## 4 Results
### Ordinal Regression  
Finally, the relationship between an outcome variable and the explanatory variables was modeled. The outcome variable was the last ordinal item in section D of the questionnaire, i.e., different levels of respondents' satisfaction with overall performance of choice A and choice B. The explanatory variables included demographic features (section A), the items in section C of the questionnaire, and the factor scores computed in Section 3.2. Since the outcome variable was an ordinal variable, it does not make sense to assume normality and equality of variances. Hence, ordinal regression was used to model the relationship between the ordinal outcome variable and the explanatory variables. Logit function was used as the link function. _SWVL (Choice A)_  
Before going into the model details, it is necessary to check if the model is capable of providing suitable predictions. The significant chi-square statistic in Table 5 shows that the model is a significant improvement over the intercept-only model. The goodness-of-fit can be determined using the Pearson statistic and Deviance statistic. The non-significant test results for Pearson statistic (\(\chi^{2}\) = 629.324, df = 711, \(p\) = 0.987) and Deviance statistic (\(\chi^{2}\) = 356.739, df = 711, \(p\) = 1.000) indicate that the model is a good fit to the data and the model predictions are similar [45]. [PERSON] and [PERSON], Nagelkerke, and McFadden pseudo R square measures indicate how well the model explains the variations in the data and were 0.415, 0.481, and 0.270, respectively. A pseudo R square value of 0.27 for McFadden showed an excellent fit [46]. The non-significant chi-square statistic (\(\chi^{2}\) = 18.057, df= 18, \(p\) = 0.452) indicated that the test of parallel lines was satisfied. Parameter estimates for ordinal regression are shown in Table 6. The sign of the coefficients for covariates indicates direct or inverse relationships between continuous predictors and the ordinal outcome variable. The relative values of the coefficients for \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \(-\)**2 Log Likelihood** & **Chi-Square** & **Df** & **Sig.** \\ \hline Intercept Only & 488.726 & & & \\ Final & 356.739 & 131.988 & 9 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SWVL—Model Fitting Information. factor levels can help explain the effects of the categorical predictors in the model. The sign of a coefficient for a factor level indicates the effect of a particular-factor level relative to the reference category. For a one unit increase in satisfaction with service-attributes (factor 1), ordered log odds of being in a higher level of overall satisfaction are expected to increase by 1.714 such that all other variables in the model are kept constant. Similarly, one unit increase in satisfaction with ambience (factor 2) is expected to increase the ordered log odds of being in a higher level of overall satisfaction by 0.871. The regression coefficients for income show that passengers in the lower income groups are expected to be more satisfied with overall service of choice A. Passengers in the younger age groups and those traveling more often are likely to be less satisfied with overall service of choice A. _Airlift (Choice B)_  
The significant chi-square statistic in Table 7 shows that the model is a significant improvement over the intercept-only model. The pseudo R square values for Cox and Snell, Nagelkerke, and McFadden were 0.078, 0.092, and 0.042, respectively, indicating that the model does not do a good job at explaining the variations in the data. Nevertheless, the model is significant and does explain the variance in the data to some extent. The non-significant test results for Pearson statistic (\(\chi^{2}\) = 291.328, df = 294, \(p\) = 0.533) and Deviance statistic (\(\chi^{2}\) = 270.682, df = 294, \(p\) = 0.832) indicate that the model predictions are similar.