方法对比

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