Q01
Message
All templatesIndustry-Specific
Employee Pay Equity & Wage Gap Perception Survey
Measures how fairly employees believe compensation is distributed across gender, race, tenure, and role, and pinpoints which pay decisions or comparisons shape that perception. Built for HR and People Analytics teams auditing pay equity, with an AI follow-up that reconstructs the specific story behind a respondent's lowest fairness rating instead of just the number.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
16 questions · ~8 min
Q02
Multiple ChoiceRequired
Are you aware of this organization's approach to ensuring equal pay for equal work (e.g., published pay bands, regular pay equity audits)?
- Yes, I've seen specific information (bands, audit results, etc.)
- I've heard it's a priority but haven't seen details
- No, I'm not aware of any approach
- Not sure
Q03
Opinion ScaleRequired
Overall, how fairly do you believe your pay compares to others doing similar work at this organization?
Scale: 1 – 7
Min:Very unfairlyMax:Very fairly
Q04
MatrixRequired
Based on what you've observed or heard, how does each of the following tend to affect pay outcomes at this organization?
6 rows × 5 columns
- Gender
- Race or ethnicity
- Age
- Tenure at the company
- Educational background
- +1 more
Columns: Much lower pay · Somewhat lower pay · No noticeable difference · Somewhat higher pay · Much higher pay
Q05
Multiple Choice
In the last 12 months, have you compared your pay to publicly available salary data or to coworkers' pay?
- Yes, using public salary data (e.g., a salary benchmarking site)
- Yes, by talking with coworkers
- Yes, both
- No, I haven't compared
Q06
Best–Worst Trade-off (MaxDiff)Required
Which of the following do you think has the biggest influence on pay differences between employees here?
- Job performance
- Years of experience
- Negotiation skill
- Manager relationships
- Job level or title
- Location or remote-work status
- Demographic characteristics (e.g., gender, race)
Pick best & worst per setBest:Most influences pay differences hereWorst:Least influences pay differences here
Q07
Opinion ScaleRequired
How confident are you that pay decisions here — raises, promotions, starting offers — are made without bias toward any group?
Scale: 1 – 5
Min:Not at all confidentMax:Completely confident
Q08
AI InterviewRequired
Reconstruct the specific experience behind this respondent's fairness rating and their confidence-in-bias-free-decisions rating. If either score was low or mid-range, ask for a concrete example — a raise, promotion, or comparison with a colleague — that shaped that view, and which demographic factor (if any) they suspect was involved. If both scores were high, ask what evidence or communication gave them that confidence, so we can identify what's working.
Q09
Multiple Choice
Has your manager or HR ever proactively explained how your pay was determined?
- Yes, in detail
- Yes, briefly
- No, never
- Don't recall
Q10
Rating Scale
How satisfied are you with how clearly this organization communicates about pay decisions and pay equity efforts?
Range: 1 – 5
Min:Very dissatisfiedMax:Very satisfied
Q11
Long Text
What one change would most improve your confidence that pay is determined fairly here? (Optional)
Q12
Message
Just a few optional background questions to help us spot patterns across groups — feel free to skip any of these.
Q13
Multiple Choice
Which gender do you identify with?
- Woman
- Man
- Non-binary
- Prefer to self-describe
- Prefer not to say
Q14
Multiple Choice
Which best describes your race or ethnicity?
- American Indian or Alaska Native
- Asian
- Black or African American
- Hispanic or Latino
- Native Hawaiian or Pacific Islander
- White
- Two or more races
- Prefer to self-describe
- Prefer not to say
Q15
Multiple Choice
How long have you been with this organization?
- Less than 1 year
- 1-3 years
- 4-7 years
- 8+ years
- Prefer not to say
Q16
Message
Thank you for your honesty. Your responses will be combined anonymously with others and reviewed by HR leadership as part of our pay equity assessment — no individual answers are shared with managers.
What’s included
AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- Goes beyond a single fairness rating with an AI follow-up interview that reconstructs the specific pay decision or comparison behind a respondent's lowest score
- Includes a max-diff exercise to rank which factors (gender, race, tenure, role) employees believe most drive pay gaps, not just whether one exists
- Pairs quantitative measures (opinion scales, matrix, rating) with an open-ended question and optional demographic breakdowns for gender, race, and tenure to segment results
- Publishes the AI's questions and follow-up logic transparently and auto-generates a report, with a free tier and $50/mo Business plan (no academic tier)
SurveyMonkey
Wage Gap Evaluation Template & QuestionsThis is a fielding-ready static template focused specifically on wage gap perception, making it a direct topical match. It relies on fixed question sets (likely scales and multiple choice) rather than adaptive probing, so any 'why' behind a low fairness score has to be inferred rather than reconstructed. It's a solid, established option for teams that just need standard wage-gap metrics without deeper narrative context.
What it does well
- Purpose-built specifically for wage gap perception rather than generic employee evaluation
- Backed by SurveyMonkey's large template library and familiar survey-building tools
- Likely quick to deploy given its templated, ready-to-use format
Where it falls short
- No adaptive AI follow-up to dig into the specific story behind a low fairness rating — respondents just leave a static score
- No voice AI interview option or guided screen-share tasks for richer qualitative context
- No published methodology or prompt-level transparency for how any follow-up questions (if present) are generated
Frequently asked questions
What questions are in the “Employee Pay Equity & Wage Gap Perception Survey” template?
The template includes 16 ready-to-use questions, starting with: “Thanks for taking a few minutes to share how you see pay fairness at our organization. Your answers are anonymous and fe…” · “Are you aware of this organization's approach to ensuring equal pay for equal work (e.g., published pay bands, regular p…” · “Overall, how fairly do you believe your pay compares to others doing similar work at this organization?”. The full set is previewed above, and every question is editable.
How long does this survey take to complete?
Respondents typically finish the 16 questions in about 8 minutes.
Can I customize this template?
Yes — every question, answer option, and the ordering is editable before you launch. You can add or remove questions, or ask the AI editor to rework the survey around your research goal.
Is this template free to use?
Yes. Open it in the editor and start customizing right away — no account required to try it, and the free plan covers launching your survey.
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
Related templates
More studies on similar topics.
Industry-Specific
Culture of Genius: Talent Mindset & Belonging Audit
Measures whether your organization or field rewards innate 'brilliance' over effort, curiosity, and collaboration — and how that mindset shapes psychological safety and belonging. Built for academic departments, R&D teams, and competitive knowledge-work cultures. The AI follow-up interview reconstructs a real moment respondents held back or witnessed talent-based bias, not just their general opinion about culture.
View templateIndustry-Specific
Pay Equity Audit: Employee Perceptions & Trust Survey
Measures employee awareness, fairness perceptions, and trust regarding organizational pay equity audits. Designed for HR and DEI teams to identify transparency gaps and prioritize communication and process improvements.
View templateIndustry-Specific
Employee Salary & Compensation Fairness Survey
Measures how employees feel about their pay, benefits, and the process behind raises — covering satisfaction, market comparison, and priority trade-offs between compensation elements. An AI follow-up interview digs into the specific reasons behind fairness or satisfaction scores, especially where perceptions of pay and market value diverge.
View templateIndustry-Specific
Pay Increase Fairness and Impact Survey
Captures how employees actually experienced their most recent pay increase — whether it felt fair, how it compares to expectations, and what should drive future raises — with an AI follow-up that digs into the reasoning behind fairness and retention ratings. Built for HR and compensation teams evaluating a recent pay cycle.
View templateIndustry-Specific
Pay Transparency & Compensation Fairness Pulse
An employee pulse survey measuring perceptions of pay clarity, fairness, and communication quality. Designed for HR and People teams seeking to identify compensation transparency gaps and prioritize total rewards improvements.
View templateIndustry-Specific
Employee Payroll Experience & Accuracy Survey
Measures how confident employees are that they're paid correctly and on time, where payroll errors and confusion actually happen, and how easy it is to get help when something looks wrong. An AI follow-up interview reconstructs the most recent payroll issue an employee ran into, step by step, instead of relying on a vague satisfaction score. Built for HR and payroll teams auditing their process.
View template