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Ecological Inference×Survey Experiment×
क्षेत्रPolitical SciencePolitical Science
परिवारRegression modelProcess / pipeline
उद्भव वर्ष19972011
प्रवर्तकLeo Goodman (ecological regression); Gary King (statistical EI solution)Experimental political science; synthesized by Diana Mutz
प्रकारAggregate-data model inferring individual-level rates from grouped totalsRandomized experiment embedded in a survey
मौलिक स्रोतKing, G. (1997). A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data. Princeton: Princeton University Press. ISBN: 9780691012414Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
उपनामEI, Ecological regression, King's ecological inference, Aggregate-to-individual inferencePopulation-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
संबंधित54
सारांशEcological inference is the problem of learning about individual behavior — such as how Black and white voters cast their ballots — when only aggregate data are available, like precinct-level turnout and racial composition. Because individual-level data are missing, the within-group rates are not directly observed; ecological inference recovers them by combining the deterministic accounting constraints that each precinct must satisfy with a statistical model of how the unobserved rates vary across precincts. Gary King's 1997 solution unified the deterministic method of bounds with Leo Goodman's classic ecological regression, sharply reducing the long-standing risk of the ecological fallacy.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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