Table 1.

Model fit statistics of the 12 EF latent variable models

Model	χ2	df	CFI	RMSEA	SRMR	AIC	BIC
Correlated-factors models
1. I+U+S	41.17	24	0.99	0.02	0.02	48,991	49,161
2. S/I+U	339.83	26	0.73	0.08	0.06	49,286	49,444
3. U/I+S	74.41	26	0.96	0.03	0.02	49,021	49,179
4. S/U+I	402.08	26	0.68	0.08	0.06	49,349	49,507
5. G	431.91	27	0.65	0.08	0.06	49,376	49,529
Bifactor models
6. C+I+U+S	28.45	18	0.99	0.02	0.02	48,991	49,195
7. C+I+S	43.83	21	0.98	0.02	0.02	49,000	49,187
8. C+I+U	219.96	21	0.83	0.07	0.06	49,176	49,363
9. C+U+S	30.01	21	0.99	0.01	0.02	48,987	49,173
10. C+I	401.81	24	0.67	0.09	0.06	49,352	49,522
11. C+S	73.29	24	0.96	0.03	0.02	49,024	49,193
12. C+U	331.92	24	0.73	0.08	0.06	49,282	49,452

χ² chi-squared statistics, df degrees of freedom, CFI comparative fit index, RMSEA the root-mean-square error of approximation, SRMR standardized root-mean square residual, AIC Akaike information criterion, BIC Bayesian information criterion.

CFI > 0.95 is commonly used as an indication of the adequate fit. Lower values of SRMR and RMSEA indicate better fit, with  < 0.05 indicating a good fit. Lower values of AIC and BIC indicate better fit. The good-fit models are indicated in bold, of which the C+U+S model showed the best overall fit.