## 3 Results and Discussion
The positive values of the location predictor coefficients (i.e., \(\beta_{1}\)-\(\beta_{5}\)), along with the fact that their associated \(\exp(\beta)\) values were greater than 1, indicates higher odds of moving to a higher ordered category in the DV for a higher value in any of the IVs (i.e., \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\), or \(X_{5}\)) by one unit. The OR value of \(\exp(\beta)\) associated with each predictor indicates the magnitude (i.e., how many times) of the increase. Thus, in Tables 3 and 4, the predictors are ordered on that basis. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & \multicolumn{2}{c}{**Predictor**} \\ \hline Safety support & \(X_{1}\) & Supervision, guidance, and inspection \\  & \(X_{2}\) & Social security and health insurance \\ \hline Safety commitment & \(X_{3}\) & Management’s commitment to safety \\  & \(X_{4}\) & Management’s safety justice \\ \hline Safety interaction & \(X_{5}\) & Coworker influences \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Construction site safety climate predictors retained in the prediction model. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{\(\theta,\beta\)} & \multirow{2}{*}{**SE**} & \multicolumn{2}{c}{**95\% Wald CI**} & \multicolumn{2}{c}{**Eval (\(\alpha,\beta\))**} & \multicolumn{2}{c}{**95\% Wald CI** for} & \multicolumn{2}{c}{**Bootstrap \({}^{\text{b}}\)**} & \multicolumn{2}{c}{**15**} \\  & & & \multicolumn{2}{c}{**Lower**} & \multicolumn{2}{c}{**Upper**} & \multicolumn{2}{c}{**Lower**} & \multicolumn{2}{c}{**Upper**} & \multicolumn{2}{c}{**Bias**} & \multicolumn{2}{c}{**SE**} & \multicolumn{2}{c}{**Upper**} \\ \cline{3-14}  & \(\theta_{1}\) & 3.699 & 0.839 & 2.015 & 5.304 & 19.027 \({}^{**}\) **  
To test the model's validity, sensitivity analysis and scenario applications were performed. As presented in Table 5, five extreme scenarios were applied using the prediction model. In the first scenario, all predictor values were set to 1, representing a scenario in which the model user rated the importance of all five safety climate predictors to be extremely unimportant as a safety climate determinant. The remaining four scenarios followed the same pattern for the values 2, 3, 4, and 5, respectively; in each scenario, all predictors were set to the same value, with the final scenario representing a model user who rated all predictors as extremely important. This procedure tested whether the model could assign higher probability percentages to higher scores on the DV. Accordingly, using Equations (17)-(24), and as presented in Table 5, the model successfully predicted the scores of the construction site's safety climate evaluations based on the importance ratings of safety climate determinants. This can be observed from the higher estimated score probability percentages shown in bold font (Table 5). The model also demonstrated the odds ratios' proportionality, having an equal OR value of 0.132, as shown in Table 5. Similarly, any combination of values ranging from 1 to 5 on the importance scale described above can be substituted in the five model predictors (i.e., \(X_{1}\), \(X_{2}\), \(X_{3}\), \(X_{4}\), or \(X_{5}\)) to estimate the safety climate evaluations. The developed prediction model correctly predicted 67% of the 401 safety climate evaluations of the construction site based on which it was modeled.