Recovering Predictor–Criterion Relations Using Covariate-Informed Factor Score Estimates. Issue 6 (2nd November 2018)

• Record Type: Journal Article
• Authors: Curran, Patrick J.; Cole, Veronica T.; Bauer, Daniel J.; Rothenberg, W. Andrew; Hussong, Andrea M.
• Is Part Of: Structural equation modeling. Volume 25:Issue 6(2018)
• Page Start: 860
• Page End: 875
• Publication Date: 2018-11-02
• Subjects: Factor score estimation -- integrative data analysis -- item response theory -- moderated nonlinear factor analysis; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2018.1473773
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Although it is currently best practice to directly model latent factors whenever feasible, there remain many situations in which this approach is not tractable. Recent advances in covariate-informed factor score estimation can be used to provide manifest scores that are used in second-stage analysis, but these are currently understudied. Here we extend our prior work on factor score recovery to examine the use of factor score estimates as predictors both in the presence and absence of the same covariates that were used in score estimation. Results show that whereas the relation between the factor score estimates and the criterion are typically well recovered, substantial bias and increased variability is evident in the covariate effects themselves. Importantly, using covariate-informed factor score estimates substantially, and often wholly, mitigates these biases. We conclude with implications for future research and recommendations for the use of factor score estimates in practice.