Table 3. Indices of model fit for healthy controls (n=141) and schizophrenia patients (n=119).

Model	χ2 (df)	p-value	χ2/df	CFI	SRMR	RMSEA	AIC	NFI
1. One-factor
Schizophrenia cases	42.86 (20)	< 0.002	2.14	0.92	0.06	0.09	90.86	0.87
Healthy controls	53.71 (20)	< 0.001	2.68	0.87	0.08	0.12	101.7	0.82
2. Two-factor models
a) Updating = shifting
Schizophrenia cases	36.19 (19)	< 0.01	1.91	0.94	0.05	0.08	70.19	0.89
Healthy controls	43.85 (19)	< 0.001	2.31	0.91	0.07	0.10	77.85	0.85
b) Updating = inhibition
Schizophrenia cases	34.80 (19)	< 0.02	1.83	0.95	0.06	0.08	84.80	0.89
Healthy controls	51.30 (19)	< 0.001	2.70	0.88	0.08	0.12	101.3	0.83
c) Inhibition = shifting
Schizophrenia cases	32.16 (19)	< 0.03	1.69	0.96	0.05	0.07	82.16	0.90
Healthy controls	43.35 (19)	< 0.001	2.28	0.91	0.07	0.10	93.36	0.85
3. Full three-factor model
Schizophrenia cases	25.78 (17)	0.08	1.51	0.97	0.04	0.05	79.78	0.95
Healthy controls	35.71 (17)	0.01	2.10	0.94	0.06	0.07	89.71	0.90
Multiple group CFA
All factor loadings free to vary between groups (unconstrained)	61.48 (34)	0.003	1.81	0.95	0.04	0.05	169.49	0.90
Only one factor loading constrained to be equal between groups	398.23 (53)	< 0.001	7.51	0.39	0.05	0.15	468.24	0.36
All estimated factor loadings, as well as factor variances, constrained equal to be between groups	498.34 (61)	< 0.001	8.17	0.23	0.08	0.16	552.11	0.20
All estimated factor loadings, as well as factor variances and covariances, constrained to be equal across groups	305.61 (48)	< 0.001	6.36	0.53	0.08	0.14	385.61	0.51

AIC = Akaike’s information criteria; CFA = confirmatory factor analysis; CFI = comparative fit index; df = degrees of freedom; NFI = normed fit index; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.

The CFA and structural equation models were examined using different index fits. The chi-square statistic provides a direct test of differences between the predicted and observed variances and covariances. The probability value associated with χ² represents the likelihood of obtaining an χ² that exceeds the χ² value when H₀ is true (Byrne34). χ²/df values less than 2.0 indicate a good model fit (Kline35). The SRMR is the square root of the averaged squared residuals (i.e., differences between the observed and predicted covariances). Values bellow 0.05 indicate a good fit and values less than 0.08 indicate a relatively good fit to the data (Hu & Bentler36). CFI and the Bentler and Bonnet NFI (Bentler & Bonett37) were also used. These include a penalty function for more complex models. CFI and NFI values vary between 0 and 1. A cutoff value close to 0.95 indicates that the model fits the data in that it adequately describes the sample data (Byrne34). The AIC addresses the issue of parsimony in the assessment of model fit (Akaike38). Lower AIC values indicate a good fit.