ØParsimony-Adjusted Measures
The Parsimony-Adjusted Measures table can be found under: View → Text Output → Model fit → Parsimony-Adjusted Measures
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Parsimony-Adjusted Measures |
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Model |
PRATIO |
PNFI |
PCFI |
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Default model |
2,398 |
0,695 |
0,696 |
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Saturated model |
0 |
0 |
0 |
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Independence model |
1 |
0 |
0 |
v PRATIO
PRATIO is the ratio of how many tracks/paths you fell to how many you could have fallen (all of them).
PRATIO = parsimony ratio that calculates the number of constraints in the model and is used to calculate the PNFI and PCFI indices.
PRATIO, or the ratio of the chi-square values of the target model to the null model, is a goodness-of-fit index commonly used in structural equation modeling. It measures the improvement in model fit when adding a path or a set of paths to the null model. The reference values for PRATIO depend on the degrees of freedom of the target model, the number of parameters estimated, and the level of significance.
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Interpretation:
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PRATIO value greater than 1 suggests that the target model fits the data better than the null model, while a PRATIO value close to 1 indicates poor model fit |
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Suggest that a PRATIO value of 2 or greater indicates good model fit |
Bollen, 1989; Hu & Bentler, 1999 |
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www.StatistischeBeratung.de |
There is no universally accepted cutoff value for PRATIO, as it depends on the specific model and sample size.
PRATIO = 2,398 indicates good model fit.
v PNFI
PNFI The Parsimion Normalized fit Index is a product of NFI and PRATIO.
PNFI = Normalized fixed parsimony index, which expresses the result of parsimony adjustment (James, Mulaik & Brett, 1982) according to the normalized fixed index (NFI).
PNFI (Parsimony Normed fit Index) and PCFI are fit indices in SEM that take into account the complexity of the model.
There is no universally accepted standard or reference value for PNFI and PCFI.
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Interpretation:
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PNFI value of 0.5 or higher indicate acceptable model fit |
Hu & Bentler, 1999 |
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www.StatistischeBeratung.de |
PNFI = 0,695 indicates good model fit.
v PCFI
PCFI = Comparative Fixed Parsimony Index which expresses the result of the parsimony adjustment applied to the Comparative fit Index (CFI). PCFI is a product of CFI and PRATIO
PNFI and PCFI (Parsimony Comparative fit Index) are fit indices in SEM that take into account the complexity of the model.
There is no universally accepted standard or reference value for PNFI and PCFI.
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Interpretation:
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PCFI value of 0.6 or higher indicate acceptable model fit |
Hu & Bentler, 1999 |
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www.StatistischeBeratung.de |
PCFI = 0,696 indicates good model fit.
| 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 > |
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