方法对比

	Expert Survey	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	—	2011	2011
提出者	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
类型	Survey of subject-matter experts to measure latent positions	Sensitive-question survey experiment	Randomized experiment embedded in a survey
开创性文献	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
别名	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
相关	4	3	4
摘要	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.