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.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- 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
Curated claims
Claims persisted in the evidence ledger, each with its own assessment.
This view does not invent a claim assessment when the ledger has none.
Related methods
Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.