Deletion statistic accuracy in confirmatory factor models

• Main Authors: Jennifer Koran, Fathima Jaffari
• Format: Article
• Language: English
• Published: SAGE Publishing 2020-05-01
• Series: Methodological Innovations
• Online Access: https://doi.org/10.1177/2059799120918349
• ISSN: 2059-7991

Social science researchers now routinely use confirmatory factor models in scale development and validation studies. Methodologists have known for some time that the results of fitting a confirmatory factor model can be unduly influenced by one or a few cases in the data. However, there has been little development and use of case diagnostics for identifying influential cases with confirmatory factor models. A few case deletion statistics have been proposed to identify influential cases in confirmatory factor models. However, these statistics have not been systematically evaluated or compared for their accuracy. This study evaluated the accuracy of three case deletion statistics found in the R package influence.SEM . The accuracy of the case deletion statistics was also compared to Mahalanobis distance, which is commonly used to screen for unusual cases in multivariate applications. A statistical simulation was used to compare the accuracy of the statistics in identifying target cases generated from a model in which variables were uncorrelated. The results showed that Likelihood distance and generalized Cook’s distance detected the target cases more effectively than the Chi-square difference statistic. The accuracy of the Likelihood distance and generalized Cook’s distance statistics was unaffected by model misspecification. The results of this study suggest that Likelihood distance and generalized Cook’s distance are more accurate under more varied conditions in identifying target cases in confirmatory factor models.