2020 · Journal of Marketing Analytics · 8, 189–202
Journal DOI: 10.1057/s41270-020-00089-1 Open access
Abstract
Generalized structured component analysis (GSCA) is a technically well-established approach to component-based structural equation modeling that allows for specifying and examining the relationships between observed variables and components thereof. GSCA provides overall fit indexes for model evaluation, including the goodness-of-fit index (GFI) and the standardized root mean square residual (SRMR). While these indexes have a solid standing in factor-based structural equation modeling, nothing is known about their performance in GSCA. Addressing this limitation, we present a simulation study’s results, which confirm that both GFI and SRMR indexes distinguish effectively between correct and misspecified models. Based on our findings, we propose rules-of-thumb cutoff criteria for each index in different sample sizes, which researchers could use to assess model fit in practice.
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Models. a Correct model. b Misspecified model. Note: The figure shows two basic design component models considered in the simulation study. All the weights and residuals are omitted
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Figure 2

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Average error rates of GFI and SRMR cutoff values in different sample sizes. Note: The figure shows the averages of Types I and II error rates for different GFI and SRMR cutoff values aggregated over three levels of component correlation in each sample size. Plus: 100, asterisk: 200, open circle 500, open square 1000, and open diamond 2000
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Peer-reviewed journal article
Journal of Marketing Analytics · 2020
DOI: 10.1057/s41270-020-00089-1Cite this work
Cho, G., Hwang, H., Sarstedt, M., Ringle, C. M. (2020). Cutoff criteria for overall model fit indexes in generalized structured component analysis. Journal of Marketing Analytics, 8, 189–202. https://doi.org/10.1057/s41270-020-00089-1