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 compl

SUBMITTER: Estabrook R 

PROVIDER: S-EPMC3773873 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature