An Updated Guideline for Assessing Discriminant Validity
回顾了区分效度的多种定义和评估技术，提出基于测量误差校正后相关性的广义定义，并通过蒙特卡洛模拟比较了不同技术，推荐了CI CFA (sys)等方法供应用研究者使用。
Discriminant validity was originally presented as a set of empirical criteria that can be assessed from multitrait-multimethod (MTMM) matrices. Because datasets used by applied researchers rarely lend themselves to MTMM analysis, the need to assess discriminant validity in empirical research has led to the introduction of numerous techniques, some of which have been introduced in an ad hoc manner and without rigorous methodological support. We review various definitions of and techniques for assessing discriminant validity and provide a generalized definition of discriminant validity based on the correlation between two measures after measurement error has been considered. We then review techniques that have been proposed for discriminant validity assessment, demonstrating some problems and equivalencies of these techniques that have gone unnoticed by prior research. After conducting Monte Carlo simulations that compare the techniques, we present techniques called CI CFA (sys) and [Formula: see text](sys) that applied researchers can use to assess discriminant validity.