How to interpret SEM model fit results in AMOS
There are several fit indices used in SEM, and the criteria for satisfactory fit can vary depending on the specific model being tested, the sample size, and the complexity of the model. However, there are some commonly used fit indices that are recommended to be considered when assessing model fit in SEM. These include:
* Parameters/Values of particular interest
It's important to note that these fit indices are not independent of each other and should be considered together to assess model fit. Additionally, the choice of fit indices can vary depending on the research question and the specific model being tested.
v Where to find and how to interpret all SEM model fit parameters in AMOS.
How to present in research papers:
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Acronym |
Explication |
Accepted fit |
Resulting fit |
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CMIN/DF |
Chi-square divided by degrees of freedom |
≤ 3 it indicates an acceptable fit ≤ 5 it indicates a reasonable fit |
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RMSEA |
Root mean square error of approximation |
≤ 0,05 are considered excellent ≤ 0,08 are considered acceptable |
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GFI |
Goodness of fit Index
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≥ 0,90 indicates a reasonable fit ≥ 0,95 is considered an excellent fit |
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CFI |
Comparative fit index |
≥ 0,90 indicates an acceptable fit ≥ 0,95 is considered an excellent fit |
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TLI |
Tucker-Lewis index |
≥ 0,9 indicates a reasonable fit ≥ 0,95 is considered an excellent fit |
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| Cite this article in your research paper: |
| APA Statistische Beratung Leonardo Miljko (datum) How to interpret SEM model fit results in AMOS. Retrieved from https://www.StatistischeDatenAnalyse.de/images/services/How_to_interpret_SEM_model_fit_results_in_AMOS.pdf . |
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Harvard
Statistische Beratung Leonardo Miljko January 10, 2020 How to interpret SEM model fit results in AMOS. viewed datum < https://www.StatistischeDatenAnalyse.de/images/services/How_to_interpret_SEM_model_fit_results_in_AMOS.pdf > |
Wichtiger Hinweis: Der Originalinhalt ist auf Kroatisch. Die Übersetzung ins Deutsche und Englische erfolgte über einen Web-Übersetzer. Wir entschuldigen uns für die Fehler.
