Vignette Experiment: Scenario & Framing Test

A true factorial vignette experiment: respondents judge realistic scenarios whose key details (price framing, messenger, wording) rotate systematically, so you can measure how each factor shifts judgment. The built-in vignette question type generates the scenario combinations for you.

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샘플 질문

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질문 6개 · 약 5분

Q01

메시지

You'll read a short, realistic scenario and give your honest reaction. Your responses are completely confidential and anonymized. Read it carefully — small details matter. There are no right answers; we're studying how people judge situations, not testing you.

Q02

질문필수

Please read the scenario and rate your agreement with the statement below it.

Q03

의견 척도필수

If this happened to a service you pay for, how likely would you be to look for an alternative?

척도: 1 – 7

최소:Wouldn't consider switching최대:Would start looking immediately

Q04

단문형필수

Which detail of the scenario most influenced your rating?

Q05

AI 인터뷰

Debrief the vignette judgment: which detail they weighed most and whether that surprised them, what would have made the scenario feel fair (or unfair), and how a similar real experience of theirs shaped the reaction. Do not reveal that other participants saw different versions until the end; then ask whether knowing that changes their view.

Q06

메시지

Thank you! Because different people saw systematically different versions, we can measure exactly how much each detail — the framing, the messenger, the reason — moved judgments.

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

A native vignette-experiment question type generates the factorial scenario combinations and randomization for you — no manual branching gymnastics
Behavioral follow-ups (switching likelihood) and a which-detail-mattered check accompany the core judgment scale
The AI debrief explores the reaction before revealing the manipulation, then tests whether knowing changes the judgment
Regression-ready structure: every respondent's scenario composition is recorded with their response

tickStat

Factorial vignette experiments - tickStat Documentation

Rare example of a survey platform with a genuinely native factorial-vignette feature: users define attributes and levels, the tool builds full or fractional factorial designs and assigns balanced randomized scenarios, respondents rate on a 0-100 slider or Likert scale, and it exports analysis-ready coded data for regression. Strong methodological tooling, but it's a rating/estimation instrument with no adaptive qualitative follow-up on why a respondent judged a scenario as they did.

잘하는 점

Genuinely native factorial-vignette engine (define attributes/levels; auto-generate full or fractional designs)
Balanced randomization so every level appears at the right frequency, across within- and between-subject designs
Choice of continuous 0-100 slider or Likert-style discrete response formats
Analysis-ready export with attribute levels pre-coded for regression

아쉬운 점

Collects only structured ratings; no adaptive AI follow-up asking why a scenario was judged that way
No auto-generated narrative report interpreting which attributes drove judgments
Design/methodology assumes a statistically literate user; little hand-holding for non-researchers
No pairing with a qualitative interview layer to explain surprising level effects

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