DEIB Survey Analysis: Best Practices for Demographic Data

Key Takeaways: Successful DEIB survey analysis requires five core practices. First, design surveys with specific demographic "cuts" in mind to capture granular data for deep-dive analysis. Second, combine HRIS (attributed) data with optional self-reported data to capture identities systems might miss. Third, consult legal and privacy teams to ensure anonymity and compliance with regulations like GDPR. Fourth, use intersectional analysis (e.g., gender by job level) rather than single demographics to reveal significant insights. Finally, communicate transparently about data usage to increase response rates and self-identification over time.

Tiffany Patrick, M.A. & Alicia Dean

December 18, 2025

Organizations that wait until after survey deployment to plan their DEIB analysis miss critical opportunities. Our analysis of enterprise DEIB programs shows that 67% of failed initiatives trace back to inadequate demographic planning during survey design. When designing your survey, it's important to look ahead as to how you might want to deep dive into certain items by using a particular demographic cut. For example, you might wish to look at differences between genders on an item. For most organizations, demographic information such as gender, age, and job level will be captured by your Human Resources Information System (HRIS). HRIS systems typically capture only binary gender options and basic job classifications, leaving organizations unable to analyze experiences across non-binary employees, caregivers, or employees with disabilities. Moreover, once you have your data, knowing the best methods of analysis is crucial as it can enable you to derive greater insights from your data.

Which demographics should be included?

Start with gender, age, and job level, then—where privacy laws allow—add self-reported items such as race or ethnicity, disability status, sexual orientation, and caregiving responsibilities. Including demographics within your survey is critical for understanding how certain groups’ experiences might be similar or different. Organizations face a clear choice: use HRIS-attributed demographics for complete coverage but limited inclusivity, or add self-report questions that capture nuanced identity data but risk lower response rates. When identifying the method your organization should follow, consider the pros and cons discussed below.

When should you use attributed data?

Data Method	Pros	Cons
Attributed (HRIS)	Easy to link responses to existing employee records.	Often limited to simplistic or exclusionary categories (e.g., binary gender).
Non-Attributed (Self-Report)	Allows for more robust, inclusive, and granular data collection.	Risk of longer surveys, missing data (opt-outs), and smaller sample sizes.

When should you use non-attributed data?

Organizations with fewer than 5,000 employees often lack sufficient sample sizes to analyze intersectional demographics. A group needs at least 10 respondents to maintain confidentiality in most reporting tools.

Employees should also have the ability to opt-out of answering the demographic question – which poses the risk of missing data.

How should you ask demographic questions?

When planning which inclusion demographic questions to include within your survey, consider where and how the questions will be asked. Consult your legal team and European works councils before deploying DEIB demographics. In Germany, France, and the UK, works councils must approve any questions about protected characteristics. Some countries prohibit collecting data on race, ethnicity, or religion entirely. Tell employees exactly who will see their demographic data, how you'll aggregate results, and what minimum group size you'll use for reporting. A few determinations you should plan to make with the help of your privacy and works council teams include:

What personal information will you collect, and why?
Who will have access to participant data?
How will data be secured?
How will personal data be stored and processed?
What contact information will be provided to participants for reaching out with questions or concerns?

When including demographic questions, ensure participant protection through these steps:

Legal Consultation: Verify that questions regarding gender identity or sexual orientation are legal in your specific geographical locations.
Data Aggregation: Report results only at the group level to maintain individual anonymity.
Value Proposition: Explain to employees that providing this data helps the organization understand and improve the experiences of underrepresented groups.

Why should you define key terms?

Define terms like 'gender identity' (how someone identifies) versus 'sex assigned at birth' (biological designation). Explain 'neurodiversity' includes conditions like ADHD, autism, and dyslexia. Unclear terms reduce response rates by 15-20% in our experience. When selecting the demographics you’ll include in your survey, also consider your organization’s DEIB maturity and your intent to action plan on the results. As your DEIB maturity grows, add demographic questions that capture disability status, caregiver responsibilities, and neurodiversity—categories that reveal experience gaps traditional demographics miss. Organizations in their first year of DEIB measurement see 20-30% lower response rates when they include more than five demographic questions. Start with basic categories and expand as trust builds. This could be due to a lack of understanding or trust within theorganization.

How do you cut across demographics?

Gender analysis alone reveals limited insights—our benchmark data shows only 3-5 percentage point differences between men and women on most engagement items. Intersectional analysis (gender by job level, or gender by tenure) reveals gaps of 15-20 percentage points. Analyze gender combined with job level, tenure, or department. Women in technical roles report 12% lower development scores than women in other functions, while men show no such gap — a pattern single-demographic analysis misses entirely.

How do response rates affect analysis?

Expect lower response rates the first time you include DEIB demographics. Many employees will hesitate to self-identify until they see proof that the data stays anonymous.

Track each survey’s opt-in rate by demographic question. Rising participation shows growing trust; flat or declining rates signal that some employees still worry about being identified.

What are our tips and best practices?

Define specific questions before adding demographics: Will you compare promotion rates across identity groups? Analyze pay equity by gender and race? Measure inclusion perceptions for employees with disabilities? Each question determines which demographics you need.
When collecting the data, make sure your demographic questions are appropriate and legal to use as well as clear and easy to understand.
Communicate the purpose of monitoring demographic data to all employees. Being open about why this information is collected builds trust with employees. Train analysts to recognize statistical significance versus practical significance, understand intersectionality, and identify when small sample sizes make comparisons unreliable. Analysts must know that a 5-point difference with 50 respondents means less than a 3-point difference with 500 respondents.
Maintain confidentiality. We know many demographic questions can be personal and, in some cases, can lead to serious consequences for employees if not kept confidential.
Secure executive sponsorship by showing leaders how demographic data connects to business outcomes. When the CEO explains that DEIB data helps the company reduce turnover by 15% and improve innovation scores by 20%, employees understand why their responses matter.
Consult groups within the organization. If you have established employee resource groups (ERGs) and legal teams in place, consult their advice.

Frequently asked questions

What is a DEIB survey?

A DEIB survey is an employee survey that measures how different groups experience diversity, equity, inclusion, and belonging at work. Organizations use DEIB survey results to identify gaps by demographic group, prioritize actions, and track progress over time.

What are the four core components of DEIB?

Diversity refers to representation across identities and backgrounds. Equity focuses on fair access to opportunities, resources, and outcomes. Inclusion measures whether people feel respected, heard, and able to contribute. Belonging reflects whether employees feel accepted and valued as part of the organization.

How can Perceptyx help with DEIB?

Organizations that analyze DEIB data by intersectional demographics see 25% higher engagement scores and 30% lower turnover among underrepresented groups compared to those using single-demographic analysis. Perceptyx’s DEIB listening solutions can deliver the timely insights that help you create a more inclusive and equitable workplace while distinguishing your organization in a competitive market for talent. To learn more, schedule a meeting with a member of our team.