## 3 Results and Discussion  
Data analysis was conducted using the above-described materials and methods. Before entering the 13 IVs (Table 1) in the model, [PERSON]'s tau-b correlation analysis was performed between all safety climate determinants, as presented in the correlation matrix (Table 2). As mentioned earlier, this was to guide when entering the IVs as predictors in the model in case of proxy variables and served to reveal any violations of the multicollinearity assumption. Despite the significant correlations, none demonstrated a high correlation coefficient (i.e., 0.80 or higher). Therefore, no signs of multicollinearity between the IVs were detected. Nevertheless, the highest correlation coefficient (0.449) was found between determinants D7 (coworker influence) and D8 (communication). This indicates that these two determinants should be treated cautiously since they might represent each other to some extent if entered in the model as simultaneous predictors. The first criterion for inclusion in the model was to select the predicator that demonstrated a higher significance in explaining the variations in DV. Second, if both were significant, the predictor with a higher partial contribution when included with other predictors in the model was chosen, resulting in higher pseudo \(R^{2}\) values of the overall model. To build the prediction model, all 13 IVs were included in the model as covariate predictors against the DV. As mentioned earlier, the process of entering IVs was conducted in a stepwise hierarchical fashion based on the structure presented in Table 1. Through fitting the ordinal logit model present in Equation (1), five significant safety climate determinants (D13, D12, D3, D4, and D7, according to Table 1) were revealed as the predictors (\(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\), and \(X_{5}\)) of the construction site's safety climate levels, as evaluated by personnel (Tables 3 and 4). To further check the multicollinearity assumption for the five predictors, the VIF for each predictor was calculated, as presented in Table 4. The VIF values ranged from 1.054 to 1.275 and were all relatively close to 1, indicating that the prediction model satisfies the multicollinearity assumption. The overall fit of the model was tested using the \(-2\)LL \(\chi^{2}\) statistic. Applying Equations (8) and (9), the difference between the baseline model and the new model including the five predictors was statistically significant: \(\chi^{2}(5)=537.804\,-\,475.708=62.096\); \(p<0.001\). Therefore, the model is considered a statistical fit. Moreover, the goodness-of-fit test indicated Pearson \(\chi^{2}(425)=460.452301\), \(p=0.114\) (\(>\)0.05), and deviance \(\chi^{2}(425)=402.745\), \(p=0.775\) (\(>\)0.05), indicating that the model is a good fit. Furthermore, the parallel lines test was performed using the \(-2\)LL \(\chi^{2}\) statistic to test the difference between the null hypothesis that the model is based on proportional odds and the alternative hypothesis of nonproportional odds. The results showed that \(\chi^{2}(5)=475.708\,-\,472.788\,=2.920\), \(p=0.712\) (\(>\)0.05), indicating that the location parameters of the model are the same across all response categories. Thus, the model satisfied the assumption of proportionality or parallelism of odds. Moreover, to measure the strength of association between the five IVs and the DV using Equations (10-12), the model demonstrated acceptable levels of pseudo \(R^{2}\)s: [PERSON] and [PERSON] = 0.143, [PERSON] = 0.178, and [PERSON] = 0.095, respectively. As presented in Table 4, all estimated regression model parameters, including the model thresholds (i.e., \(\theta_{1}\) and \(\theta_{2}\)) and the location predictor coefficients (i.e., \(\beta_{1}\)-\(\beta_{5}\)), were significant. This was measured using Wald \(\chi^{2}\) statistics, which were obtained using Equations (13) and (14). Moreover, estimated regression model parameters demonstrated reasonable SE and bias levels on both 95% Wald CIs and 95% bootstrapped CIs.