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