Item Response Modeling of Forced-Choice Questionnaires
Item Response Modeling of Forced-Choice Questionnaires
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DOI：
10.1177/0013164410375112
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发表时间：
2011-06-01
影响因子：
2.7
通讯作者：
Maydeu-Olivares, Alberto
中科院分区：
文献类型：
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作者：
Brown, Anna;Maydeu-Olivares, Alberto
多维强迫选择格式可以显着减少与评级量表通常相关的众多响应偏差的影响。然而，如果用经典的方法进行评分，这些问卷会产生自相矛盾的数据，这会导致扭曲的尺度关系，并使个体之间的比较成为问题。本研究展示了如何项目反应理论（IRT）建模可以应用于克服这些问题。本文介绍了一种基于Thurstone比较数据框架的多维IRT模型，该模型适用于任何强迫选择问卷，该问卷由符合优势反应模型的项目组成，具有任何数量的测量性状和任何块大小（即，对、三胞胎、四胞胎等）。Thurstonian IRT模型是具有结构化因子负荷、结构化唯一性和结构化局部依赖性的正态拱形模型。这些模型可以直接估计使用结构方程建模（SEM）软件Mplus。通过大量的模拟研究，探讨了在不同的强迫选择设计下，潜在特质是如何恢复的，并为最佳问卷设计提供了指导。一个实证的应用程序来说明如何在实践中应用该模型。它的结论是，当推荐的设计准则得到满足，分数估计从强迫选择问卷与所提出的方法重现的潜在特征。
Multidimensional forced-choice formats can significantly reduce the impact of numerous response biases typically associated with rating scales. However, if scored with classical methodology, these questionnaires produce ipsative data, which lead to distorted scale relationships and make comparisons between individuals problematic. This research demonstrates how item response theory (IRT) modeling may be applied to overcome these problems. A multidimensional IRT model based on Thurstone's framework for comparative data is introduced, which is suitable for use with any forced-choice questionnaire composed of items fitting the dominance response model, with any number of measured traits, and any block sizes (i.e., pairs, triplets, quads, etc.). Thurstonian IRT models are normal ogive models with structured factor loadings, structured uniquenesses, and structured local dependencies. These models can be straightforwardly estimated using structural equation modeling (SEM) software Mplus. A number of simulation studies are performed to investigate how latent traits are recovered under various forced-choice designs and provide guidelines for optimal questionnaire design. An empirical application is given to illustrate how the model may be applied in practice. It is concluded that when the recommended design guidelines are met, scores estimated from forced-choice questionnaires with the proposed methodology reproduce the latent traits well.