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http://dx.doi.org/10.25673/81384
Titel: | Cutoff Criteria for Overall Model Fit Indexes in Generalized Structured Component Analysis |
Autor(en): | Cho, Gyeongcheol Hwang, Heungsun Sarstedt, Marko Ringle, Christian M. |
Erscheinungsdatum: | 2020 |
Art: | Artikel |
Sprache: | Englisch |
URN: | urn:nbn:de:gbv:830-882.0106459 urn:nbn:de:gbv:ma9:1-1981185920-833398 |
Schlagwörter: | Component-based structural equation modeling Generalized structured component analysis Model fit GFI SRMR |
Zusammenfassung: | 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. |
URI: | https://opendata.uni-halle.de//handle/1981185920/83339 http://dx.doi.org/10.25673/81384 |
Open-Access: | Open-Access-Publikation |
Nutzungslizenz: | (CC BY 4.0) Creative Commons Namensnennung 4.0 International |
Sponsor/Geldgeber: | Projekt DEAL 2020 |
Journal Titel: | Journal of marketing analytics |
Verlag: | Palgrave Macmillan |
Verlagsort: | Houndmills |
Band: | 8 |
Heft: | 3 |
Originalveröffentlichung: | 10.15480/882.2916 |
Seitenanfang: | 189 |
Seitenende: | 202 |
Enthalten in den Sammlungen: | Fakultät für Wirtschaftswissenschaft (OA) |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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Cho et al._Cutoff criteria_2020.pdf | Zweitveröffentlichung | 708.59 kB | Adobe PDF | Öffnen/Anzeigen |