When we began the journey of writing Inclusalytics: How DEI Leaders Use Data to Drive Their Work, we realized that so many organizations were still early on in their DEI journeys and needed support determining what DEI is at its core, how to measure it, and how to turn that data into action. With those questions from our clients in mind, our research background in human behavior in the workplace, and through copious interviews with DEI leaders, Inclusalytics was born. The reception for the book over the last three years has been more than we could have ever imagined!
And while the book is an easy read, sometimes you need something even shorter to digest or perhaps a quick refresher on key points.
Never fear! Over the course of the next few months, we’ll be releasing “Inclusalytics Snapshot” blogs for each of the chapters of our book Inclusalytics. These recaps (or sneak peeks!) provide a glimpse of some of what we covered in our best-selling book. Up next: Chapter Eight!
Chapter 8: Turn Data into Insights
After collecting your organization’s DEI data, the next step is to attempt to draw meaningful conclusions. Ideally, an analyst with statistical expertise will provide insights from the data collected, but it is important to note that the most complicated analysis is not always the right analysis. Rather, the best analysis is the one that answers the questions you are asking.
Cleaning Your Data
When cleaning your data, it's essential to ensure all variables are organized and clearly defined. Each row, column, and value should be understood, and duplicate entries must be removed. Handling missing data is crucial in quantitative analysis. It’s important to document your approach to missing data, ensuring that your method is justifiable.
To reduce missing data, you can make all items required. An option such as “choose not to answer” or “don’t know” should still be provided. Further, nonresponse patterns may reveal insights, serving as data themselves.
Analyzing Quantitative Data
When analyzing quantitative data, statistical significance and effect size help determine whether certain groups score lower on average and how substantial those differences are.
Adverse impact analysis should also be considered in any DEI evaluation. In the context of hiring, adverse impact analysis examines whether a selection process disproportionately affects members of protected groups. If a hiring method results in significantly fewer opportunities for these groups, it could be unlawfully discriminatory unless proven necessary for job performance. Validity evidence is often requested to justify such selection processes, highlighting the importance of data transparency and defensible decision-making.
Analyzing Qualitative Data
For qualitative data, thematic analysis is a powerful method to uncover common themes across responses. Using multiple coders reduces bias and brings the analysis closer to what’s truly present in the data. Once themes are identified, it's valuable to dig deeper to understand which respondents feel a certain way and why. Advances in AI can also support thematic analysis by identifying patterns more efficiently, but human oversight remains important to ensure accuracy and context.
Interpreting Results
It’s essential that impact measures are established at the outset of any intervention to determine what you’re hoping to learn. Once you have completed your analyses, there are several possible outcomes and different ways these outcomes can be interpreted:
There was no change. If your analyses reveal that your outcome has not changed since before the intervention, it’s possible that you have not given the intervention enough time to work. This finding could also point to an ineffective intervention or a flawed analysis plan. Further, you may have taken the wrong approach at the outset and tried to solve the wrong problem. If there was no change, it’s a great opportunity to review the entire process to determine if there were any faulty decision points.
There are gaps in the employee experience for certain groups. This finding can provide important guidance for the road forward and further intervention to improve the experience of these employees. When making these decisions, it’s important to take an approach that won’t decrease the experience of other groups, including overrepresented groups.
"Be sure to include and monitor overrepresented group members' voices in your DEI efforts to ensure that you understand their perceptions of DEI to counteract any feelings of exclusion or backlash."
When deciding what to share of your findings with your organization, we recommend transparency. This is the best policy for organizations working toward meaningful change.
Want to read more? Buy your own copy of Inclusalytics here.
Looking to get started on your DEI measurement journey in your organization? Contact us today.
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