Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Conjoint Survey Experiment× | Survey Experiment× | |
|---|---|---|
| Область | Political Science | Political Science |
| Семейство | Process / pipeline | Process / pipeline |
| Год появления≠ | 2014 | 2011 |
| Автор метода≠ | Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto | Experimental political science; synthesized by Diana Mutz |
| Тип≠ | Multi-attribute forced-choice survey experiment with design-based causal estimands | Randomized experiment embedded in a survey |
| Основополагающий источник≠ | Hainmueller, J., Hopkins, D. J., & Yamamoto, T. (2014). Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments. Political Analysis, 22(1), 1–30. DOI ↗ | Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528 |
| Другие названия | Causal conjoint, Forced-choice conjoint experiment, AMCE conjoint, Conjoint experiment | Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment |
| Связанные | 4 | 4 |
| Сводка≠ | A conjoint survey experiment presents respondents with profiles — of candidates, immigrants, policies, or products — described by several attributes whose levels are independently randomized, and asks respondents to choose between or rate the profiles. Hainmueller, Hopkins, and Yamamoto's 2014 framework places this design on a rigorous causal footing, defining the average marginal component effect (AMCE) as the design-based causal effect of an attribute level, averaged over the randomization distribution of all other attributes. It lets political scientists estimate the relative causal weight of many decision factors simultaneously from realistic, multidimensional choices. | 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. |
| ScholarGateНабор данных ↗ |
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