方法对比

	Vignette Experiment	Conjoint Survey Experiment	Survey Experiment
领域	Political Science	Political Science	Political Science
方法族	Process / pipeline	Process / pipeline	Process / pipeline
起源年份	—	2014	2011
提出者	Survey and social-psychological research traditions	Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto	Experimental political science; synthesized by Diana Mutz
类型	Randomized experiment using short described scenarios	Multi-attribute forced-choice survey experiment with design-based causal estimands	Randomized experiment embedded in a survey
开创性文献	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.	Mutz, D. C. (2011). Population-Based Survey Experiments. Princeton, NJ: Princeton University Press. ISBN: 9780691144528
别名	Vignette study, Experimental vignette, Scenario experiment, Text-vignette experiment	Causal conjoint, Forced-choice conjoint experiment, AMCE conjoint, Conjoint experiment	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
相关	3	4	4
摘要	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.	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.