Comparer des méthodes

	Conjoint Survey Experiment	Factorial Survey Experiment
Domaine	Political Science	Political Science
Famille	Process / pipeline	Process / pipeline
Année d'origine	2014	1982
Auteur d'origine	Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto	Peter H. Rossi and collaborators
Type	Multi-attribute forced-choice survey experiment with design-based causal estimands	Multi-factor randomized vignette experiment
Source fondatrice	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.	Wallander, L. (2009). 25 Years of Factorial Surveys in Sociology: A Review. Social Science Research, 38(3), 505–520.
Alias	Causal conjoint, Forced-choice conjoint experiment, AMCE conjoint, Conjoint experiment	Factorial survey, Factorial survey approach, Multi-factor vignette survey, Rossi vignette method
Apparentées	4	3
Résumé	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 factorial survey experiment, often simply called a factorial survey, asks respondents to judge short descriptions — vignettes — whose multiple features are fully crossed and randomly varied. By factorially combining many dimensions, each at several levels, the design generates a large universe of vignettes; respondents rate a random sample of them, and regression of the ratings on the dimension levels recovers the independent causal effect of each feature on judgment. It scales the single-scenario vignette experiment up to many simultaneously manipulated attributes.