Journal
STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL
Volume 27, Issue 3, Pages 333-350Publisher
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/10705511.2019.1647107
Keywords
partial least squares; bias-correction; unweighted composite scores; factor scores
Funding
- Natural Science Foundatoin of China [31971029]
- Institute for Scholarship in the Liberal Arts, College of Arts and Letters, University of Notre Dame
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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.
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