ABSTRACT

To ensure meaningful comparison of test scores across groups or time, measurement invariance (i.e., invariance of the general factor structure and the values of the measurement parameters) across groups or time must be examined. However, many empirical examinations of measurement invariance of psychological/educational questionnaires need to address two issues: Using the appropriate model for ordinal variables (e.g., Likert scale items), and handling missing data. In two Monte Carlo simulations, this study examined the performance of one full-information-maximum-likelihood-based method and five multiple-imputation-based methods to obtain tests of measurement invariance across groups for ordinal variables that have missing data. Our results indicate that the full-information-maximum-likelihood-based method and one of the multiple-imputation-based methods generally have better performance than the other examined methods, though they also have their own limitations.

摘要

为了确保跨组或跨时间的测试分数有意义的比较，必须检查跨组或跨时间的测量不变性（即，一般因素结构和测量参数值的不变性）。然而，许多关于心理/教育问卷测量不变性的实证检验需要解决两个问题：对有序变量（例如，李克特量表项目）使用适当的模型，以及处理缺失数据。在两次蒙特卡罗模拟中，本研究检查了一种基于全信息最大似然的方法和五种基于多重插补的方法的性能，以获得具有缺失数据的序数变量的跨组测量不变性检验。