Consistent Factor Score Regression: A Better Alternative for Uncorrected Factor Score Regression?

Abstract

Researchers in the behavioral, educational, and social sciences often aim to analyze relationships among latent variables. Structural equation modeling (SEM) is widely regarded as the gold standard for this purpose. A straightforward alternative for estimating the structural model parameters is uncorrected factor score regression (UFSR), where factor scores are first computed and then employed in regression or path analysis. Unfortunately, the most commonly used factor scores (i.e., Regression and Bartlett factor scores) may yield biased estimates and invalid inferences when using this approach. In recent years, factor score regression (FSR) has enjoyed several methodological advancements to address this inconsistency. Despite these advancements, the use of FSR with correlation-preserving factor scores, here termed consistent factor score regression (cFSR), has received limited attention. In this paper, we revisit cFSR and compare its advantages and disadvantages relative to other recent FSR and SEM methods. We conducted an extensive simulation study comparing cFSR with other estimation approaches, assessing their performance in terms of convergence rate, bias, efficiency, and type I error rate. The findings indicate that cFSR outperforms UFSR while maintaining the conceptual simplicity of UFSR. We encourage behavioral, educational, and social science researchers to avoid UFSR and adopt cFSR as an alternative to SEM.