Cutoff criteria for overall model fit indexes in generalized structured component analysis

By Gyeongcheol Cho, Heungsun Hwang, Marko Sarstedt, Christian M. Ringle. Journal of Marketing Analytics, 2020

319 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/975a3ed1988b2f66763293e02ad34ed1b880fffd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. S. Naz
2. António Abreu
3. Mário Nuno Mata
4. José Moleiro Martins
5. Shamim Akhtar
6. Shuja Iqbal

Citations in the corpus

• Linking Entrepreneurial Orientation with Innovation Performance in SMEs; the Role of Organizational Commitment and Transformational Leadership Using Smart PLS-SEM (2021, methodology): “According to Henseler, Hubona [101] and Cho, Hwang [104], the standardized root means square (SRMR) values should be lower than 0.”