Search for: All records

Abstract Pairwise comparison models are an important type of latent attribute measurement model with broad applications in the social and behavioural sciences. Current pairwise comparison models are typically unidimensional. The existing multidimensional pairwise comparison models tend to be difficult to interpret and they are unable to identify groups of raters that share the same rater-specific parameters. To fill this gap, we propose a new multidimensional pairwise comparison model with enhanced interpretability which explicitly models how object attributes on different dimensions are differentially perceived by raters. Moreover, we add a Dirichlet process prior on rater-specific parameters which allows us to flexibly cluster raters into groups with similar perceptual orientations. We conduct simulation studies to show that the new model is able to recover the true latent variable values from the observed binary choice data. We use the new model to analyse original survey data regarding the perceived truthfulness of statements on COVID-19 collected in the summer of 2020. By leveraging the strengths of the new model, we find that the partisanship of the speaker and the partisanship of the respondent account for the majority of the variation in perceived truthfulness, with statements made by co-partisans being viewed as more truthful. 

Does treatment mode matter in studies of the effects of candidate race or ethnicity on voting decisions? The assumption implicit in most such work is that such treatment mode differences are either small and/or theoretically well understood, so that the choice of how to signal the race of a candidate is largely one of convenience. But this assumption remains untested. Using a nationally representative sample of white voting-age citizens and a modified conjoint design, we evaluate whether signaling candidate ethnicity with ethnic labels and names results in different effects than signaling candidate ethnicity with ethnically identifiable photos and names. Our results provide strong evidence that treatment-mode effects are substantively large and statistically significant. Further, these treatment-mode effects are not consistent with extant theoretical accounts. These results highlight the need for additional theoretical and empirical work on race/ethnicity treatment-mode effects.