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| List Experiment× | Survey Experiment× | |
|---|---|---|
| Fachgebiet | Political Science | Political Science |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr | 2011 | 2011 |
| Urheber≠ | Survey methodology; modern estimators by Kosuke Imai, Graeme Blair, Adam Glynn | Experimental political science; synthesized by Diana Mutz |
| Typ≠ | Sensitive-question survey experiment | Randomized experiment embedded in a survey |
| Wegweisende Quelle≠ | Imai, K. (2011). Multivariate Regression Analysis for the Item Count Technique. Journal of the American Statistical Association, 106(494), 407–416. DOI ↗ | Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528 |
| Aliasnamen | Item count technique, Unmatched count technique, Item count method, List randomization | Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment |
| Verwandt≠ | 3 | 4 |
| Zusammenfassung≠ | The list experiment, also called the item count technique, is a survey design that measures the prevalence of a sensitive attitude or behavior without ever requiring any respondent to directly disclose it. Respondents are randomly split into two groups: a control group sees a list of innocuous items and reports only how many apply to them, while a treatment group sees the same list plus one sensitive item. Because respondents report only a count, no individual answer reveals their stance on the sensitive item, and the difference in average counts between the groups estimates the proportion holding the sensitive trait. | 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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