方法对比

	Endorsement Experiment	Randomized Response Technique
领域	Political Science	Political Science
方法族	Process / pipeline	Process / pipeline
起源年份	2011	1965
提出者	Bullock, Imai & Shapiro (statistical framework)	Stanley L. Warner
类型	Indirect survey experiment for sensitive latent support	Sensitive-question survey technique
开创性文献	Bullock, W., Imai, K., & Shapiro, J. N. (2011). Statistical Analysis of Endorsement Experiments: Measuring Support for Militant Groups in Pakistan. Political Analysis, 19(4), 363–384.	Warner, S. L. (1965). Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. Journal of the American Statistical Association, 60(309), 63–69.
别名	Endorsement question design, Endorsement experiment design, Indirect support measurement, Group-endorsement experiment	RRT, Randomized response, Warner's randomized response, Forced-response technique
相关	4	3
摘要	An endorsement experiment indirectly measures latent support for a sensitive or stigmatized actor by randomizing whether a policy is attributed to that actor and comparing how respondents' support for the policy shifts. Formalized statistically by Bullock, Imai, and Shapiro in 2011 to measure support for militant groups in Pakistan, the design infers favorability toward an actor that respondents would not safely disclose directly from the change in policy support it induces, typically estimated with hierarchical item-response models.	The randomized response technique (RRT) is a survey method for asking about sensitive or stigmatized topics while guaranteeing each respondent's privacy. Introduced by Stanley Warner in 1965, it uses a randomizing device — a coin, die, or spinner — to determine, privately and unknown to the interviewer, whether the respondent answers the sensitive question or an alternative. Because the analyst knows only the probability distribution of the device and not the outcome for any individual, no answer can be traced to a particular question, yet the population prevalence of the sensitive trait can be recovered exactly by inverting the known randomization.