เปรียบเทียบวิธี

	List Experiment	Conjoint Survey Experiment
สาขาวิชา	Political Science	Political Science
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2011	2014
ผู้ริเริ่ม	Survey methodology; modern estimators by Kosuke Imai, Graeme Blair, Adam Glynn	Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto
ประเภท	Sensitive-question survey experiment	Multi-attribute forced-choice survey experiment with design-based causal estimands
แหล่งต้นตำรับ	Imai, K. (2011). Multivariate Regression Analysis for the Item Count Technique. Journal of the American Statistical Association, 106(494), 407–416.	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.
ชื่อเรียกอื่น	Item count technique, Unmatched count technique, Item count method, List randomization	Causal conjoint, Forced-choice conjoint experiment, AMCE conjoint, Conjoint experiment
ที่เกี่ยวข้อง	3	4
สรุป	The list experiment, also called the item count technique, is a survey design that measures the prevalence of a sensitive attitude or behavior without ever requiring any respondent to directly disclose it. Respondents are randomly split into two groups: a control group sees a list of innocuous items and reports only how many apply to them, while a treatment group sees the same list plus one sensitive item. Because respondents report only a count, no individual answer reveals their stance on the sensitive item, and the difference in average counts between the groups estimates the proportion holding the sensitive trait.	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.