Please use this identifier to cite or link to this item:
http://dx.doi.org/10.25673/81384
Title: | Cutoff Criteria for Overall Model Fit Indexes in Generalized Structured Component Analysis |
Author(s): | Cho, Gyeongcheol Hwang, Heungsun Sarstedt, Marko Ringle, Christian M. |
Issue Date: | 2020 |
Type: | Article |
Language: | English |
URN: | urn:nbn:de:gbv:830-882.0106459 urn:nbn:de:gbv:ma9:1-1981185920-833398 |
Subjects: | Component-based structural equation modeling Generalized structured component analysis Model fit GFI SRMR |
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. |
URI: | https://opendata.uni-halle.de//handle/1981185920/83339 http://dx.doi.org/10.25673/81384 |
Open Access: | Open access publication |
License: | (CC BY 4.0) Creative Commons Attribution 4.0 |
Sponsor/Funder: | Projekt DEAL 2020 |
Journal Title: | Journal of marketing analytics |
Publisher: | Palgrave Macmillan |
Publisher Place: | Houndmills |
Volume: | 8 |
Issue: | 3 |
Original Publication: | 10.15480/882.2916 |
Page Start: | 189 |
Page End: | 202 |
Appears in Collections: | Fakultät für Wirtschaftswissenschaft (OA) |
Files in This Item:
File | Description | Size | Format | |
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Cho et al._Cutoff criteria_2020.pdf | Zweitveröffentlichung | 708.59 kB | Adobe PDF | View/Open |