Comparer des méthodes

	Vignette Experiment	Conjoint Survey Experiment
Domaine	Political Science	Political Science
Famille	Process / pipeline	Process / pipeline
Année d'origine	—	2014
Auteur d'origine	Survey and social-psychological research traditions	Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto
Type	Randomized experiment using short described scenarios	Multi-attribute forced-choice survey experiment with design-based causal estimands
Source fondatrice	Atzmüller, C., & Steiner, P. M. (2010). Experimental Vignette Studies in Survey Research. Methodology, 6(3), 128–138.	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.
Alias	Vignette study, Experimental vignette, Scenario experiment, Text-vignette experiment	Causal conjoint, Forced-choice conjoint experiment, AMCE conjoint, Conjoint experiment
Apparentées	3	4
Résumé	A vignette experiment presents respondents with a short, carefully constructed description of a person, situation, or scenario — a vignette — in which one or more features are experimentally manipulated, and then asks for a judgment, attitude, or intended action. By randomizing which version of the scenario each respondent reads, the researcher isolates the causal effect of each manipulated feature on the elicited judgment, combining the realism of a concrete scenario with the causal leverage of an 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.