Data_Sheet_1_Estimation of Response Styles Using the Multidimensional Nominal Response Model: A Tutorial and Comparison With Sum Scores.zip

Recent years have seen a dramatic increase in item response models for measuring response styles on Likert-type items. These model-based approaches stand in contrast to traditional sum-score-based methods where researchers count the number of times that participants selected certain response options. The multidimensional nominal response model (MNRM) offers a flexible model-based approach that may be intuitive to those familiar with sum score approaches. This paper presents a tutorial on the model along with code for estimating it using three different software packages: flexMIRT®, mirt, and Mplus. We focus on specification and interpretation of response functions. In addition, we provide analytical details on how sum score to scale score conversion can be done with the MNRM. In the context of a real data example, three different scoring approaches are then compared. This example illustrates how sum-score-based approaches can sometimes yield scores that are confounded with substantive content. We expect that the current paper will facilitate further investigations as to whether different substantive conclusions are reached under alternative approaches to measuring response styles.

近年来，用于测量李克特型项目（Likert-type items）作答风格的项目反应模型（item response models）数量大幅增长。这类基于模型的分析路径，与传统的基于总分的计分方法形成鲜明对比：传统方法下，研究者仅统计参与者选择特定作答选项的频次。多维名义作答模型（multidimensional nominal response model, MNRM）提供了一种灵活的基于模型的分析路径，对于熟悉总分法的研究者而言易于理解。本文针对该模型提供了一份教程，并附带了使用三款不同软件包（flexMIRT®、mirt与Mplus）进行模型估计的代码。本文重点围绕作答函数的设定与解读展开论述。此外，本文还详细阐释了如何借助该多维名义作答模型实现总分至量表分的转换。随后，本文结合一个真实数据集示例，对三种不同的计分方法进行了对比分析。该示例直观展现了基于总分的计分方法有时会生成与实质研究内容相混淆的量表得分。我们期望本文能够推动后续研究，进一步探讨不同作答风格测量方法下是否会得出相异的实质研究结论。