Empowering ReachOut e-Diversity News - May 2022

Empow weerring ing Empo An Electronic Electronic Publication Publication of of the the An Ohio Developmental Developmental Disabilities Disabilities Council Council Ohio

Reach Out e-Diversity News is designed to EMPOWER you to…

• Recognize the needs of un/underserved Ohioans with developmental disabilities.

May 2022 Edition | Volume 1, Issue 4 Read, Pass on to Friends, Family Members, Colleagues & Constituents

Don’t Miss an Issue!

• Collaborate and coordinate to make sure culturally competent services are provided.

• Measure and monitor to get results that support un/underserved Ohioans living more productive and independent lives.

This month’s focus: Data Tells a Story Why?

Data can either unintentionally reinforce harmful stereotypes and perpetuate inequity and bias, or it can promote diversity, equity, and inclusion. This issue is designed to inspire you to use your data to tell a compelling story; increase recognition of the role collecting demographic data plays in telling a story that demonstrates you and your organization value diversity and inclusion; demonstrate how to leverage data to facilitate more equitable organizations and systems.

Diversity, Equity, and Inclusion (DEI) Data Tool Kit Download NOW and gain access to tools you can use to help you… ˜ ˜ ˜ ˜

gain access to data about disabilities that you can customize gain access to a survey tool that will help you design an inclusive survey for individuals with disabilities understand the data analysis process identify how to choose an appropriate data collection method Page 1

This Months -HighlightsI Love Data

Data Equity: What is it, and Why Does It Matter?

How We Collect Data Determines Whose Voices Are Heard

JOIN the Outreach Difference Maker Challenge It’s a Story Slam June 14, 2022 4:00 – 5:00 pm Look out for your Challenge Invite

MAY

Asian American, Native Hawaiian, and Pacific Islander (AA and NHPI) Heritage Month—a celebration of AA and NHPI individuals and communities in the United States.

More Information

The purpose of Empowering Reach Out e-Diversity News is to promote interagency collaboration and coordination that result in agencies providing culturally competent services to the unserved/underserved populations in Ohio. It is the policy of the Ohio Developmental Disabilities Council to use person-first language in items written by staff. Items reprinted or quoted exactly as they originally appear may not reflect this policy.

Empowering Reach Out e-Diversity News is produced by The Outcomes Management Group, Ltd. This product is funded all or in part by the Ohio Developmental Disabilities Council.

Page 2

Empowering Empowering

e-Diversity News News ReachOut e-Diversity ReachOut

An Electronic Electronic Publication Publication of of the the An Ohio Developmental Developmental Disabilities Disabilities Council Council Ohio

I Love Data!

5 Reasons You Should Too

Patricia Larkins Hicks, Ph.D. Outcomes Management Group, LTD Over the past twenty-seven years, as an evaluator and a person passionate about outcomes, working with organizations and individuals in non-profit, for-profit, and governmental organizations, I have found that dissatisfaction with numbers and fear of numbers have immobilized and prevented individuals and organizations from taking appropriate actions. The fact is, “taking appropriate actions” increases positive numbers, and moves negative numbers into positive numbers. Inaction maintains the status quo and sometimes makes the numbers worse. It was early in my career that that I fell in love with data. Specifically, during the time when I was working with individuals who had problems communicating, I was particularly drawn to those whose communication had been impaired due to a stroke. That interest led me to a doctoral program at Memphis University. While working with one of my favorite patients, I made an invaluable discovery about the value of data.

My Experience Mr. W. had been in speech therapy for three years. During that time, he had not uttered a word. You can imagine that after having led a successful life, Mr. W, was frustrated that he was unable to speak. This frustration was compounded by the fact that he was able to understand what others was saying. When he tried to respond, all that he uttered sounded like gibberish. Nevertheless, Mr. W. came faithfully every week to our clinic for his therapy sessions. He had a loving wife, who was always by his side when he came to therapy. She participated in our family support group sessions. Mrs. W. longed to hear her husband’s voice again. She missed not being able to engage in conversation with him and that was all that she wanted.

Page 3

One day, after being exposed to the work of a leading researcher in the profession, a new procedure appeared to be one that might benefit Mr. W. After gaining consent from my advisor, the new procedure was implemented. To everyone’s amazement, Mr. W. uttered his first words. While I was certain the procedure had made the difference, my advisor cautioned me to establish a research design which would allow me to “measure” and gather “data” to support “my assumption”. To that end, I decided to measure the newest and traditional procedures to determine which facilitated the greater change over time. After eight weeks, the comparative data indicated that Mr. W. was talking at a faster rate with the newer procedure. No one could be more thrilled than Mrs. W. She finally heard the words she longed to hear from her husband when he said, “I love you.” This was the moment that “I fell in love with data.”

It was this single experience that taught me 5 reasons to love data.

#1 Data helps you identify what is most important, what is significant. You have a limited amount of time to accomplish your goals, live your dreams. You need to spend your time on what matters. Data facilitates focus.

#2 Data represents a set of facts. While you are quick to make assumptions, your assumptions are not always accurate. Data provides validation of the extent to which assumptions are accurate. If you want to succeed in the shortest amount of time, then you want to make sure you validate your assumptions early and often.

#3 Data provides insight into progress. You can manage that which you measure.

Knowing whether your efforts are resulting in positive change is essential. Data facilitates recognition that you are moving in the right direction; if not, data provides insight into where you need to self-correct, adjust. Data helps you know when you have reached your goal.

#4 Data facilitates identification of trends. Data represents a specific moment in time.

When you collect data regularly over time, you can detect different patterns pertaining to people, processes, tools, finances, results, and so much more. Recognizing trends leads to innovation and better preparation.

Page 4

#5 Data is information. Just like words, numbers tell a story.

Be sure to tell your story. When others tell your story, their bias, experience, and intentions influence the story. And the story may not be the one that you want told. Use your data and tell your story!

Now, it is your turn to fall in love with data.

Dr. Patricia Larkins Hicks is an “outcomes pioneer.” In 1987, she led the development of the first national program evaluation system (PES) for the American Speech Language Hearing Association (Rockville, MD. that included functional communication measures.

In 2020, data from these tools were used to tell the story that demonstrated speech-language pathologists’ services contributed to the progress of children on the autism spectrum. Dr. Hicks continues to develop and use evaluation measures to tell stories that identify changes in boards, workforces, and funding which create more diverse, inclusive, and equitable organizations and systems.

Learn more at: https://www.omgknowsbest.com/ organizational-b-e-s-t-.html

The purpose of Empowering Reach Out e-Diversity News is to promote interagency collaboration and coordination that result in agencies providing culturally competent services to the unserved/underserved populations in Ohio. It is the policy of the Ohio Developmental Disabilities Council to use person-first language in items written by staff. Items reprinted or quoted exactly as they originally appear may not reflect this policy.

Empowering Reach Out e-Diversity News is produced by The Outcomes Management Group, Ltd. This product is funded all or in part by the Ohio Developmental Disabilities Council.

Page 5

Empowering Empowering

e-Diversity News News ReachOut e-Diversity ReachOut

An Electronic Electronic Publication Publication of of the the An Ohio Developmental Developmental Disabilities Disabilities Council Council Ohio

Data Equity: What Is It, and Why Does It Matter?

Joyce Lee-Ibarra JLI Consulting

What Does “Data Equity” Mean?

Racial justice protests across the country have sparked new conversations regarding race, racism, and social justice in nearly every aspect of American society. From housing to health care access, from employment opportunities to wealth accumulation, from education to policing practices, issues and institutions are being re-examined through the lens of antiracism and equity. Data, and the systems that create and rely upon data, have been no exception.

The term “data equity” captures a complex and multi-faceted set of ideas. It refers to the consideration, through an equity lens, of the ways in which data is collected, analyzed, interpreted, and distributed. It underscores marginalized communities’ unequal opportunities to access data and, at times, their harm from data’s misuse. It raises the issue of data sovereignty, and the democratization of data. And data equity pushes us to consider the ways that data can reinforce stereotypes, exacerbate problems like racial bias, or otherwise undermine social justice.

“Data justice,” a term that at times is used interchangeably or in close relation to “data equity,” Page 6

has been tied to the ethics of personal data privacy, big data, and decision making that results from the “datafication” of modern society. But it is also used to encompass the complex meanings that data equity captures, including concerns regarding power and privilege, knowledge equity, and the ways that harmful decision making may be justified or maintained through data.

What Are Some Key Ideas Behind the Data Equity Concept?

Data is not objective. While numbers and figures may be neutral in and of themselves, they don’t exist in a vacuum. Data is collected, analyzed, interpreted, and distributed by people, who bring to their work their subjective experiences and potential biases. The goals or motivations we have in our data work, as well as the questions we ask and how they are framed, are likewise informed by our perspectives, even unintentionally. Various forms of interpretation bias, for example, can color our understanding of data, leading us to selectively value or dismiss certain outcomes and explanations over others.

Data can create and perpetuate power dynamics. Knowledge, as the saying goes, is power. Data, as a form of knowledge, can create power imbalances, which become more visible when we reflect on questions such as:

·

Why are we seeking to collect data?

·

Who is empowered to collect data?

·

What is the dynamic between question asker vs. question answerer?

·

Who will be the end user of the data?

·

Who and what will the data be in service to?

·

Who are the “experts” in the data project?

While framed in terms of research, the points raised by the authors of Why Am I Always Being Researched? certainly apply equally to the collection and generation of data from people and communities: Page 7

“Right or wrong, research can drive decisions. If we do not address the power dynamics in the creation of research, at best, we are driving decision-making from partial truths. At worst, we are generating inaccurate information that ultimately does more harm than good in our communities. This is why we must care about how research is created.”

Equity needs to be addressed throughout the data life cycle. The data life cycle refers to the various stages of a data project that offer opportunities to practice greater consciousness of and commitment to equity, fairness, and access. We All Count breaks down the data life cycle into seven stages—from funding through the communication and distribution of results—and describes potential inequity pitfalls in each. Urban Institute offers principlealigned practices for the data life cycle, which is described as occurring in four stages—each of which can be guided by three principles from human subjects’ protections: beneficence, respect for persons, and justice.

As a community research and evaluation consultant, I’m taking stock of how I approach my work, and how I can better center equity in my collaborations with funders and nonprofits. The traditional evaluation paradigm frequently employed by the social sector is a prime example of data reinforcing power dynamics and inequity. Funders and foundations often hold financial sway over the nonprofits or community members being evaluated. Evaluators may act as the sole arbiters of what data is useful or meaningful, forcing established orthodoxies on the evaluation process without regard to culture, history, or context. Guidance provided by efforts such as the Equitable Evaluation Initiative seek to reimagine the evaluation process, and harness evaluation and the data it generates as tools in service to equity, rather than obstacles to achieving it.

CONTACT: www.jliconsultinghawaii.com

The purpose of Empowering Reach Out e-Diversity News is to promote interagency collaboration and coordination that result in agencies providing culturally competent services to the unserved/underserved populations in Ohio. It is the policy of the Ohio Developmental Disabilities Council to use person-first language in items written by staff. Items reprinted or quoted exactly as they originally appear may not reflect this policy.

Empowering Reach Out e-Diversity News is produced by The Outcomes Management Group, Ltd. This product is funded all or in part by the Ohio Developmental Disabilities Council.

Page 8

Empowering Empowering

e-Diversity News News ReachOut e-Diversity ReachOut

An Electronic Electronic Publication Publication of of the the An Ohio Developmental Developmental Disabilities Disabilities Council Council Ohio

How We Collect Data Determines Whose Voices Are Heard Rella Kaplowitz, Senior Program Officer for Evaluation and Learning Jasmine Laroche, Program Associate for Evaluation and Learning Charles and Lynn Schusterman Family Foundation

What Data to Collect The first step in inclusive and equitable demographic data collection is determining what data will best suit your organization’s needs. Your organization should thoughtfully consider which demographic data to prioritize and the best method to collect them. Careful consideration of data collection is vital because demographic questions can be sensitive and respondents may feel the survey ignores the complexity of their identity, may be skeptical, or defensive if the purpose of the data collection or how the organization ti will ill use the th data d t is i not clear.

Do not gather data just to gather data.

Example questions for consideration: What is the purpose of my data collection tool? (e.g., application, program evaluation, customer satisfaction), and do I need to collect demographic data?

Page 9

˜ ˜ ˜

Does the strategy or program want to reach a specific population? Why? What demographic data do we need to evaluate whether we are making progress?

What are the criteria for distinguishing between “nice to know” and “need to know” data?

What specific decisions will the data help inform? Will the data be pertinent and actionable?

Who will review the demographic data? Who will use these data to make decisions?

˜ ˜

Data Privacy and Confidentiality Example questions for consideration: Make sure you consider how to best protect personally identifiable information (bit.ly/PersonallyIdentifiableInfo), which could potentially identify an individual, either alone or when combined with other information linked or linkable to them. The Health Insurance Portability and Accountability Act (HIPPA), Family Educational Rights and Privacy Act (FERPA), or General Data Protection Regulation (GDPR) may also regulate some data collected. Accordingly, organizations should think about developing a data management plan to support data privacy and confidentiality. ˜ ˜ ˜ ˜ ˜ ˜

Do the data include personally identifying information?

Do the data include any sensitive information individuals may want to keep private?

Are there any data regulations to consider, such as HIPAA, FERPA, or GDPR? What are the plans to securely store the data?

Who will have access to the data and under what circumstances?

How long will you retain the data?

Provide Multi-select Checkboxes or Open-ended Questions Multiselect: Identity is complex, so the best approach for inclusive data collection is to avoid making respondents feel like you are “boxing them in” with only one possible answer. Allowing respondents to select multiple answers gives them more freedom to express their diversity of their identity for a given trait.

Open-Ended: With open-ended questions, respondents can clearly express their view of themselves related to a specific trait and use the terms they feel are appropriate for them.

Additionally, collecting data on how people self-identify in open-ended questions over a long period can inform the wording of future survey questions because it allows individuals to respond in their own words based on how they describe themselves.

Page 10

Make sure you look at organizational capacity for analysis. For example, to mitigate some of the additional workload on analyzing open-ended responses, consider including a multiselect pick list with open ended response choices.

Multiselect and Open-ended Question Combination Consideration I identify as: (select all that apply) ˜ American Indian or Alaska Native ˜ Asian or Asian American ˜ Black or African American ˜ Hispanic, Latinx, or Spanish origin ˜ Middle Eastern or North African ˜ Native Hawaiian or Pacific Islander ˜ White ˜ Another race or ethnicity not listed above ___________________________________ Please print your specific race or ethnic identity in the space below. For example, Korean, Mexican American, Navajo Nation, Somalian, et.al. ________________________________

Include “Prefer Not to Answer” and Replace “Other” Response Choices.

Required Questions and Opting Out: Requiring respondents to answer questions ensures that you collect the most complete dataset. However, the tactic can be problematic when it comes to demographic questions because questions of identity can be sensitive, or it may not be clear to the respondent how you plan to use the data. If you require respondents to answer demographic questions, they might now complete the survey or form. Along with clearly explaining why you are collecting the data and how you will use them, one solution is to make the questions optional, and another is to include a “prefer not to answer” option.

Including a “prefer not to answer” option allows you to track how often respondents select that response choice and explore why individuals may be opting out of responding. Do the response choices not reflect how the individuals see themselves? Is the question unclear?

Other: While it is important to include a response choice that has an option for individuals to enter information that does not appear on a finite list, having to enter something in an “Other” category can alienate some respondents. Instead, consider using language like “Prefer to Describe” or “Prefer to Self-Describe.”

Page 11

Where to Place the Questions It is generally recommended to place demographic questions at the end to limit response bias and reduce potential survey fatigue. Reasons for adding these questions at the beginning of the survey include determining eligibility for participation if demographics are a necessary part of the analysis or if researchers want to route participants through particular sections of the survey.

Assess the Order of Response Choices The order in which response choices appear can reinforce implicit bias. For example, “United States” often appears as the first response choice to a question about country of origin because survey design focuses on creating the simplest and most efficient use experience and it is assumed that a majority of respondents to a U.S.-based survey will select that response. The same is often true with race and ethnicity questions, where “White” is the first response choice.

While not always possible, consider randomizing response choice, displaying them alphabetically or manually arranging them to support an inclusive survey experience.

How demographic data is collected represents who is seen and who is erased, who counts and who does not. The practices provided are a first step in striking a better balance of collecting the data organizations need while reducing inequity and celebrating a comprehensive exploration of diversity.

VISIT www.schusterman.org

The purpose of Empowering Reach Out e-Diversity News is to promote interagency collaboration and coordination that result in agencies providing culturally competent services to the unserved/underserved populations in Ohio. It is the policy of the Ohio Developmental Disabilities Council to use person-first language in items written by staff. Items reprinted or quoted exactly as they originally appear may not reflect this policy.

Empowering Reach Out e-Diversity News is produced by The Outcomes Management Group, Ltd. This product is funded all or in part by the Ohio Developmental Disabilities Council.

Page 13

Empowering Empowering

e-Diversity News News ReachOut e-Diversity ReachOut

An Electronic Electronic Publication Publication of of the the An Ohio Developmental Developmental Disabilities Disabilities Council Council Ohio

Diversity, Equity, and Inclusion (DEI) Data Tool Kit This month’s Diversity Equity Inclusion Data Tool Kit contains a “must have” set of resources that will provide you with access to data that you can customize to meet your needs; information and tools that will help you be inclusive in data collection; “best practices” that you can utilize to define and implement data collection and data analysis. Click on each description and check out the examples pertaining to resources, discussions, best practices, and training

RESOURCES Disability Statistics Customize a search by choosing a statistic of interest and filtering by disability status and type; Compare results of your search in all 50 states

2019 State Report for County-Level Data: Employment | Annual Disability Statistics Compendium Find employment data for individuals with disabilities, including rate of employment across all Ohio counties

Disability and Health System Data - Center for Disease Control Find health data on Ohio’s disability community and intersections with other factors (i.e. age, education level) Page 14

DISCUSSIONS

Importance of tracking and using data to support People with Disabilities with Ken Hatch

iData Survey Tool: Inclusive Data Collection for Persons with Disabilities

BEST PRACTICES

Data Management: Principles, Tools, and Best Practices

TRAININGS A Beginners Guide To The Data Analysis Process

Research Design: Choosing your Data Collection Methods | Scribbr

The purpose of Empowering Reach Out e-Diversity News is to promote interagency collaboration and coordination that result in agencies providing culturally competent services to the unserved/underserved populations in Ohio. It is the policy of the Ohio Developmental Disabilities Council to use person-first language in items written by staff. Items reprinted or quoted exactly as they originally appear may not reflect this policy.

Empowering Reach Out e-Diversity News is produced by The Outcomes Management Group, Ltd. This product is funded all or in part by the Ohio Developmental Disabilities Council.

Page 15