Katherine Clayton, Yusaku Horiuchi, Aaron R. Kaufman, Gary King, and Mayya Komisarchik
Abstract: Conjoint survey designs are spreading across the social sciences due to their unusual capacity to identify many causal effects from a single randomized experiment. Unfortunately, because the nature of conjoint designs violates aspects of best practices in questionnaire construction, they generate substantial measurement error-induced bias. By replicating both data collection and analysis of eight prominent conjoint studies, all of which closely reproduce published results, we show that about half of all observed variation in this most common type of conjoint experiment is effectively random noise. We then discover a common empirical pattern in how measurement error appears in conjoint studies and use it to derive an easy-to-use statistical method to correct the bias.