모든 템플릿

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.

샘플 질문

템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.

질문 16개 · 약 8분
Q01
메시지

Thanks for taking a few minutes to share how you see pay fairness at our organization. Your answers are anonymous and feed directly into our pay equity review — this takes about 5-6 minutes.

Q02
객관식필수

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
의견 척도필수

Overall, how fairly do you believe your pay compares to others doing similar work at this organization?

척도: 17
최소:Very unfairly최대:Very fairly
Q04
매트릭스필수

Based on what you've observed or heard, how does each of the following tend to affect pay outcomes at this organization?

6개 행 × 5개 열
  • Gender
  • Race or ethnicity
  • Age
  • Tenure at the company
  • Educational background
  • 외 1개
: Much lower pay · Somewhat lower pay · No noticeable difference · Somewhat higher pay · Much higher pay
Q05
객관식

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
최선·최악 선택형(MaxDiff)필수

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)
세트별 최선·최악 선택최선:Most influences pay differences here최악:Least influences pay differences here
Q07
의견 척도필수

How confident are you that pay decisions here — raises, promotions, starting offers — are made without bias toward any group?

척도: 15
최소:Not at all confident최대:Completely confident
Q08
AI 인터뷰필수

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
객관식

Has your manager or HR ever proactively explained how your pay was determined?

  • Yes, in detail
  • Yes, briefly
  • No, never
  • Don't recall
Q10
평점 척도

How satisfied are you with how clearly this organization communicates about pay decisions and pay equity efforts?

범위: 15
최소:Very dissatisfied최대:Very satisfied
Q11
장문형

What one change would most improve your confidence that pay is determined fairly here? (Optional)

Q12
메시지

Just a few optional background questions to help us spot patterns across groups — feel free to skip any of these.

Q13
객관식

Which gender do you identify with?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q14
객관식

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
객관식

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
메시지

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.

포함된 기능

  • AI 후속 질문

    정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.

  • 주의력 확인 장치

    성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.

  • AI가 작성한 문안

    문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.

  • 자동 리포트

    응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.

다른 서비스와 비교

다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.

이 템플릿을 선택하는 이유

  • 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 & Questions

This 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.

잘하는 점

  • 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

아쉬운 점

  • 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

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