เปรียบเทียบวิธี

	Randomized Response Technique	Expert Survey	List Experiment	Survey Experiment
สาขาวิชา	Political Science	Political Science	Political Science	Political Science
ตระกูล	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
ปีกำเนิด	1965	—	2011	2011
ผู้ริเริ่ม	Stanley L. Warner	Comparative party-positioning research (Castles & Mair; Chapel Hill team)	Survey methodology; modern estimators by Kosuke Imai, Graeme Blair, Adam Glynn	Experimental political science; synthesized by Diana Mutz
ประเภท	Sensitive-question survey technique	Survey of subject-matter experts to measure latent positions	Sensitive-question survey experiment	Randomized experiment embedded in a survey
แหล่งต้นตำรับ	Warner, S. L. (1965). Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. Journal of the American Statistical Association, 60(309), 63–69.	Bakker, R., de Vries, C., Edwards, E., Hooghe, L., Jolly, S., Marks, G., Polk, J., Rovny, J., Steenbergen, M., & Vachudova, M. A. (2015). Measuring Party Positions in Europe: The Chapel Hill Expert Survey Trend File, 1999–2010. Party Politics, 21(1), 143–152.	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
ชื่อเรียกอื่น	RRT, Randomized response, Warner's randomized response, Forced-response technique	Expert judgment survey, Party expert survey, Chapel Hill Expert Survey, Expert placement survey	Item count technique, Unmatched count technique, Item count method, List randomization	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
ที่เกี่ยวข้อง	3	4	3	4
สรุป	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.	An expert survey measures latent political quantities — most often parties' positions on policy dimensions — by asking a panel of country and subject-matter experts to place the objects of interest on structured numerical scales. Averaging many experts' judgments yields position estimates, while the spread across experts provides a built-in measure of uncertainty and reliability. The Chapel Hill Expert Survey is the leading example, producing comparable measures of European parties' positions on ideology, European integration, and many specific issues over time.	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.