Regression Analysis with Latent Variables by Partial Least Squares and Four Other Composite Scores: Consistency, Bias and Correction. Issue 3 (3rd May 2020)

• Record Type: Journal Article
• Authors: Yuan, Ke-Hai; Wen, Yong; Tang, Jiashan
• Is Part Of: Structural equation modeling. Volume 27:Issue 3(2020)
• Page Start: 333
• Page End: 350
• Publication Date: 2020-05-03
• Subjects: partial least squares -- bias-correction -- unweighted composite scores -- factor scores; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2019.1647107
• Languages: English
• ISSNs: 1070-5511

Abstract

Abstract : Compared to the conventional covariance-based SEM (CB-SEM), partial-least-squares SEM (PLS-SEM) has an advantage in computation, which obtains parameter estimates by repeated least squares regression with a single dependent variable each time. Such an advantage becomes increasingly important with big data. However, the estimates of regression coefficients by PLS-SEM are biased in general. This article analytically compares the size of the bias in the regression coefficient estimators of the following methods: PLS-SEM; regression analysis using the Bartlett-factor-scores; regression analysis using the separate and joint regression-factor-scores, respectively; and regression analysis using the unweighted composite scores. A correction to parameter estimates following mode A of PLS-SEM is also proposed. Monte Carlo results indicate that regression analysis using other composite scores can be as good as PLS-SEM with respect to bias and efficiency/accuracy. Results also indicate that corrected estimates following PLS-SEM can be as good as the normal-distribution-based maximum likelihood estimates under CB-SEM.