Data were analyzed using IBM SPSS-22 and Amos-18. The mean and standard deviation for each study measure and correlation coefficients between variables are shown in
Table 1. Results showed that all of the correlation coefficients were significantly positive. The proposed model adopting dissociative experiences as a predictive variable, IC as a mediating variable, and OCS and schizotypal personality traits as dependent variables was examined. According to assumptions underlying SEM, all questionnaire scores were checked for missing data, outliers, and normality before performing the analysis. The results of the SEM investigation indicated that the proposed model had goodness of fit, and all Goodness-of-Fit indexes were good: χ
2/df = 1.85 (< 3) and RMSEA = 0.05 [CI (90%) = 0.03 - 0.06]. GFI, AGFI, and CFI were 0.95, 0.92, and 0.97, respectively, and all of them were above 0.9. The final model and standard path coefficients are displayed in
Figure 1. All the coefficients were statistically significant (P < 0.001). Therefore, it was argued that dissociative experiences may have affected the OCS and schizotypal personality traits directly and indirectly (through IC).
Next, multi-group analysis was performed to investigate the moderating role of negative affect. To this end, the sample was divided into three groups with high, moderate, and low negative affect based on the standard deviation of this variable. Then, the fitness of the proposed model was examined in each one of the three groups. As shown in
Table 2, the proposed model had Goodness-of-Fit in all three groups. The difference between the chi-square value in constrained and unconstrained model was then investigate. Adopting this method facilitated comparing the degree of the proposed model’s equality in the three groups. When the factor loadings, variances, and covariances in the model were equal in different groups, the model was assumed to be constrained. To create a constrained model in AMOS software, an equal parameter was specified for groups with a label. Thus, any unlabeled parameter was freely estimated. As a result, unlabeled parameters were freely estimated and may have had different values in groups (
39). The results showed that the improvement of chi-square from the original unconstrained model (200.57) to the constrained model (255.17) was significant (54.60 > 48.28) due to the chi-square distribution table (χ
2 = 48.28, df = 28, P = 0.01). Therefore, the moderating role of negative affect was confirmed. Then, further investigation was performed in order to determine the different path coefficients in the three groups. According to Byrne (
39,
40), the coefficients important for the researcher were assumed to be equal in all three groups first by forming different constrained models, and then the chi-square index in each one of these constrained models was compared with the unconstrained model. The results of the comparisons are shown in
Table 3. As shown, the chi-square difference between model A and the original unconstrained model was statistically significant (∆χ
2 = 21.22, ∆df = 12, P = 0.04). These findings suggested that at least one of the paths in the model was not equal in different groups. As indicated in
Table 3, findings revealed that only the chi-square difference between constrained model E and the unconstrained model (model 1) was statistically significant (∆χ
2 = 7.82, P = 0.02) among all models. This finding implied that only the IC path on OCS, among the paths in the model, was moderate by NA. As shown in
Table 4, the standard regression coefficient of this path was reduced by increasing the NA, but this path was still significant in all three groups.