迫选式人格测验的传统计分与IRT计分模型

迫选测验的传统计分方式会产生自模式数据, 不能进行传统的信效度检验、因素分析和方差分析等。近年来研究者提出了一些基于项目反应理论的计分模型, 如瑟斯顿IRT模型和MUPP模型等, 它们可以规避自模式数据的弊端。瑟斯顿IRT模型方便进行参数估计, 模型定义灵活; 而MUPP模型的拓展性较差, 参数估计的方法有待提高。另一方面, 已有研究者基于MUPP模型开发了一些抗作假的迫选测验, 而瑟斯顿IRT模型距离这种应用还比较远。此外, 两个模型的适用性和有效性都有待更多的实证研究来检验。

The Conventional and the IRT-based Scoring Methods of Forced-Choice Personality Tests

The conventional scoring method of forced-choice personality tests produces the ipsative data to which reliability and validity analysis, factor analysis, and analysis of variance (ANOVA) can not apply. In order to overcome these problems, some researchers have come up with item response theory-based (IRT-based) models for the scoring and analysis of forced-choice tests. These models, such as Thurstonian IRT model and Multi-Unidimensional Pairwise-Preference Model (MUPP), can avoid the disadvantages of ipsative data. On one hand, Thurstonian IRT model can be easily used for parameter estimation and it is flexible in model specification, while MUPP has poor expansibility and still needs improvement in parameter estimation method. On the other hand, MUPP has been used to develop forced-choice tests against faking but Thurstonian IRT model is still far from this kind of application. More empirical studies are needed to test the applicability and effectiveness of both models.