Replication Data for: Identification of Preferences in Forced-Choice Conjoint Experiments: Reassessing the Quantity of Interest

Forced-choice conjoint experiments have become a standard component of the experimental toolbox in political science and sociology. Yet the literature has largely overlooked the fact that conjoint experiments can be used for two distinct purposes: to uncover respondents' multidimensional preferences, and to estimate the causal effects of some attributes on a profile's selection probability in a multidimensional choice setting. This paper makes the argument that this distinction is both analytically and practically relevant, because the quantity of interest is contingent on the purpose of the study. The vast majority of social scientists relying on conjoint analyses, including most scholars interested in studying preferences, have adopted the average marginal component effect (AMCE) as their main quantity of interest. The paper shows that the AMCE is neither conceptually nor practically suited to explore respondents' preferences. Not only is it essentially a causal quantity conceptually at odds with the goal of describing patterns of preferences, but it also does generally not identify preferences, mixing them with compositional effects unrelated to preferences. This paper proposes a novel estimand—the average component preference (ACP)—designed to explore patterns of preferences, and it presents a method for estimating it.

强制选择型联合实验（forced-choice conjoint experiments）现已成为政治学与社会学实验工具箱中的标准组成部分。然而现有学术文献在很大程度上忽略了一个关键事实：联合实验可服务于两类截然不同的研究目标：其一为揭示受访者的多维偏好，其二为在多维选择情境中，估算特定属性对某一属性组合方案（profile）被选中概率的因果效应。本文提出，这一区分兼具分析与实践层面的重要意义，因为核心研究量会随研究目的的不同而发生变化。绝大多数依赖联合分析的社会科学家，包括多数以偏好研究为核心议题的学者，均将平均边际成分效应（average marginal component effect, AMCE）作为其核心研究量。本文证明，AMCE无论在概念层面还是实践层面，均不适用于探索受访者的偏好。从概念上而言，AMCE本质上属于因果性研究量，与描述偏好模式的研究目标相悖；不仅如此，AMCE通常也无法准确识别偏好，而是会将偏好与和偏好无关的构成效应混为一谈。本文提出了一种全新的估计量——平均成分偏好（average component preference, ACP），专门用于探索偏好模式，并给出了对应的估计方法。