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 (Department of International Business and Technology Studies, Texas A&M International University, Laredo, TX, USA)
Copyright: © 2017 |Pages: 9
DOI: 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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Illustrative Model And Data

Our discussion is based on the illustrative model depicted in Figure 1, which builds on actual empirical studies in the field of e-collaboration (Kock, 2005; 2008; Kock & Lynn, 2012). This illustrative model addresses the organizational effect of the use of an internal e-collaboration management tool with social networking capabilities (EM) on job performance (JP), an effect that is mediated by intermediate effects on job satisfaction (JS) and job innovativeness (JI).

Figure 1.

Illustrative model used

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