices prevent participation. Examples include lack of information, geographic inaccessibility, inconvenient course times, and prohibitive tuition rates. Dispositional deterrents refer to barriers involving learners’ self-perceptions and attitudes. Examples include low confidence, negative past schooling experiences, or fear of math (Quigley, 1997; Zhang et al., 2011).

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Employment can also contribute deterrents (Bergson-Shilcock, 2017; Patterson, 2013). Jobs lost in the 2008 recession affected least educated most and were frequently replaced with jobs requiring PSE in fields like health care (Carnevale et al., 2013).

Questions arose from 2012 PIAAC-USA findings as to whether not recognizing a connection between learning and career prospects deters participation (Kis & Field, 2013), or even contributes to a “vicious cycle” of minimal learning and fewer career opportunities (OECD, 2013; Patterson & Paulson, 2014).

Institutional barriers form a second cluster of deterrents. Postsecondary participation may never happen if adults cannot navigate enrollment, program selection, or financial aid processes (Fike & Fike, 2008). Potential participants, such as first-generation or immigrant learners, may cope with cultural stereotypes, immigration problems, and language barriers (Bergson-Shilcock, 2017; Spellman, 2007). Schleicher (2013) noted limited employer support for learning—either with PSE costs or with flexible work schedules. Furthermore, employer support is sorely lacking for least educated, who tend to need it most (Bergson-Shilcock, 2017; Patterson & Paulson, 2014).

Other adults face dispositional deterrents. They may not see themselves as being in the workforce or having career options. They may perceive their skills as enough to get by in life or may discount enhancing skills to keep their jobs (Smith, 2009). They may lack confidence or feel discouraged from pursuing education. For younger adults, entering PSE depends on the encouragement of parents (Patterson, 2014). Most nonparticipants in PIAAC-USA did not have strong parental PSE role models, and frequently, their parents may not have graduated from HS (Patterson & Paulson, 2014).

Lacking parental role models, they may turn to siblings, or even children, for encouragement and support (McAnnaney, 2009; Patterson, 2014).

To summarize, the least educated face numerous deterrents. To lessen earnings inequality (Autor, 2014), reduce literacy classism (Quigley, 2017), and remain economically vibrant, the United States needs to raise skill levels of the entire population (Carnevale et al., 2013; Reder, 2010), including the forgotten 90%.

Methodology To investigate nonparticipation of least educated adults, this article applies quantitative techniques to a large-scale sample, PIAAC-USA. The article investigates what happens to least educated nonparticipants—and how it happens.

Research Questions 1. In PIAAC-USA 2012/2014, how do the characteristics and backgrounds of least educated compare by participation status?

2. What are their assessed skill levels for literacy, numeracy, and problem-solving via technology rich environments (PSTRE)?

3. How do matched characteristics, backgrounds, and skill levels contribute to their propensity to graduate from HS? 4. How do propensity scores and deterrent covariates predict their likelihood of not participating in recent formal or nonformal education?

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Sample and Analyses PIAAC-USA 2012/2014 contains information on nonparticipants’ educational background, deterrents, and characteristics, along with skill levels in literacy, numeracy, and PSTRE. The PIAAC data set contains U.S. data from OECD’s international working-age adult survey and assessment of skills in 2012, as well as data from PIAAC’s 2014 U.S. National Supplement Household Study data released in late 2016. The original PIAAC-USA (2012) surveyed and assessed 5,010 adults ages 16 to 65 years; 2014 supplemental data extends the sample to more than 8,000 adults and includes key subgroups: unemployed adults (ages 16-65 year), young adults (ages 16-34 year), and older adults (ages 66-74 year).

PIAAC data collection relied on a complex sampling design to ensure representativeness of the population (Hogan et al., 2016). Adults took surveys and assessments on laptop computers. They completed both an extensive Background Questionnaire and assessments in literacy, numeracy, and PSTRE. Assessment scores were estimated using 10 plausible values per content domain. Scores ranged from 0 to 500 and were classified into one of five levels. Levels for literacy and numeracy were as follows: below Level 1 (0-175), Level 1 (176-225), Level 2 (226-275), Level 3 (276-325), and Levels 4/5 (326-500). Levels for PSTRE were below Level 1 (0-240), Level 1 (241290), Level 2 (291-340), and Level 3 (341-500; Hogan et al., 2016).

PIAAC files were assembled from public-use files that perturbed and categorized individual data for confidentiality. Weights were applied so that each respondent represented an accurate proportion of 203 million adults and standard errors would reflect variability estimated in that population. More detail on sampling, weighting, background questionnaire administration, and assessments is available in the PIAAC technical report (Hogan et al., 2016).

PIAAC data were analyzed in IDB Analyzer software (available for download through the PIAAC Gateway website), SPSS 23, and R software. IDB Analyzer software allowed for means comparison of plausible values for literacy, numeracy, and PSTRE assessments, and for categorical analyses (primarily cross-tabulations). The OECD International Data Explorer was used to calculate mean score differences in literacy, numeracy, and PSTRE assessments. The reference group in all analyses, unless otherwise stated, is adults ages 20 to 74 years with low skills who indicated learning formally or nonformally in the year before PIAAC. To determine if group differences were practically meaningful, effect sizes were calculated with a 95% confidence threshold of twice the standard error for percentage differences, and Cohen’s d for mean differences.

Nonparticipants (n = 2,214) pursued no formal education in the 12 months (FE12) or nonformal education in the 12 months (NFE12) prior to PIAAC participation (FE12 = 0 and NFE12 = 0). Adults are further filtered by age (20 to 74 years, in 5-year increments, to exclude young adult age groups traditionally in school) and to two initial education levels: those with less than HS (LHS, i.e., ISCED ≤ 1, 1, or 2) as highest education completed, and those graduating HS (i.e., ISCED = 3). Although adults may have taken postsecondary coursework or nonformal education previously, they did not do so in the year before PIAAC and had no postsecondary credential, diploma, or degree. The comparison group (n = 1,894) consists of adults who reported pursuing formal or nonformal education

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in the year before PIAAC participation (i.e., FE12 = 1 or NFE12 = 1), were ages 20 to 74 years, and were in either initial education level. Further PIAAC-USA variables representing deterrents and activities are described in the Findings section.

To address the third research question, data were matched and analyzed. The author analyzed available predictors using a propensity score analysis (PSA) approach (Bai, 2015; Hahs-Vaughn, 2017; Schneider, Carnoy, Kilpatrick, Schmidt, & Shavelson, 2007). PSA is useful in reducing selection bias when comparing group differences in situations in which randomized control trials are not feasible (Bai, 2015). Through matching, PSA seeks to balance the distributions of observed covariates between the proposed “treatment” group (in this article, HS graduates in the PIAAC-USA sample) and “control” group (i.e., LHS adults) as if adults had been randomly assigned to either condition. Variables for PSA matching were numeracy skills (the sum of 10 plausible values for assessed numeracy) and those associating with HS graduation: age group (5-year increments), gender, annual income (in quintiles), highest parental education, residence density (urban or not), and U.S. region (southern or not). Scores were matched with replacement using Nearest Neighbor procedures in the MatchIt package of R, with a 2:1 matching ratio to improve match capability, and applied PIAAC sampling weight (Hahs-Vaughn, 2017; Randolph, Falbe, Manuel, & Balloun, 2014). All HS adults and all but 75 LHS adults were successfully matched (n = 4,033). The PIAAC sampling weight was applied both in matching and in logistic regression analysis to acknowledge PIAAC’s complex sampling design and enhance accuracy of inferences (Hahs-Vaughn, 2015). Replicate weights were also employed in logistic regression analysis. Once scores were matched, PSA models were statistically evaluated by percentage of bias reduction and inspection of absolute standardized differences in means.

Propensity scores and individual covariates were entered into logistic regression analyses to predict nonparticipation (vs. other status). Covariates include propensity to complete HS, monthly earnings (in deciles), age group (20-74 years), health status, employment status, gender, how adults primarily got information on current events (i.e., from the Internet, from television, or from family/friends/coworkers), level of incomplete education, weekly hours worked, flexibility in work scheduling, learning strategies, measures of social trust, U.S. region (southern or not), residence density (urban or not), primary reasons for not taking nonformal education, computer use (ever used a computer vs. not), and presence of hearing, visual, or learning difficulties. p Values were set at .10 and Wald statistics greater than one were employed to permit a maximum number of predictors.

Odds ratios were calculated as effect sizes to identify practical significance.

Descriptive Findings In response to the first research question, one in four nonparticipating adults had an initial education level of LHS (26%); three in four completed HS (74%). Initial education rates for adults in a comparison group (i.e., 20- to 74-year-old adults without postsecondary degrees that did participate in learning) indicate 9 in 10 completed HS (89%) and 1 in 10 were LHS (11%).

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Demographics and Degree Completion Among nonparticipants, Southern United States was overrepresented, and the Midwestern and Western regions were underrepresented. Regionally, 44% of nonparticipants live in the South; approximately 17% live in the Northeastern United States, 20% in the Midwest, and 19% in the Western United States. Approximately one third each live in cities or rural areas, one fourth in suburbs, and one tenth in towns.

Residence density of nonparticipants varies by region, however. Half of nonparticipants in the Midwest and 40% of nonparticipants in the South live in rural areas. In the West, nearly 9 in 10 nonparticipants live in a city (46%) or a suburb (41%).

In the comparison group, proportionately fewer adults were from the South (37%), about the same were from the Northeastern United States (15%), and proportionately more were from the Midwest (24%) and West (25%). Residence density is similar for the comparison group, with the exception that Midwestern and Southern rural proportions are smaller than for nonparticipants. The Western percentages of city residents (41%) and suburban residents (46%) are similar for the comparison group.

Age of nonparticipants (20-74 years) and the comparison group in 10-year increments is displayed in Figure 2. Nonparticipants are significantly older than the comparison group, d = .74. Proportions of nonparticipants and comparison group who are middle-aged (i.e., ages 30-59 years) are similar. Nonparticipants, however, have a smaller percentage of young adults, aged 20 to 29 years (11%), than the comparison group (36%) and a higher proportion of older adults, aged 60 to 74 years (30%), than the comparison group (12%).

Both nonparticipants and the comparison group have approximately half men and half women overall. The proportions of nonparticipant men and women tend to stay balanced with advancing age, yet in the comparison group the proportion of women gradually increases with age. A middle-aged group of nonparticipants is an exception. In their early 50s, LHS nonparticipants are more often male (61%) than female. The LHS men in their early 50s also had high rates of unemployment (36%) and disability (19%). When asked why they did not participate in nonformal education, no prominent reason was given.

A total of 3 in 10 nonparticipants (30%) report not completing a degree program which they started (regardless of degree type), less than in the comparison group (47%). Unfinished degree types for nonparticipants are presented in Figure 3. Nearly half of nonparticipants not completing a program left a postsecondary certificate or diploma unfinished (45%). One in five left an associate degree unfinished (18%). In the comparison group, 41% did not complete a postsecondary certificate or diploma.

However, the comparison group had a lower rate of leaving HS early (7%) and higher rates of leaving more advanced degrees unfinished (23% left an associate degree and 29% left a bachelor degree or higher program incomplete).

Deterrents to Nonformal Education In the year before PIAAC, 18% of nonparticipants wanted to pursue nonformal education but did not. Three top reasons they gave, as displayed in Figure 4, were cost, work obligations, and family responsibilities.

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Comparison Group

36

18

18

18

12

Percent in Age Group 11

Nonpar cipants

0

20-29 Years

17

20

30-39 Years

20

40

40-49 Years

23

60

30

80

50-59 Years

100

60-74 Years

Figure 2. Age distribution of nonparticipants in learning, PIAAC (2012/2014).

Note. PIAAC = Program for the International Assessment of Adult Competencies.

Figure 3. Unfinished degree type percentages of nonparticipants, PIAAC (2012/2014).

Note. PSE = postsecondary education; PIAAC = Program for the International Assessment of Adult Competencies.

When disaggregated by education attainment, nonparticipants indicate low interest in nonformal education—15% of LHS and 20% of HS nonparticipants did so. LHS adults who indicated interest cite family responsibilities as a reason for not participating (25%) at a proportionately higher percentage than HS adults (17%). HS nonparticipants see cost as a reason at a higher rate (25%) than LHS nonparticipants (18%). Both groups were equally “too busy at work” (19%).

In contrast, 37% of the comparison group, or twice the rate of nonparticipants, wanted to pursue nonformal education the year before and did not, and this percentage did not differ by LHS/HS status. Although cost concerned both groups, nonparticipants emphasize family responsibilities more (19%) and the comparison group emphasizes work responsibilities more (30%) as reasons for not learning nonformally (see Figure 4).

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Figure 4. Top reasons for not pursuing nonformal education, PIAAC (2012/2014).

Note. PIAAC = Program for the International Assessment of Adult Competencies.

Families More than three fourths of nonparticipants (77%) have a partner or spouse, at a significantly higher rate than the comparison group (63%). Nonparticipants live in households of three people (M = 3.0, SD = 1.5), and half of households with children have no more than two children (18% have one child and 33% have two). Rates are similar in the comparison group: mean household number is 3.2 (SD = 1.4), 21% have one child, and 33% have two children. Median annual income level is similar for both groups; approximately 60% of nonparticipants and 56% of the comparison group have an annual income at or below the median, which represents 200% of poverty.

Four in five nonparticipants are born in the United States (82%) and are native English speakers (80%). Comparison group rates are significantly higher, 87% born in United States and 84% native speakers. A total of 19% of nonparticipants are firstgeneration immigrants, a significantly higher rate than 13% in comparison group.

Nonparticipant parents’ backgrounds model education positively (see Figure 5); nearly half (49%) have a father or a mother who graduated from HS and completed some PSE or earned a college degree. Still these rates are much lower than those of the comparison group, 71% for mothers and 70% for fathers.

Figure 5 also shows approximately two in five nonparticipants have neither a father or mother who completed HS. As seen in Figure 6, this tendency is largely generational. Of nonparticipants whose parents did not complete HS, 61% were at or above the median age (50-54 years). Approximately two thirds (66%) of nonparticipants with at least one parent having a postsecondary degree were younger than the median age.

Health and Disabilities Nonparticipating adults claim a median “good” health, yet 30% report health challenges (i.e., “fair” or “poor” health). The median health status for the comparison group is “very

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0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% neither parent completed high school

19%

37%

father completed high school and some PSE

40%

mother completed high school and some PSE mother completed bachelor degree father completed bachelor degree Nonpar cipants

39%

9% 11%

48% 49%

22% 22%

Comparison Group

Figure 5. Parents’ education background, PIAAC (2012/2014).

Note. PSE = postsecondary education; PIAAC = Program for the International Assessment of Adult Competencies.

Figure 6. Percentage of adults younger than age 45 by their parent’s highest education level, PIAAC (2012/2014).

Note. LHS = less than high school; HS = high school; PSE = postsecondary education; PIAAC = Program for the International Assessment of Adult Competencies.

good,” and only 15% report health challenges. The incidence of nonparticipant disabilities is 15% hearing difficulties, 20% vision difficulties, and 10% learning disabilities. In comparison group, rates of vision and hearing difficulties (9% and 11%, respectively) are significantly lower, yet learning disabilities rates are comparable (9%).

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Patterson Table 1.  Ranked Industry Classifications of Nonparticipants’ Employers, PIAAC (2012/2014).

Industry 1. Construction 2. Retail 3. Food and beverage service 4. Building and landscape service 5. Health care 6. Motor vehicle trade and repair 7. Education 8. Public administration and security

Nonparticipants (%)

Comparison group (%)

6 5 4 4 2 2 1 1

5 8 7 3 5 2 3 6

Note. PIAAC = Program for the International Assessment of Adult Competencies.

Of those who report not working, one third (33%) do not pursue work because of long-term illness (the top reason). Another 4% are temporarily ill, bringing the total percentage of unemployed adults affected by health to 37%, higher than the 15% comparison group rate. Illness particularly affects the ability of middle-aged adults to find work; many reporting long-term illness (43%) are 35 to 54 years old and 39% are 55 to 65 years.

Employment About half of nonparticipants are employed (53%) at a substantially lower rate than comparison group, 81%. Predominantly employed in the private sector (88%), nonparticipants tend to work in small businesses employing 50 or fewer (59%) rather than in large companies (250+ employees; 18%). In contrast, 76% of comparison group works in the private sector, 55% in small businesses, and 22% in large companies (250+ employees). Indeed, 18% of nonparticipants work in skilled occupations, lower than the 32% of comparison group members doing so. In semiskilled occupations, 33% work in white-collar occupations (vs. 39% in comparison group), and 32% in bluecollar occupations (higher than the 19% in comparison group). The top eight industries employing them are presented in Table 1; approximately one fourth of nonparticipants and two fifths of comparison group work in these industries. Nonparticipants work most often in construction or retail, whereas comparison group members tend to work most often in retail, food and beverage service, or public administration and security.

While the top occupation for both groups is sales workers, in nonparticipants’ top five occupations are (in rank order) personal service workers, building and related trades, metal and machinery trades, and drivers and mobile plant operators. The corresponding occupations for the comparison group, after sales workers, are personal care workers, business and administration associate professionals, and drivers and mobile plant operators.

Three fourths of nonparticipants (78%) and comparison group (76%) are satisfied with their current job. Nonparticipants work an average 40 hours per week, and 27%

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Figure 7.  Nonparticipant hours worked, PIAAC (2012/2014).

Note. PIAAC = Program for the International Assessment of Adult Competencies.

works more than 40 hours per week (see Figure 7). Rates for the comparison group are similar.

One in five (21%) nonparticipants are self-employed, significantly more often than adults in comparison group (11%). Managerial responsibilities for nonparticipants are minimal; 24% manage other employees (vs. 30% in comparison group).

Learning at Work Two thirds of employed nonparticipants have at least some flexibility in how they sequence work tasks (67%) and accomplish their work (66%). Three fourths have some flexibility in pacing the speed of work (75%). As in comparison group, about half (46%) have some flexibility in how work hours are structured, while 54% does not. The comparison group has significantly more flexibility in sequencing work tasks (74%), accomplishing work (75%), and in pacing the speed of work (78%).

When working, nonparticipants report a median of weekly learning by doing.

However, they learn from their coworkers or supervisors and learn to keep up to date a median of monthly. Learning at work tends to occur more frequently in comparison group; the median for learning in all three areas is weekly.

Unemployment For nonparticipants, rates of unemployment (12% vs. 8% in comparison group) and permanent disability are high (13% vs. 2% for comparison group). More than four fifths of unemployed nonparticipants give one of three reasons for not seeking work (see Figure 8): long-term illness (33%), caring for family (17%), and retirement (37%, 97% at age 55 or later). Nearly all nonparticipants not seeking work because they care for family are women (95%), and 39% are aged 45 to 65 years, implying many may

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Nonpar cipants

Figure 8. Top reasons unemployed adults gave for not looking for work, PIAAC (2012/2014).

Note. PIAAC = Program for the International Assessment of Adult Competencies.

serve as caregivers for multiple generations. The comparison group’s reasons for not seeking work, with much less long-term illness, are as follows: being a student (10%), caring for family (19%), and long-term illness (13%).

Learning Strategies Virtually all nonparticipants (90% to 92%), regardless of employment status, believe they could understand and speak English well or very well. Additionally, 87% believe they could read English and 83% write English well or very well. They report positively on learning strategies. Approximately two thirds report a “high extent” of liking to learn new things (64%) and looking for additional information (66%), as shown in Figure 9. In comparison group, rates are even higher: 82% like to learn new things to a high extent and 80% to look for additional information. Nonparticipants find it more problematic to determine how different ideas fit together (49% could figure it out to a high extent) than does the comparison group (64% to a high extent). More problematic for nonparticipants is relating new ideas into real life (24% to a high extent).

Computer Use Computer use is high; 73% of nonparticipants report ever having used a computer before. The rate of computer use climbs to 95% for adults younger than 25 years, and is more than 65% for adults 55 years and older. In comparison group, 91% has ever

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Nonpar cipants

Figure 9. Learning strategies, used to a high extent, of nonparticipants and comparison group, PIAAC (2012/2014).

Note. PIAAC = Program for the International Assessment of Adult Competencies.

used a computer, including more than three fourths of those 55 years and older; both rates are significantly higher.

Of nonparticipants using the Internet outside of work, 90% use e-mail at least sometimes and 54% e-mail daily (respective rates for e-mail use and daily e-mail in comparison group are 95% and 66%). Hence 89% of nonparticipants go online at least sometimes to better understand health or financial issues (vs. 94% of comparison group), and 73% to conduct financial transactions (less than 83% of comparison group members doing so). Only 31% of nonparticipants get online at least sometimes to meet virtually, compared with half (47%) of comparison group members. All rates for nonparticipants are significantly lower than for comparison group.

Community Involvement and Social Trust PIAAC survey data also offer insights into nonparticipants’ infrequent community involvement and low levels of social trust. Most nonparticipants (67%) do not volunteer in nonprofit organizations, charities, or political parties. Three fourths (78%) trust only a few people completely. Four in five (83%) agree if they are not careful, other people take advantage of them. Nearly half (48%) agree that “people like me don’t have a say” in what the government does. Interestingly, 34% disagree with the same

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statement. Older nonparticipants tend to be more polarized as to whether they have a say, and younger ones more neutral, neither agreeing nor disagreeing.

A greater proportion of comparison group, on the other hand, tends to volunteer (27% regularly and 27% less than monthly). They are significantly more trusting than nonparticipants—71% of adults in the comparison group trust only a few people completely. Still the comparison group believes others take advantage (80%) at approximately the same rate as for nonparticipants. Two fifths (42%) disagree that they have no say in government, but like nonparticipants, younger adults in the comparison group tend to be more neutral.

When asked where they get information about current events, three fifths of nonparticipants receive it “a lot” from television (60%) and one third online (34%); for the comparison group, television rates are lower (52%) and online rates are much higher (58%).

A fourth of nonparticipants receive information a lot from family, friends, or coworkers (22%) or radio (22%); rates for the comparison group are higher (28% and 26%, respectively). Overall, family, friends, or coworkers, along with television, are the most frequent sources of current events information (“a lot” or “some”), with 94% of nonparticipants and adults in comparison group getting at least some information from these sources.

Assessed Skills in Literacy, Numeracy, and PSTRE The second research question considers differences in assessed skill levels for both groups. Adults were assessed in three skill domains: literacy, numeracy, and PSTRE. As displayed in Figure 10, on average adults score at Level 2, except for nonparticipants who were assessed in numeracy. At Literacy Level 2, respondents were tasked with matching text and information, paraphrasing, and making low-level inferences.

At Numeracy Level 2, PIAAC respondents could respond to mathematical content in common contexts and apply two or more steps to solving math problems (Patterson & Paulson, 2016). Nonparticipants score at Level 1 in numeracy, on average. For both groups, average numeracy scores are lower than those in literacy and PSTRE. Significant gaps occur in mean scores between groups, as shown in Figure 10.

Although literacy and PSTRE skill levels average at Level 2, these average scores are near the bottom of the level, indicating that many nonparticipants score lower. The magnitude of score differences is moderate for all three domains.

Logistic Regression Findings Propensity scores were developed as described in the methodology section, with all but 75 cases (n = 4,033) successfully matched on HS completion, assessed numeracy skills, and several demographic and background variables. Propensity scores were then entered into a logistic regression model to predict likelihood of nonparticipation.

Table 2 presents the most important predictors of nonparticipation, along with odds ratios to estimate likelihood of nonparticipation. Predictors with negative beta weights and odds ratios below 1.0 exhibit reduced likelihood of nonparticipation; predictors with positive beta weights and odds ratios above 1.0 exhibit increased likelihood of

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Nonpar cipants

Figure 10.  Assessed skills in literacy, numeracy, and PSTRE of nonparticipants and comparison group, PIAAC (2012/2014).

Note. PSTRE = problem-solving via technology rich environments; PIAAC = Program for the International Assessment of Adult Competencies.

nonparticipation. The Nagelkerke pseudo R2 is low, indicating that additional predictors are needed to more fully explain the likelihood of nonparticipation.

As presented in Table 2, the strongest predictor is the propensity to complete HS, as based on assessed numeracy levels and demographic/background variables.

Nonparticipation in education of adults with high propensity to graduate is approximately six times less likely than for adults with low propensity. Other important negative predictors are health status and ability to relate new ideas to real life. Being able to relate new ideas to real life reduces nonparticipation in education; with greater extent of this learning strategy, the likelihood of nonparticipation decreases by 34%. Incrementally for decreasing health (e.g., good to fair, fair to poor), the likelihood of nonparticipation decreases by 31%. A lesser predictor is a measure of social trust: The more adults disagree that others take advantage of them, the more nonparticipation likelihood is reduced.

Strong positive predictors of nonparticipation include work reasons and gaining information online. Adults who report being too busy at work to take nonformal education have a 45% greater likelihood of nonparticipation. The likelihood of nonparticipation increases by approximately 31% for those who tend to primarily get information online in contrast with other sources.

Two lesser positive predictors are age group and work schedule flexibility. The likelihood of nonparticipation increases 13% for each increment of age (16-19 years to 20-24 years, 20-24 years to 25-29 years, etc.). Those with more work schedule flexibility have an 11% greater likelihood of nonparticipation per increment.

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Patterson Table 2. Important Predictors From Logistic Regression of Nonparticipation, PIAAC (2012/2014).

Predictor High school completion propensity Health status (excellent to poor) Incomplete education level Learning strategy: can relate new ideas to real life Monthly income (deciles) Social trust: others take advantage if not careful Age group Primarily gains information about current events from the Internet Work schedule flexibility Being too busy at work was reason for no nonformal education last year Constant Pseudo R2

Standard error

Wald statistic

p

Odds ratio

−1.8

0.8

4.55

<.05

0.17

−0.3 −0.1 −0.3

0.1 0.1 0.1

5.23 3.51 6.63

<.05 <.10 <.01

0.77 0.88 0.75

−0.1 −0.1

0.2 0.1

2.35 1.51

>.10 >.10

0.92 0.87

0.1 0.3

0.0 0.1

7.51 5.75

<.01 <.05

1.13 1.31

0.1 0.4

0.1 0.3

1.50 1.44

>.10 >.10

1.11 1.45

2.1 0.20

1.7

1.49

>.10

7.67

b

Note. PIAAC = Program for the International Assessment of Adult Competencies.

Discussion To summarize major findings, nonparticipation is associated with both deterrents and supports. Most deterrents identified in PIAAC are situational: increasing age, variable role models from parental education, low income, work responsibilities, and family responsibilities all appear to contribute to nonparticipation. Dispositional deterrents from PIAAC include lack of interest in education, health and disability challenges, low social trust, and difficulties in relating new ideas to real life. Two institutional deterrents are cost of education and little work schedule flexibility. Nonparticipants also experience lower assessed skills, particularly in numeracy. Supports nonparticipants report are liking to learn new things, use of computers, and getting information from television and trusted people in their lives.

A first situational deterrent is age. Moreover, 70% of nonparticipants are younger than 60 years and could be recruited to education if interested or in the workforce. Many educators may assume that middle-aged adults do not want or need further education, but as the number of adults 25 and older entering PSE rises (Ryu, 2010), that assumption needs challenged. A sizable percentage of adults do not have family role models for pursuing further education and may not know how to do so or what advantages may result. Adult and PSE programs may need to supply this information (Patterson, 2014; Patterson & Paulson, 2016) and to offer examples where participation has been successful.

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Deterrents affect least educated adults in middle age. Where nonparticipant men in their early 50s have fewer HS diplomas and higher rates of unemployment and disability, they could be recruited through health care professionals, vocational rehabilitation agencies, or disabilities service providers. For the unemployed, recruiting methods offering career pathways could be fruitful and perhaps less intimidating than starting PSE directly.

Other situational deterrents are being “too busy at work” and, to a lesser extent, family caretaking. How can adult education programs and nonformal education programs accommodate the needs of adults who work full-time, have family responsibilities, and have limited means? How can educators help adults and employers to see relevant connections between learning and work—and ways learning can support their careers? Communication among employers and educators is key to reducing deterrents. As Bergson-Shilcock (2017) notes, employers need to recognize implications of supporting employees’ skill building, either through support of nonformal education opportunities or through release time to gain external instruction.

The incidence of fair and poor health is a strong concern for nonparticipants. How can educators reach adults who may struggle with long-term illness and cannot work?

One approach might be to offer further learning to prevent skills from eroding until they return to work. Education programs could support needs of prospective participants with health challenges in several ways. Recruitment might need to occur through doctors, dentists, and health clinics. This recruitment involves more than simply leaving brochures in a doctor’s waiting room, rather actively building relationships with local medical professionals who recognize what the program offers and can quickly refer adults who struggle with health literacy or numeracy. Flexible scheduling may be required across time, and flexibility with deadlines for assignments could also be necessary. Participants with health care or disability issues—as well as family caregivers—may also require individualized support and instruction. Flexibility may be even more beneficial to middle-aged adults dealing with long-term illness.

The top occupations of nonparticipants—personal service workers, building trades, metal and machinery trades, and drivers and mobile plant operators—offer clues as to where to recruit them. Adult and PSE programs not already doing so could foster relationships with chambers of commerce, small businesses, business development centers, and local trade associations or unions in these occupational areas. Industry partnerships, including multiple firms and education partners, could collaborate to identify needs, determine available training resources, and develop career pathways for adults (Bergson-Shilcock, 2017). As with other growing relationships, adult educators will need to cultivate patience in developing new connections and willingness to learn more about occupations and potential participants from them.

Institutional deterrents are costs of education and inflexible work schedules. Do nonparticipants realize adult education programs have minimal or no cost? Even PSE programs may cost less than expected, and understanding the difference between grants and loans is necessary as they consider costs and learn to navigate financial aid processes (Fike & Fike, 2008).

As well as recruiting prospective learners, adult or postsecondary educators could also convince businesses of the advantages of flexible scheduling for their employees

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(Bergson-Shilcock, 2017). Flexible work hours can promote learning, especially “learning by doing” that nonparticipants are already somewhat comfortable with and that would directly benefit the company. The high rate of self-employment among nonparticipants also offers insights for recruitment, in that programs could point out benefits of additional skills for leading and growing businesses.

Since most adults report liking to learn new things and to look for new information, programs could offer occasional open house events on topics of community interest or to adults in certain occupations. An added bonus would be in appealing to adults’ curiosity and explaining how they could use new information to their advantage in real life applications. In recruitment materials, adult educators can also capitalize on adults liking to learn new things. Programs may believe their materials already do so, but the best way to reevaluate materials would be to ask nonparticipating adults with low skills from the community for their reactions—these could be friends, relatives, or neighbors of current adult learners.

In the current decade, educators can no longer argue that adults are not online.

Nearly all adults are getting online, whether on computers or smartphones, for more information. Are programs taking full advantage of those interactions to recruit new learners? Also, programs need to investigate how adult education can be delivered through online learning—what are the most effective methods and how can potential adult learners be encouraged to engage in these opportunities? Another medium that nonparticipants prefer is television. Do adult and postsecondary educators take full advantage of television services as a medium for recruiting and serving adult learners?

Another area of concern is lack of community involvement and of social trust.

Since lack of trust is high, educators should consider expanding word of mouth to recruit adults to education. Even though many nonparticipants believe others take advantage of them, they tend to place faith in family, friends, and trusted coworkers to gain information. To the extent that adult and postsecondary educators can ally themselves with community and family members who live near or work closely with nonparticipants, the chances of successful recruitment would rise.

Most important predictors of nonparticipation are actionable. While poor health, advancing age, or low income may not be directly actionable, they offer valuable insights into opportunities for recruitment, individualized supports, flexible programming, and financial aid. Educators’ efforts will go a long way to closing wide skills gap, reducing literacy classism (Quigley, 2017), and preparing adults for the future workforce (Autor, 2014).

Limitations and Conclusion A first limitation of the article is associated with the measure employed to identify nonparticipation. As a cross-sectional survey, PIAAC addresses participation in formal or nonformal education within the past 12 months rather than measuring it as time invariant or longitudinally. Some adults who pursued learning 2 or more years earlier perhaps took a break from participation. Still the article’s results provide evidence, such as the much lower skill levels of nonparticipants, which suggests that nonparticipation for many may be long-standing.

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This article leaves many questions unanswered, in part because certain background questions were not included in PIAAC and in part because findings raise new questions which are beyond the scope of the article to address. With the exception of PSA, all analyses in this article were descriptive, and causality should not be inferred.

Future research could consider geographic differences, age and parental background, and the “reasons behind the reasons” of other deterrents. Nonparticipants need to be an integral part of this future research and no longer “forgotten” in future studies of deterrents (Quigley, 2006). While residence density is evenly distributed across the United States, the overrepresentation of nonparticipants in the South and in rural areas of the Midwest and South for the least educated adults is a concern. Future researchers could investigate qualitatively how nonparticipant experiences differ and what reasons residents in these areas give for not participating. How could adult and postsecondary educators from different regions of the country collaborate to change this balance?

Further qualitative investigation is also needed on the role of age, interest in education, and parental education. What are the interest levels of middle-aged adults in further education, and how do those levels fluctuate with advancing age (Smith, 2009)? How long are they planning to stay in the workforce, and what types of learning might appeal to them? What family role models, if any, might encourage them to pursue education? A sizable proportion of nonparticipants are simply not interested in nonformal education. Why might nonparticipants be uninterested, especially at work?

Do they anticipate nonformal education as “going to school again”—as stigmatizing, irrelevant, or boring (Quigley, 1997, 2017)? Whatever is gleaned in answer to this question can be used in recruitment materials to show prospective participants how adult or PSE may differ from previous “school” experiences and how further education connects to the workplace.

Deterrents such as costs of education, work and family responsibilities, and even health challenges are a reality. Research needs to determine what is behind these deterrents— what feeds into challenges—and how some adults can identify solutions where others cannot. Greater understanding of the “reasons behind the reasons” will help adult and postsecondary educators identify and implement solutions to the deterrents adults face.

In-depth PIAAC-USA analysis of the backgrounds and deterrents of least educated adults adds to the knowledge base of nonparticipation in formal or nonformal education.

This article also identifies solutions that might trigger them to reengage. Knowing even more about what happened educationally to the forgotten 90% is informative to educators and policy makers—and most important, to prospective adult learners themselves.

Acknowledgments The author wishes to thank B. Allan Quigley, Jaleh Soroui, and two anonymous reviewers for insightful comments on earlier drafts of the article.

Author’s Note Paper presented on April 2017 at American Educational Research Association conference, San Antonio, TX. Content responsibility is solely the author’s.

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Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

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Author Biography Margaret Becker Patterson is currently Senior Researcher with Research Allies for Lifelong Learning in the Washington, DC, metro area (www.researchallies.org). She led the award-­ winning Adult Learner Leadership in Education Services (ALLIES) evaluation for VALUEUSA, the national organization of adult learners. Previously, she served as Research Director at GED Testing Service and administered and taught in adult education programs in Nebraska, Nevada, and Kansas.