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Using this approach, we find some discrepancies between an assortment of model fit statistics.
The model fit statistics indicate a significant model fit for all models.
We compared model fit statistics to a null model as a baseline estimation method.
The model fit statistics illustrated that the two-factor paradigm appropriately fit the data from the second undergraduate group.
The model fit statistics indicate that the orthogonal design fits the data better than the corresponding efficient design and data.
Table 2 displays the model fit statistics from the test of measurement invariance between the EALS and the EALS-SF.
Significance of model fit statistics was determined by Chi-square analysis.
b: Overall model fit statistics.
Model fit statistics were used to explore these relationships.
Detailed model fit statistics are presented in Table 3.
Model Fit Statistics (AIC) for multivariate was 369.08 (p < 0.001).
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