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AbstractConjoint analysis is widely used for estimating the effects of a large number of treatments on multidimensional decision-making. However, it is this substantive advantage that leads to a statistically undesirable property, multiple hypothesis testing. Existing applications of conjoint analysis except for a few do not correct for the number of hypotheses to be tested, and empirical guidance on the choice of multiple testing correction methods has not been provided. This paper first shows that even when none of the treatments has any effect, the standard analysis pipeline produces at least one statistically significant estimate of average marginal component effects in more than 90% of experimental trials. Then, we conduct a simulation study to compare three well-known methods for multiple testing correction, the Bonferroni correction, the Benjamini–Hochberg procedure, and the adaptive shrinkage (Ash). All three methods are more accurate in recovering the truth than the conventional analysis without correction. Moreover, the Ash method outperforms in avoiding false negatives, while reducing false positives similarly to the other methods. Finally, we show how conclusions drawn from empirical analysis may differ with and without correction by reanalyzing applications on public attitudes toward immigration and partner countries of trade agreements.

AbstractA common approach when studying the quality of representation involves comparing the latent preferences of voters and legislators, commonly obtained by fitting an item response theory (IRT) model to a common set of stimuli. Despite being exposed to the same stimuli, voters and legislators may not share a common understanding of how these stimuli map onto their latent preferences, leading to differential item functioning (DIF) and incomparability of estimates. We explore the presence of DIF and incomparability of latent preferences obtained through IRT models by reanalyzing an influential survey dataset, where survey respondents expressed their preferences on roll call votes that U.S. legislators had previously voted on. To do so, we propose defining a Dirichlet process prior over item response functions in standard IRT models. In contrast to typical multistep approaches to detecting DIF, our strategy allows researchers to fit a single model, automatically identifying incomparable subgroups with different mappings from latent traits onto observed responses. We find that although there is a group of voters whose estimated positions can be safely compared to those of legislators, a sizeable share of surveyed voters understand stimuli in fundamentally different ways. Ignoring these issues can lead to incorrect conclusions about the quality of representation.

AbstractDespite significant debate about the ability of international law to constrain state behavior, recent research points to domestic mechanisms that deter non-compliance, most notably public disapproval of governments that violate treaty agreements. However, existing studies have not explicitly differentiated two distinct, theoretically important motivations that underlie this disapproval: respect for legal obligations versus the desire to follow common global practices. We design an innovative survey experiment in Japan that manipulates information about these two potential channels directly. We examine attitudes towards four controversial practices that fall afoul of international law—same-surname marriage, whaling, hate speech regulation, and capital punishment—and find that the legal obligation cue has a stronger effect on respondent attitudes than the common practices cue. We also show subgroup differences based on partisanship and identification with global civil society. These results demonstrate that the legal nature of international law is crucial to domestic compliance pull.