방법 비교

	Multidimensional Unfolding	Survey Experiment
분야	Political Science	Political Science
계열	Latent structure	Process / pipeline
기원 연도	2000	2011
창시자	Keith T. Poole (nonparametric optimal classification and unfolding)	Experimental political science; synthesized by Diana Mutz
유형	Latent-space scaling model placing individuals and stimuli in a joint space	Randomized experiment embedded in a survey
원전	Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237.	Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
별칭	Unfolding analysis, Optimal classification, Preference unfolding, Joint-space scaling	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
관련	5	4
요약	Multidimensional unfolding places both individuals and the stimuli they evaluate — candidates, parties, bills — in a single joint low-dimensional space, so that each person's preferences are explained by their proximity to the stimuli. In political science it underlies Keith Poole's nonparametric optimal classification of roll-call votes and the unfolding of thermometer ratings and rank orders, recovering legislators' and bills' positions from nothing but the pattern of choices. Unlike correlation-based scaling, unfolding treats preference as a single-peaked function of distance: you like what is close to you and dislike what is far.	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.