Consequently, we used a multi-group SEM to assess the invariance of the model (Figure 1 ) across gender and age group (respondents aged 18-44 and aged 45 or above). We tried to test the model by stratifying the sample into four subgroups (female aged 18-44, female aged 45 or above, male aged 18-44 and male aged 45 or above). However, the sample size for males aged 18-44 was relative small (Table S2) . Moreover, all the model variables were treated as categorical variables and we used the WLSMV method to estimate the model. This method requires that each subsample covers all the categories of each variable. In the case of one category, younger males, not all variable values were present. To meet the assumptions for analysis we would need to recode all variables, intrinsically altering the model. In order to avoid this, we relinquished a combined four-group comparison and instead compared the model across gender and the two age groups separately. To perform multigroup comparison we first ran a model with all parameters unconstrained. We then identified factor loadings that were not significantly different (p$0.05) and set these as equal, while loadings that were significantly different were allowed to vary, and finally paths that did not differ significantly were constrained to be equal while those that differed significantly were allowed to vary and estimated separately by groups. The ''DIFFTEST'' option in MPlus 6.0 was used to obtain a correct chi-square difference test for the WLSMV estimators and was used to estimate the differences between the least constrained model (with all the paths freely estimated) and the most constrained model (with all the paths constrained to be equal) as well as the partially constrained model (with some of the paths freely estimated and others constrained to be equal) [43] . A p-value.0.05 for the ''DIFFTEST'' indicate a nonsignificant difference between the models.