방법 비교

	List Experiment	Randomized Response Technique
분야	Political Science	Political Science
계열	Process / pipeline	Process / pipeline
기원 연도	2011	1965
창시자	Survey methodology; modern estimators by Kosuke Imai, Graeme Blair, Adam Glynn	Stanley L. Warner
유형	Sensitive-question survey experiment	Sensitive-question survey technique
원전	Imai, K. (2011). Multivariate Regression Analysis for the Item Count Technique. Journal of the American Statistical Association, 106(494), 407–416.	Warner, S. L. (1965). Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. Journal of the American Statistical Association, 60(309), 63–69.
별칭	Item count technique, Unmatched count technique, Item count method, List randomization	RRT, Randomized response, Warner's randomized response, Forced-response technique
관련	3	3
요약	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.	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.