Replication Data for: Does Conjoint Analysis Mitigate Social Desirability Bias?

How can we elicit honest responses in surveys? Conjoint analysis has become a popular tool to address social desirability bias (SDB), or systematic survey misreporting on sensitive topics. However, there has been no direct evidence showing its suitability for this purpose. We propose a novel experimental design to identify conjoint analysis's ability to mitigate SDB. Specifically, we compare a standard, fully randomized conjoint design against a partially randomized design where only the sensitive attribute is varied between the two profiles in each task. We also include a control condition to remove confounding due to the increased attention to the varying attribute under the partially randomized design. We implement this empirical strategy in two studies on attitudes about environmental conservation and preferences about congressional candidates. In both studies, our estimates indicate that the fully randomized conjoint design could reduce SDB for the average marginal component effect (AMCE) of the sensitive attribute by about two-thirds of the AMCE itself. While encouraging, we caution that our results are exploratory and exhibit some sensitivity to alternative model specifications, suggesting the need for additional confirmatory evidence based on the proposed design.

如何在调查中获取真实作答？联合分析（Conjoint Analysis）已成为应对社会期望偏差（Social Desirability Bias, SDB）——即针对敏感话题的系统性调查误报行为——的主流工具。然而目前尚无直接证据表明其在此场景下的适用性。本研究提出一种全新实验设计，用以验证联合分析缓解社会期望偏差的能力。具体而言，我们将标准完全随机化联合分析设计与部分随机化设计进行对比：后者仅在每项任务的两个方案间变动敏感属性。我们还设置了对照组，以排除部分随机化设计中因对变动属性关注度提升而带来的混淆效应。我们在两项研究中应用该实证策略：一项针对环境保护态度，另一项针对国会候选人偏好。两项研究的估计结果均显示，完全随机化联合分析设计可将敏感属性的平均边际成分效应（Average Marginal Component Effect, AMCE）对应的社会期望偏差降低约该效应本身的三分之二。尽管结果颇具启发性，但本研究提醒：本次发现仍属探索性分析，且对不同模型设定存在一定敏感性，这表明需基于本研究提出的设计开展更多验证性研究。