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| Randomized Response Technique× | Survey Experiment× | |
|---|---|---|
| Tudományterület | Political Science | Political Science |
| Módszercsalád | Process / pipeline | Process / pipeline |
| Keletkezés éve≠ | 1965 | 2011 |
| Megalkotó≠ | Stanley L. Warner | Experimental political science; synthesized by Diana Mutz |
| Típus≠ | Sensitive-question survey technique | Randomized experiment embedded in a survey |
| Alapmű≠ | Warner, S. L. (1965). Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias. Journal of the American Statistical Association, 60(309), 63–69. DOI ↗ | Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528 |
| Alternatív nevek | RRT, Randomized response, Warner's randomized response, Forced-response technique | Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment |
| Kapcsolódó≠ | 3 | 4 |
| Összefoglaló≠ | The randomized response technique (RRT) is a survey method for asking about sensitive or stigmatized topics while guaranteeing each respondent's privacy. Introduced by Stanley Warner in 1965, it uses a randomizing device — a coin, die, or spinner — to determine, privately and unknown to the interviewer, whether the respondent answers the sensitive question or an alternative. Because the analyst knows only the probability distribution of the device and not the outcome for any individual, no answer can be traced to a particular question, yet the population prevalence of the sensitive trait can be recovered exactly by inverting the known randomization. | 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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