Сравнение на методи

	Bayesian Item Response Theory in Politics	Survey Experiment
Област	Political Science	Political Science
Семейство	Latent structure	Process / pipeline
Година на възникване	2004	2011
Създател	Clinton, Jackman & Rivers (political IRT formulation); Treier & Jackman (latent-trait measurement)	Experimental political science; synthesized by Diana Mutz
Тип	Latent-variable measurement model for binary and ordinal items	Randomized experiment embedded in a survey
Основополагащ източник	Clinton, J., Jackman, S., & Rivers, D. (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98(2), 355–370.	Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
Други названия	Bayesian IRT, Political item response model, Latent trait measurement model, Bayesian latent measurement in politics	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
Свързани	5	4
Резюме	Bayesian 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.