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Education & Academic

Sensitive Topic List Experiment (Item Count)

Measure behaviors people won't admit directly: the list experiment (item count technique) asks only HOW MANY statements apply — never which — so individual answers stay genuinely deniable while group comparisons reveal the true rate. The native question type randomizes control and treatment lists for you.

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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

This study uses a technique designed for honest answers on sensitive topics: you'll see a short list of statements and tell us only HOW MANY apply to you — never which ones. Your responses are completely confidential and anonymized. That means no answer you give can reveal anything specific about you.

Q02
設問必須

How many of the following statements apply to you? Count them privately, then enter only the number.

Q03
オピニオンスケール必須

How comfortable did you feel answering honestly with this counting format?

スケール: 1 – 5
最小:Still felt exposed最大:Completely safe
Q04
選択式必須

In general, how sensitive do you consider this topic among your peers?

  • Not sensitive — people discuss it openly
  • Somewhat sensitive
  • Very sensitive — rarely discussed honestly
Q05
AIインタビュー

WITHOUT ever asking whether the sensitive statement applied to them, explore the topic's social context: why people in their environment might underreport this behavior in normal surveys, what social or professional consequences drive that, and what conditions (anonymity guarantees, framing, who's asking) make honest answers more likely. Keep the tone academic and never probe their personal count.

Q06
メッセージ

Thank you. Because different participants received slightly different lists, comparing group averages estimates how common the sensitive behavior really is — with no individual ever identifiable.

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 list-experiment question type randomizes control and treatment lists automatically — the method's hardest part, handled
  • Individual answers stay genuinely deniable: respondents only ever report a count, never which items
  • A comfort check validates that the format actually made honest answering feel safe
  • The AI interview explores the topic's social context without ever probing any individual's answer

Gradient Metrics

List Experiments

Applied methodology guide (not a drop-in survey template) explaining list experiments as indirect measurement of private opinion via the item-count technique: respondents report how many of a list they agree with, control vs. treatment lists differ by one sensitive item, and prevalence is the difference in means. Cites a real 19,000+ response deployment (Social Pressure Index with Populace). No competitor here ships this as a native, self-serve question type.

優れている点

  • Clear, correct explanation of the control-vs-treatment design and difference-in-means estimation
  • Grounds the method in a real large-sample deployment (19,000+ responses, Social Pressure Index)
  • Frames the practical use case: measuring the gap between public and private opinion on sensitive topics
  • Emphasizes the privacy guarantee that makes honest answers possible

物足りない点

  • It is a blog/methodology explainer, not a usable template or a built-in question type a researcher can drop into a survey
  • No tooling to auto-randomize respondents into control/treatment arms and enforce balanced allocation
  • No built-in estimator/report that computes prevalence and confidence intervals from collected data
  • No guardrails against ceiling/floor effects (list design) surfaced for a non-methodologist user

SensitiveQuestions.org (R 'list' package)

Statistical Methods for the Item Count Technique and List Experiment (R package 'list')

The canonical academic toolkit for list-experiment analysis: an R package implementing multivariate/random-effects/Bayesian MCMC regression, joint modeling, combined list+direct-question estimators, and statistical tests to detect list-experiment failure. Authoritative on analysis, but it is code for researchers post-collection, with no fielding UI, randomization, or respondent experience.

優れている点

  • Comprehensive, peer-reviewed estimators (multivariate, random-effects, Bayesian MCMC hierarchical regression)
  • Supports advanced designs: multiple sensitive items, list experiments as predictors, combined list+direct estimates
  • Includes diagnostics and placebo tests to detect list-experiment failure
  • Grounded in six methods papers (2011-2016), the field standard for analysis

物足りない点

  • Analysis-only R code; provides no survey fielding, randomization, or respondent-facing UI
  • Requires statistical programming expertise, out of reach for a typical survey author
  • No integration with data collection: the researcher must field the experiment elsewhere and export data
  • No auto-generated plain-language report; outputs are statistical objects, not decision-ready summaries

よくあるご質問

「Sensitive Topic List Experiment (Item Count)」テンプレートにはどのような設問が含まれていますか？

すぐに使える設問が6問含まれており、最初の設問は次のとおりです：「This study uses a technique designed for honest answers on sensitive topics: you'll see a short list of statements and t…」・「How many of the following statements apply to you? Count them privately, then enter only the number.」・「How comfortable did you feel answering honestly with this counting format?」。すべての設問は上でプレビューでき、自由に編集できます。

このアンケートの回答にはどのくらい時間がかかりますか？

回答者は通常、6問を約5分で回答し終えます。

テンプレートは編集できますか？

はい。公開前であれば、すべての設問、選択肢、順序を編集できます。設問の追加や削除のほか、調査の目的に合わせた作り直しをAIエディターに依頼することもできます。

このテンプレートは無料で使えますか？

はい。エディターで開けば、すぐに編集を始められます。お試しにアカウントは不要で、無料プランでアンケートを公開できます。

公開の準備はできましたか？

このテンプレートをエディターで開いてみてください。最初の回答者が目にする前に、すべてを自由に変更できます。

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