Education & Academic

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.

このテンプレートを使うほかのテンプレートを見る

設問の例

テンプレートの内容をプレビューできます。すべての設問は公開前に自由に編集できます。

全6問・約5分

Q01

メッセージ

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

設問必須

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

Q03

オピニオンスケール必須

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

スケール: 1 – 7

最小:Wouldn't consider switching最大:Would start looking immediately

Q04

自由回答（短文）必須

Which detail of the scenario most influenced your rating?

Q05

AIインタビュー

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

メッセージ

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.

含まれる機能

AIによる深掘り
自由回答に合わせてAIが追加で質問し、固定のフォームでは拾えない具体的な内容を引き出します。
注意確認設問
急いだ回答や質の低い回答者を除外する仕組みを標準で備えています。
AIが作成する設問文
文言、設問の順序、条件分岐をAIが調査の目的に合わせて作成します。
自動レポート
回答が集まると、テーマ、引用、わかりやすい要約が自動で作成されます。

他ツールとの比較

ほかのアンケートツールで最も近いテンプレートを調べました。それぞれの優れている点と、このテンプレートがさらに踏み込んでいる点をまとめています。

このテンプレートを選ぶ理由

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.

優れている点

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

物足りない点

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

よくあるご質問