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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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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

6 questions · ~5 min
Q01
Message

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
List experimentRequired

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

Q03
Opinion ScaleRequired

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

Scale: 1 – 5
Min:Still felt exposedMax:Completely safe
Q04
Multiple ChoiceRequired

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 Interview

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
Message

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.

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

  • 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.

What it does well

  • 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

Where it falls short

  • 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.

What it does well

  • 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

Where it falls short

  • 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

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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