A multidimensional pairwise comparison model for heterogeneous perceptions with an application to modelling the perceived truthfulness of public statements on COVID-19.

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 si

SUBMITTER: Yu Q 

PROVIDER: S-EPMC9115520 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature