Structural Equation Modeling with Factors and Composites: A Comparison of Four Methods

• Published in: International journal of e-collaboration Vol. 13; no. 1; pp. 1 - 9
• Main Author: Kock, Ned
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2017
• Subjects: Algorithms; Analysis; Buildings; Collaboration; Least squares method; Mathematical analysis; Mathematical models; Methods; Modelling; Monte Carlo simulation; Multivariate statistical analysis; Regression; Software utilities; Statistical analysis; Structural equation modeling; Variables
• ISSN: 1548-3673; 1548-3681
• DOI: 10.4018/IJeC.2017010101

Recent methodological developments building on partial least squares (PLS) techniques and related ideas have significantly contributed to bridging the gap between factor-based and composite-based structural equation modeling (SEM) methods. PLS-SEM is extensively used in the field of e-collaboration, as well as in many other fields where multivariate statistical analyses are employed. The author compares results obtained with four methods: covariance-based SEM with full information maximum likelihood (FIML), factor-based SEM with common factor model assumptions (FSEM1), factor-based SEM building on the PLS Regression algorithm (FSEM2), and PLS-SEM employing the Mode A algorithm (PLSA). The comparison suggests that FSEM1 yields path coefficients and loadings that are very similar to FIML's; and that FSEM2 yields path coefficients that are very similar to FIML's and loadings that are very similar to PLSA's.