Attention to diversity, equity, and inclusion (DEI) in higher education has intensified in recent months on many campuses. Increasingly, institutional leaders acknowledge that advancing social justice requires involving everyone on campus, not only Chief Diversity Officers or those with the explicit charge of promoting equity and inclusion. Like everyone else on campus, institutional research staff have a part to play. One pivotal way IR offices are involved in DEI efforts is in the design and creation of institutional DEI dashboards. These dashboards serve various audiences but have the same underlying goal: to make meaning out of institutional data in order to advance diversity, equity, and inclusion at our institutions.

Inequality and social injustice are complex, thorny problems without simple solutions, and DEI dashboards can provide a starting point for understanding and addressing the issues. In June 2020, Miami University in Ohio set out to build a dashboard of its own. The Office of Institutional Research and Effectiveness was instrumental in development and organization of the dashboard. In this article, we discuss measures and data used in institutional DEI dashboards, suggest best practices and design considerations, and offer examples from a DEI dashboard currently being used to support DEI efforts at one university.

Best Practices and Design Considerations

DEI dashboards vary based on institutions' needs, but the following are best practices and design decisions to consider when developing a dashboard.

Who Should be Represented?

Higher education communities include various constituents: students, faculty, staff, alumni, administrators, trustees, incoming/admitted students, residents of the surrounding community, and more. Dashboards should be specific and inclusive when representing these groups. For example, “faculty” typically encompasses many different roles that vary tremendously by responsibilities, promotion and reward systems, and level of inclusion in decision making. Therefore, dashboards about faculty are most meaningful when faculty are disaggregated, such as by role or rank. A recent examination of national data on scholars of color by rank, for example, revealed that faculty of color "occupy the most vulnerable faculty positions" after data indicated "Hispanic faculty members are most likely to hold instructor and lecturer roles, and Black faculty members are most prevalent in positions with no academic rank." Miami University's institutional DEI dashboard, presents faculty retention information separately for tenure-track faculty and non-tenure-track faculty, defined by the MU community as teaching faculty, clinical faculty, and lecturers.

How Should Groups Be Labeled?

The language we use to describe groups of people is powerful, so thoughtfully labeling categories and even choosing color schemes used in our dashboards is essential. For example, we should use humanizing language to describe categories of people, such as international students instead of non-resident aliens. How do we know what language is appropriate or inappropriate? One way is to ask, for example through focus groups. Or we can stay abreast of research that asks students how they perceive their own racial identities (e.g., Stewart, 2009) and how they think about the identity categories we ask them to choose from (e.g., Johnston, Ozaki, Pizzolato, & Chaudhari, 2014). 

How Should Data Be (Dis)aggregated?

Interactive dashboards often allow users to customize the extent to which the people represented can be disaggregated into finer-grained groups. When sample sizes are large enough to maintain confidentiality, try to give dashboard users the option to disaggregate identity categories into smaller groups. For example, in some cases Korean American and Indian American could be disaggregated instead of grouping them with “Asian American.” Often, grouping people into broad racial categories overlooks various cultural and social identities represented by this group and obscures differences in educational opportunities and outcomes across subgroups (Teranishi, 2007).

Data Storytelling and Wrapping It Up

Ultimately, effective DEI dashboards tell stories with data. We leave you with four key principles (the “4 As”) for effectively building dashboards and data visuals, offered by Knaflic (2015) in Storytelling with data: A data visualization guide for business professionals. These principles include:

• Affordances: Aspects inherent to the design that make it obvious how the data is to be used.
• Accessibility: The designs should be usable by people of diverse technical abilities (digital vs. non-digital natives).
• Aesthetics: Make it pretty—more aesthetically pleasing designs are perceived as easier to use/interpret.
• Acceptance: For a design to be effective, it must be accepted by its intended audience.

Using the four key principles and considering the design questions above will start the process of creating DEI dashboards that can help drive improvements and narrow the focus for taskforce members and campus leadership.

Sample Dashboard Views

References

Dam, R., & Siang, T. (n.d.). 5 stages in the design thinking process. Retrieved January 08, 2021, from https://www.interaction-design.org/literature/article/5-stages-in-the-design-thinking-process

Dykes, B. (2019, December 20). Data storytelling: The essential data science skill everyone needs. Retrieved January 08, 2021, from https://www.forbes.com/sites/brentdykes/2016/03/31/data-storytelling-the-essential-data-science-skill-everyone-needs/#1e00074652ad

Harris, J. C., Barone, R. P., & Davis, L. P. (2015). Who benefits?: A critical race analysis of the (d)evolving language of inclusion in higher education. Thought & Action, 21-38.

Harris, T. M. (2020). Dismantling the trifecta of diversity, equity, and inclusion: The illusion of heterogeneity. In H. Oliha-Donaldson (Ed.), Confronting equity and inclusion incidents on campus: Lessons Learned and Emerging Practices. Taylor & Francis.

Johnston, M. P., Casey Ozaki, C., Pizzolato, J. E., & Chaudhari, P. (2014). Which box(es) do I check? Investigating college students’ meanings behind racial identification. Journal of Student Affairs Research and Practice, 51(1), 56-68.

Knaflic, C. (2014, February 10). Storyboarding. Retrieved January 08, 2021, from http://www.storytellingwithdata.com/blog/2014/02/storyboarding

Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Hoboken, NJ: John Wiley & Sons.

Popova, M. (2015, November 11). How to write with style: Kurt Vonnegut's 8 keys to the power of the written word. Retrieved January 08, 2021, from https://www.brainpickings.org/2013/01/14/how-to-write-with-style-kurt-vonnegut/

Stewart, D.-L. (2009). Perceptions of multiple identities among Black college students. Journal of College Student Development, 50(3), 253–270.

Teranishi, R. T. (2007). Race, ethnicity, and higher education: The use of critical quantitative research. New Directions for Institutional Research, 133, 37–49.

Zamudio-Suaréz, F. (2020, February). Race on campus: What I learned from a deep dive into diversity data. The Chronicle of Higher Education. Retrieved from https://www.chronicle.com

Inger Bergom is Senior Data Analyst at Tufts University. She earned a Ph.D. and M.A. in higher education from the University of Michigan. Views presented are her own and do not represent those of her employer.

Mollie Miller is the Associate Director for Institutional Research and Effectiveness at Miami University. Mollie is a current candidate for the degree of Doctor of Philosophy in Interdisciplinary Studies majoring in Educational Studies. Mollie currently serves as President for the Ohio Association for Institutional Research and Planning and a sector four-year public representative.

The authors thank Christina Butler, Dr. William Knight, and the eAIR Editorial Board for their feedback on previous versions of this article.