## 2 Materials and Methods
If the result is not significant (i.e., \(p>0.05\)), this test indicates that the model with the selected predictors is a good fit. Furthermore, to measure the strength of association between the selected set of IVs and the DV, the pseudo \(R^{2}\) measures [PERSON] and [PERSON] \(R^{2}\), [PERSON]'s \(R^{2}\), and [PERSON]'s \(R^{2}\) were employed following Equations (10)-(12), respectively:  
Figure 1:3  
Figure 1:4  
Figure 1:5  
where L(\(\hat{B}\)) is the log-likelihood function for the model with the estimated parameters, L\(\left(\hat{B}^{(0)}\right)\) is the log-likelihood with just the thresholds, and \(n\) is the number of cases (sum of all weights). Moreover, to assess the significance level of each estimated regression model parameter, including thresholds (i.e., \(\theta\)s) and predictor coefficients (i.e., \(\beta\)s) and their associated standard errors (SEs) for each IV (i.e., location), the Wald \(\chi^{2}\) statistic based on the \(z\)-statistic was constructed following Equations (13) and (14), respectively. The significance of each estimated parameter was measured on a 95% Wald confidence interval (CI). The same test was performed for the parameters' associated OR (i.e., \(\exp(\theta,\beta)\) or \(e^{\theta}\) and \(e^{\theta}\)). Figure 1:6  
Figure 1:7  
Furthermore, to ensure the validity and accuracy of the results, the assumptions of the multiple ordinal logistic regression were checked and satisfied [47; 48; 49; 50; 51; 52]. These assumptions include that the DV is measured on an ordinal level, which is the case in this study, given the DV measurement scale described above. Another assumption is that IVs are continuous, ordinal, and/or categorical variables, which is the case for all IVs using this scale. Moreover, the independence-of-observations assumption is satisfied in this study, as each data point comes from a different respondent. Furthermore, the assumption of proportionality or parallelism of odds for the final model was checked by conducting the test of parallel lines using the -2 LL \(\chi^{2}\) statistic. The result was insignificant (i.e., \(p>0.05\)), indicating that the location parameters of the regression model coefficients were the same across all the DV categories (in this case, DV scores). Another assumption is to ensure that there are no to low levels of multicollinearity between IVs. This was checked by correlation analysis between all IVs using [PERSON]'s tau-b test statistics. This test reveals any signs of multicollinearity and guides the process of entering the IVs as predictors in the model in case there are proxy variables. Furthermore, the variance inflation factor (VIF) was calculated for each of the predictors retained in the final model; values close to 1 indicate low multicollinearity, while values close to 10 indicate that multicollinearity is a problem for the model [49]. Furthermore, linearity between IVs and the logit of DV was checked for the final model. An insignificant goodness-of-fit test \(-2\)LL \(\chi^{2}\) statistic and an overall highly significant fit of the model based on the \(-2\)LL \(\chi^{2}\) statistic comparing the intercept-only model and the final model was found. Additionally, each predictor's interactions in the final model and their log transformations were calculated and force-entered into the model, finding them to be insignificant. This is an indication satisfying the linearity assumption, according to [PERSON] [49]. Finally, sample size sufficiency was calculated using the rule-of-thumb formula in Equation (15), which is based on the concept of event per variable (EPV) [54]. Figure 1:8  
where \(N_{EPV}\) is the estimated required sample size based on the EPV and \(i\) is the number of IVs in the final model. Sample size adequacy in this study was based on the number of retained predictors in the final model, \(k=5\) and \(N_{EPV}=100+50(5)=350\). Thus, the sample size used in this study, \(N=401\) (\(>\)350), is sufficient, yielding a medium-to-large effect size [54].