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cA good fitting model has a Comparative Fit Index (CFI) approaching.96, a Root Mean Square Error of Approximation (RMSEA) approaching.06 and a Weighted Root Mean Square Residual (WRMR) of 1 or less [ 40]. dFull details regarding these analyses in terms of treating missing data, item deletion and cross-loading suggested by modification indices are available from the first author on request.
Although a model with a non significant chi-square estimate is generally considered a good fitting model, Hu and Bentler recommended combinational rules to evaluate model fit [ 39].
It is expected that a good fitting model generates standardized residuals (approximately) normally distributed.
Meanwhile, the refined model was confirmed as a good fitting model via analysis of variance (ANOVA).
The RFI value for our SEM is 0.966 which means that ours is a very good fitting model.
As "adequate precision value" was indicative of the signal-to-noise ratio index, a value greater than 4 indicated the proper prerequisites for a good fitting model.
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Hu and Bentler (1999) observed from different studies that good fitting models should have a value of.95 or greater for CFI, GFI, NFI, TLI or greater, the value of (RMSEA) equal to or less than.06.
However, the goal is not to find good fitting models for their own sake.
All logistic regression models achieved a rho-square between 0.20 and 0.40 suggesting they were very good (fitting) models for predicting job satisfaction.
In addition to a nonsignificant chi square (χ2), allowing a rejection of the null hypothesis (i.e. an unacceptable representation of the data), a good-fitting model was considered to have a comparative fit index (CFI) ≥0.95 and a root mean square approximation of variance (RMSEA) significantly smaller than 0.05 [61], [61].
For each model, global Wald chi-square values were calculated, a significant value of which indicates a good-fitting model.
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