방법 비교

	Multilevel Regression and Poststratification	Survey Experiment
분야	Political Science	Political Science
계열	Regression model	Process / pipeline
기원 연도	2004	2011
창시자	Gelman and Little (method); Park, Gelman & Bafumi (state-level application)	Experimental political science; synthesized by Diana Mutz
유형	Survey small-area estimation model combining multilevel regression with census poststratification	Randomized experiment embedded in a survey
원전	Park, D. K., Gelman, A., & Bafumi, J. (2004). Bayesian Multilevel Estimation with Poststratification: State-Level Estimates from National Polls. Political Analysis, 12(4), 375–385.	Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
별칭	MRP, Mister P, Multilevel regression with poststratification, Small-area opinion estimation	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
관련	5	4
요약	Multilevel regression and poststratification (MRP) estimates opinion or behavior in small subpopulations — states, districts, demographic groups — from a single national survey that is far too small to support direct estimates in each unit. It first fits a multilevel model that predicts the outcome from individual demographic and geographic characteristics, borrowing strength across units through partial pooling, and then poststratifies the predicted values to known population counts of demographic-by-geographic cells. Introduced for state-level opinion by Park, Gelman, and Bafumi (2004) and shown by Lax and Phillips (2009) to outperform disaggregation, MRP has become the standard tool for subnational opinion estimation.	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.