Measuring Demographics 

Measuring Demographics 

By Inclusion Analytics

Organizational diversity has been an area of focus for many large organizations since the enactment of Affirmative Action in the 1960s. Below are answers to four important questions regarding the type of demographic data to collect and what to analyze once data is available to your organization. 

What types of demographic data could we collect? 

The three most common demographic data points collected by organizations are age, sex or gender, and race/ethnicity. Even without diversity goals, many organizations collect this information for other entities, such as government contracting requirements or to better allocate benefits. 

Other, less common categories, that may help you understand the needs of your workforce better are caregiving status, disability status, sexual orientation, veteran status, etc. Once your organization knows the type of demographic data it is ready to collect, there are many ways to analyze the data to answer important questions regarding representation.  

Is our workforce representative of our recruitment area population? 

The answer to this question is fairly simple if you have employee demographic data. Location representation comparisons can be found at the US census website using the population tables. Simply calculate the percentage of each demographic group within your organization ((group total/organization total)*100) and compare it to the demographic group within your recruiting area. If you have multiple locations, you should consider this analysis for each city in which the company is located.  

How does overall representation breakout by management level? 

Overall, representation within your organization is important, but understanding how that data disaggregates by job level answers a totally different question, so you may want to examine whether representation is equitable as you move up the management hierarchy. The first step to conducting analyses by level is to ensure each piece of data can be tied through your human resource information system (HRIS). For instance, job codes or families may need to be categorized based on level before breaking employee data down by demographics. Once complete, simply calculate percentages of each demographic group of interest by level. For example, if your gender breakdown at the lowest level of the organization is 50% men, 45% women, and 5% a third gender, the same proportions should be expected at each level up in the organization.  

What does representation in turnover look like? 

We influence the diversity of our workforce by both recruiting new employees and by retaining current employees. When individuals leave the organization, it is extremely important to know who is leaving and why. To answer the who, we simply keep tying demographic data to our databases. Tracking the demographics in turnover help us to know whether certain groups leave at greater rates than other groups. Similarly, involuntary turnover should be reviewed to examine whether certain groups are let go at higher rates than other groups. 

Answering the why is a little more complicated. Exit interviews in the form of one-on-one interviews or in survey format provide greater context for why employees leave. These interviews should be structured, standardized, and conducted outside the person’s direct reporting structure. Next, data should be analyzed by demographic group wherever possible to assess whether there are trends between groups.  

All in all, there are so many ways that the data your organization is already collecting can be analyzed to support diversity, equity, and inclusion efforts. Whether the primary need is to expand recruitment and hiring or to expand development opportunities, data can help you create a more diverse, equitable, and inclusive workplace.  


About Inclusion Analytics

Co-founders of Inclusion Analytics, Emily Adams and Laura Brooks Dueland, are both doctoral candidates in the Industrial/organizational Psychology Program at the University of Nebraska at Omaha. Prior to the launch of Inclusion Analytics. Emily worked in post-secondary education administration, juvenile justice system reform, and behavioral health workforce development. Laura’s experience includes behavioral interviewing, promotional exam development, and human resources consulting in the areas of compensation and benefits. With research focus on validity and the execution of solutions aimed at reducing bias in the employee lifespan, both Emily and Laura aim to help organizations create a workplace that works for all.