Replication Data for: Estimating and Using Individual Marginal Component Effects from Conjoint Experiments

Conjoint experiments are quickly gaining popularity as a vehicle for studying multidimensional political preferences. A common way to explore heterogeneity of preferences estimated with conjoint experiments is by estimating average marginal component effects across subgroups. However, this method does not give the researcher the full access to the variation of preferences in the studied populations, as that would require estimating effects on the individual level. Currently, there is no accepted technique to obtain estimates of individual-level preferences from conjoint experiments. In this paper, I address this gap by proposing a procedure to estimate individual preferences as respondent-specific marginal component effects. The proposed strategy does not require any additional assumptions compared to the standard conjoint analysis, although some changes to the task design are recommended. I also discuss methods to account for uncertainty in resulting estimates. Using the proposed procedure, I partially replicate a conjoint experiment on immigrant admission with necessary design adjustments. Then, I demonstrate how individual marginal component effects can be used to explore distributions of preferences, intercorrelations between different preference dimensions, and relationships of preferences to other variables of interest.联合实验（Conjoint Experiments）作为研究多维政治偏好的工具正迅速普及。借助联合实验估计偏好异质性的常用方法，是在各子群体间估算平均边际成分效应（Average Marginal Component Effects）。然而，该方法无法让研究者完整掌握研究总体中偏好的变异情况，因为这需要在个体层面估算效应值。当前，尚无公认的技术可从联合实验中获取个体层面偏好的估计值。本文针对这一研究空白，提出了将个体偏好估计为受访者专属边际成分效应（Respondent-Specific Marginal Component Effects）的研究流程。相较于标准联合分析，本文提出的策略无需额外假设，尽管研究中建议对任务设计做出部分调整。本文还探讨了处理估计结果不确定性的方法。借助所提流程，本文对一项关于移民准入的联合实验进行了带有必要设计调整的部分复刻。随后，本文展示了个体边际成分效应可用于探索偏好分布、不同偏好维度间的相互关联，以及偏好与其他相关变量之间的关系。