A Cautionary Note on Modeling Multitrait-Multirater Data Arising From Ill-Structured Measurement Designs
研究发现，在分析多特质多评价者数据时，仅因选择不同评价者就会导致协方差矩阵、模型收敛性、拟合度等结果显著不同，提醒研究者注意此类问题。
The authors illustrate a problem with confirmatory factor analysis (CFA)-based strategies to model disaggregated multitrait—multirater (MTMR) data—the potential to find markedly different results with the same sample of ratees simply as a result of how one selects and identifies raters within the data set one has gathered for analysis. Using performance ratings gathered as part of a large criterion-related validation study, the authors show how such differences manifest themselves in several ways including variation in (a) covariance matrices that serve as input for the modeling effort, (b) model convergence, (c) admissibility of solutions, (d) overall model fit, (e) model parameter estimates, and (f) model selection. Implications of this study for past research and recommendations for future CFA-based MTMR modeling efforts are discussed.