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| Multidimensional Unfolding× | Survey Experiment× | |
|---|---|---|
| Field | Political Science | Political Science |
| Family≠ | Latent structure | Process / pipeline |
| Year of origin≠ | 2000 | 2011 |
| Originator≠ | Keith T. Poole (nonparametric optimal classification and unfolding) | Experimental political science; synthesized by Diana Mutz |
| Type≠ | Latent-space scaling model placing individuals and stimuli in a joint space | Randomized experiment embedded in a survey |
| Seminal source≠ | Poole, K. T. (2000). Nonparametric Unfolding of Binary Choice Data. Political Analysis, 8(3), 211–237. DOI ↗ | Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528 |
| Aliases | Unfolding analysis, Optimal classification, Preference unfolding, Joint-space scaling | Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment |
| Related≠ | 5 | 4 |
| Summary≠ | 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. |
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