## 4 Results and Discussion
### Principal Component Analysis (PCA)  
To differentiate between the correlated and uncorrelated linear combinations of the covariates and avoid multicollinearity, Principal Component Analysis (PCA) was chosen to analyze the significance of the self-reported perception and preference towards the particular questions in this study. PCA is usually used to verify that the linear combinations have maximum variance. At the same time, the results obtained from the PCA are generally discussed and presented in terms of factor scores and loadings. The factor scores or component scores are the transformed variable values correlated with a particular data point. Meanwhile, loadings indicate the weight by which every standardized original variable should be multiplied in order to obtain the component scores [17]. In this study, PCA was performed under the condition by which the Eigenvalue was more than 1. Several factors in an analysis of a particular variable can be usefully determined by Eigenvalue [18]. Other than that, reliability \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Risky Behavior**} & \multicolumn{2}{c}{**Risk Perception**} & \multicolumn{2}{c}{**Positive Affect**} \\ \cline{2-7}  & **Mean** & **S.D.** & **Mean** & **S.D.** & **Mean** & **S.D.** \\ \hline \multicolumn{7}{l}{Frequently changing lane to overtake the vehicle in front.} & 2.941 & 1.214 & 3.618 & 1.136 & 2.642 & 1.238 \\ \multicolumn{7}{l}{Speeding up and suddenly braking.} & 2.142 & 0.928 & 3.936 & 1.166 & 1.941 & 1.035 \\ \multicolumn{7}{l}{Exceeding speed limit even feeling unsafe.} & 2.074 & 1.131 & 3.873 & 1.241 & 2.069 & 1.134 \\ \multicolumn{7}{l}{Riding fast on the curve.} & 1.995 & 0.97 & 4.059 & 1.202 & 2.069 & 1.181 \\ \multicolumn{7}{l}{Continue riding although feeling sleep.} & 2.049 & 1.122 & 4.019 & 1.267 & 1.706 & 0.826 \\ \multicolumn{7}{l}{Taking alcohol before riding.} & 1.211 & 0.587 & 4.201 & 1.314 & 1.348 & 0.75 \\ \multicolumn{7}{l}{Run a red light.} & 2.020 & 1.096 & 3.897 & 1.253 & 1.921 & 1.024 \\ \multicolumn{7}{l}{Racing with other vehicles.} & 1.500 & 0.874 & 4.054 & 1.245 & 1.735 & 1.036 \\ \multicolumn{7}{l}{Riding during peak hour.} & 3.216 & 1.245 & 3.015 & 1.129 & 2.265 & 1.14 \\ \multicolumn{7}{l}{Fail to keep a proper distance with other vehicles.} & 2.206 & 1.016 & 3.603 & 1.151 & 2.01 & 0.915 \\ \multicolumn{7}{l}{Overtaking/turning without using signal lights.} & 1.887 & 0.968 & 3.775 & 1.207 & 1.814 & 0.965 \\ \multicolumn{7}{l}{Riding without wearing crash helmet.} & 1.608 & 0.933 & 4.123 & 1.236 & 1.73 & 1.046 \\ \multicolumn{7}{l}{Crossing a stop-junction without fully stopping.} & 2.191 & 1.109 & 3.632 & 1.156 & 2.138 & 1.128 \\ \multicolumn{7}{l}{Using mobile phone while riding.} & 1.549 & 0.872 & 4.088 & 1.233 & 1.721 & 1.029 \\ \multicolumn{7}{l}{Not switching on the headlights during daytime.} & 1.931 & 1.345 & 2.936 & 1.368 & 2.240 & 1.230 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean and standard deviation (S.D.) of each observed variable. \begin{table}
\begin{tabular}{c c} \hline \hline
**Latent Variables** & **Cronbach’s Alpha** \\ \hline \multicolumn{3}{l}{Risky Behaviors (\(\chi\))} & 0.833 \\ \multicolumn{3}{l}{Risk Perception (\(\gamma\))} & 0.945 \\ \multicolumn{3}{l}{Positive Affect (\(\chi\))} & 0.889 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Reliability test (Cronbach’s Alpha) of the self-reported variables. values higher than 0.7, 20% or higher variance explained, and rotation was implemented in the PCA to obtain factor loading, as these are also important conditions that need to be met in order to carry out PCA [19]. Factor analysis is a technique that is used to reduce a large number of variables to a smaller number of factors. This technique extracts the maximum common variance from all variables and assigns them a common score. As an index of all variables, this score can be used for further analysis. Additionally, items that have factor loadings lower than 0.5 were considered to be insignificant.