方法对比

	Randomized Response Technique	List Experiment	Survey Experiment
领域	Political Science	Political Science	Political Science
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	1965	2011	2011
提出者	Stanley L. Warner	Survey methodology; modern estimators by Kosuke Imai, Graeme Blair, Adam Glynn	Experimental political science; synthesized by Diana Mutz
类型	Sensitive-question survey technique	Sensitive-question survey experiment	Randomized experiment embedded in a survey
开创性文献	Warner, S. L. (1965). Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. Journal of the American Statistical Association, 60(309), 63–69.	Imai, K. (2011). Multivariate Regression Analysis for the Item Count Technique. Journal of the American Statistical Association, 106(494), 407–416.	Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
别名	RRT, Randomized response, Warner's randomized response, Forced-response technique	Item count technique, Unmatched count technique, Item count method, List randomization	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
相关	3	3	4
摘要	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.	The list experiment, also called the item count technique, is a survey design that measures the prevalence of a sensitive attitude or behavior without ever requiring any respondent to directly disclose it. Respondents are randomly split into two groups: a control group sees a list of innocuous items and reports only how many apply to them, while a treatment group sees the same list plus one sensitive item. Because respondents report only a count, no individual answer reveals their stance on the sensitive item, and the difference in average counts between the groups estimates the proportion holding the sensitive trait.	A survey experiment embeds a randomized experiment inside a survey: respondents are randomly assigned to different versions of a question, frame, or stimulus, and their answers are compared to estimate a causal effect. By combining the internal validity of randomization with the representative samples and rich measurement of survey research, survey experiments — especially population-based ones — let political scientists draw causal inferences about how information, framing, or message attributes shape public attitudes and behavior.