A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence

Abstract

Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the existence of missing data have generally been ignored in this debate. This article presents a simulation study that compares the reliability of sum scores, regression-based and expected posterior methods for factor score estimation for confirmatory factor models in the presence of missing data. Although all methods perform reasonably well with complete data, expected posterior-weighted (full) maximum likelihood methods are significantly more reliable than sum scores and regression estimators in the presence of missing data. Factor score reliability for complete data can be predicted by Guttman's 1955 formula for factor communality. Furthermore, factor score reliability for incomplete data can be reasonably approximated by communality raised to the power. An empirical demonstration shows that the full maximum likelihood method best preserves the relationship between nicotine dependence and a genetic predictor under missing data. Implications and recommendations for applied research are discussed.