Conjoint Survey Experiment
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.
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Sources
- 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: 10.1093/pan/mpt024 ↗
- Hainmueller, J., Hangartner, D., & Yamamoto, T. (2015). Validating Vignette and Conjoint Survey Experiments against Real-World Behavior. Proceedings of the National Academy of Sciences, 112(8), 2395–2400. DOI: 10.1073/pnas.1416587112 ↗
- Leeper, T. J., Hobolt, S. B., & Tilley, J. (2020). Measuring Subgroup Preferences in Conjoint Experiments. Political Analysis, 28(2), 207–221. DOI: 10.1017/pan.2019.30 ↗
How to cite this page
ScholarGate. (2026, June 22). Conjoint Survey Experiment (Causal Conjoint Analysis). ScholarGate. https://scholargate.app/en/political-science/conjoint-survey-experiment
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Conjoint AnalysisExperimental design↔ compare
- Factorial Survey ExperimentPolitical Science↔ compare
- Survey ExperimentPolitical Science↔ compare
- Vignette ExperimentPolitical Science↔ compare