Item response theory is a widely used framework for the design, scoring, and scaling of measurement instruments. Item response models are typically used for dichotomously scored questions that have only two score points (e.g., multiple-choice items). However, given the increasing use of instruments that include questions with multiple response categories, such as surveys, questionnaires, and psychological scales, polytomous item response models are becoming more utilized in education and psychology. This study aims to demonstrate the application of explanatory item response models to polytomous item responses in order to explain common variability in item clusters, person groups, and interactions between item clusters and person groups. Explanatory forms of several polytomous item response models – such as Partial Credit Model and Rating Scale Model – are demonstrated and the estimation procedures of these models are explained. Findings of this study suggest that explanatory item response models can be more robust and parsimonious than traditional item response models for polytomous data where items and persons share common characteristics. Explanatory polytomous item response models can provide more information about response patterns in item responses by estimating fewer item parameters.

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多项目反应的解释性反应模型

项目响应理论是用于测量仪器的设计，评分和定标的广泛使用的框架。项目响应模型通常用于仅具有两个得分点的二分计分的问题（例如，多项选择项）。但是，由于越来越多地使用包含诸如调查，问卷和心理量表等具有多种回答类别的问题的工具，因此多项回答模型在教育和心理学中得到了越来越多的利用。这项研究旨在证明解释性项目反应模型在多项目项反应中的应用，以便解释项目聚类，人员组以及项目聚类和人员组之间的相互作用的常见变异性。演示了多种多项目响应模型的解释形式，例如部分信用模型和等级量表模型，并解释了这些模型的估计过程。这项研究的发现表明，对于项目和人员具有共同特征的多态数据，解释性项目响应模型比传统的项目响应模型更健壮和简约。解释性多项目响应模型可以通过估计较少的参数来提供有关响应中响应模式的更多信息。这项研究的结果表明，对于项目和人员具有共同特征的多态数据，解释性项目响应模型比传统的项目响应模型更健壮和简约。解释性多项目响应模型可以通过估计较少的参数来提供有关响应中响应模式的更多信息。这项研究的结果表明，对于项目和人员具有共同特征的多态数据，解释性项目响应模型比传统的项目响应模型更健壮和简约。解释性多项目响应模型可以通过估计较少的参数来提供有关响应中响应模式的更多信息。