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.

中文翻译：

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异质感知的多维成对比较模型，用于对有关 COVID-19 的公开声明的感知真实性进行建模


成对比较模型是一种重要的潜在属性测量模型，在社会和行为科学中具有广泛的应用。当前的成对比较模型通常是一维的。现有的多维成对比较模型往往难以解释，并且无法识别共享相同评估者特定参数的评估者群体。为了填补这一空白，我们提出了一种新的多维成对比较模型，具有增强的可解释性，该模型明确地模拟了评估者如何差异化地感知不同维度上的对象属性。此外，我们在特定于评估者的参数上添加了狄利克雷过程，这使我们能够灵活地将评估者分为具有相似感知取向的组。我们进行模拟研究，表明新模型能够从观察到的二元选择数据中恢复真实的潜在变量值。我们使用新模型来分析 2020 年夏季收集的关于 COVID-19 陈述的感知真实性的原始调查数据。通过利用新模型的优势，我们发现发言者的党派倾向和受访者的党派倾向解释了感知真实性方面的大部分差异，同党发表的声明被认为更真实。