Regression Analysis with Latent Variables by Partial Least Squares and Four Other Composite Scores: Consistency, Bias and Correction

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 bi...

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Bibliographic Details
Published in:Structural equation modeling 2020-05, Vol.27 (3), p.333-350
Main Authors: Yuan, Ke-Hai, Wen, Yong, Tang, Jiashan
Format: Article
Language:English
Subjects:
Bias
bias-correction
factor scores
partial least squares
Regression analysis
unweighted composite scores
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Summary: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.
ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2019.1647107