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Bayesian Item Response Theory in Politics×Survey Experiment×
분야Political SciencePolitical Science
계열Latent structureProcess / pipeline
기원 연도20042011
창시자Clinton, Jackman & Rivers (political IRT formulation); Treier & Jackman (latent-trait measurement)Experimental political science; synthesized by Diana Mutz
유형Latent-variable measurement model for binary and ordinal itemsRandomized experiment embedded in a survey
원전Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370. DOI ↗Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
별칭Bayesian IRT, Political item response model, Latent trait measurement model, Bayesian latent measurement in politicsPopulation-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
관련54
요약Bayesian item response theory (IRT) in political science measures latent traits — such as ideology, level of democracy, or political knowledge — from observed binary or ordinal items, treating each item's response probability as a function of a respondent's position on the latent scale. Formalized for politics by Clinton, Jackman, and Rivers (2004) for roll-call votes and extended by Treier and Jackman (2008) to measure democracy as a latent variable, the approach combines item characteristic curves with prior distributions and estimates everything jointly by Markov chain Monte Carlo, yielding full posterior uncertainty for every subject's latent score.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.
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