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

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

Ned Kock
Copyright: © 2017 |Volume: 13 |Issue: 1 |Pages: 9
ISSN: 1548-3673|EISSN: 1548-3681|EISBN13: 9781522511496|DOI: 10.4018/IJeC.2017010101
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MLA

Kock, Ned. "Structural Equation Modeling with Factors and Composites: A Comparison of Four Methods." IJEC vol.13, no.1 2017: pp.1-9. http://doi.org/10.4018/IJeC.2017010101

APA

Kock, N. (2017). Structural Equation Modeling with Factors and Composites: A Comparison of Four Methods. International Journal of e-Collaboration (IJeC), 13(1), 1-9. http://doi.org/10.4018/IJeC.2017010101

Chicago

Kock, Ned. "Structural Equation Modeling with Factors and Composites: A Comparison of Four Methods," International Journal of e-Collaboration (IJeC) 13, no.1: 1-9. http://doi.org/10.4018/IJeC.2017010101

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Abstract

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.

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