NNFINon-Normed Fit Index (statistics)
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The CFI, NFI, and NNFI all indicated an acceptable fit of the model to the data.
Other model fit measures such as GFI, AGFI, CFI, NNFI, IFI were employed to assess the "goodness of fit" of the measurement model (Byrne 2001).
The following goodness-of-fit indices were used during the MGCFA analysis: Chi-squared, RMSEA, CFI and NNFI (Tucker Lewis index or Non-Normed fit index).
An analysis of a more stringent standard presented by Hu and Bentler (1999) indicated that the NNFI and CFI should exceed a .
97 para NFI, NNFI, CFI, indicativos todos de un buen ajuste (Schermelleh-Engel, Moosbrugger y Muller, 2003).
The NNFI compares the hypothesized model to a null model in which covariances among observed variables are assumed to be 0.
Invariance testing involves examining differences in fit between the constrained and unconstrained models in terms of the chi-square index, the CFI, and the NNFI (Cheung & Rensvold, 2002; Little, 1997).
The fit indices such as GFI, AGFI, NNFI and CFI were all more than 0.