Linganisha mbinu

	Factorial Survey Experiment	Conjoint Survey Experiment	Survey Experiment
Nyanja	Political Science	Political Science	Political Science
Familia	Process / pipeline	Process / pipeline	Process / pipeline
Mwaka wa asili	1982	2014	2011
Mwanzilishi	Peter H. Rossi and collaborators	Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto	Experimental political science; synthesized by Diana Mutz
Aina	Multi-factor randomized vignette experiment	Multi-attribute forced-choice survey experiment with design-based causal estimands	Randomized experiment embedded in a survey
Chanzo asilia	Wallander, L. (2009). 25 Years of Factorial Surveys in Sociology: A Review. Social Science Research, 38(3), 505–520.	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
Majina mbadala	Factorial survey, Factorial survey approach, Multi-factor vignette survey, Rossi vignette method	Causal conjoint, Forced-choice conjoint experiment, AMCE conjoint, Conjoint experiment	Population-based survey experiment, Survey-embedded experiment, Question-wording experiment, Framing experiment
Zinazohusiana	3	4	4
Muhtasari	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.	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.