Table 3.
Summary table of goodness-of-fit indices.
| Category | Indicator | Acceptance level | Purpose |
|---|---|---|---|
| CMIN (Chi-square, χ2) | The smaller, the better | Chi-square is used to measure the difference between the hypothetical model and the actual data | |
| P | 0.05 ≤ p ≤ 1.00 | ||
| CMIN/DF (χ2/df) | ＜2-5 | ||
| Absolute fit indices | Root mean square error of approximation (RMSEA) | 0.05 < RMSEA ≤0.08 (good fit), 0.08 < RMSEA ≤1 (marginal fit), SRMR≤0.10 |
Estimates how well the model fits |
| Standardized Root Mean square Residua (SRMR) | |||
| Goodness-of-fit index (GFI) | ≥0.90 GFI/AGFI ≥0.90 (good fit), 0.80 ≤ GFI/AGFI ≤0.90 (marginal fit) |
GFI indicates the proportion of variance in the sample variance matrix | |
| Adjusted goodness-of-fit index (AGFI) | The AGFI can be used to compensate for the GFI index, where the value of the index is adjusted according to the number of parameters | ||
| Incremental fit indices | Incremental-Fit Index (IFI) | ≥0.90 IFI/NFI/CFI/TLI ≥0.90 (good fit), 0.80 ≤ IFI/NFI/CFI/TLI ≤0.90 (marginal fit) |
The IFI points out the problems of parsimony and sample size |
| Normed Fit Index (NFI) | NFI compares the overall fit of the researchers' model with the improvement of a model | ||
| Comparative Fix Index (CFI) | CFI is a modified version of NFI that takes into account the sample size | ||
| Tucker-Lewis Index (TLI) | TLI indicates the correlation of model complexity |