# Factors Affecting Cybersecurity Awareness among University Students
## 4 Results
### Description of the Independent Variable (X) Used
#### 4.2.5 Multiple Tests  
Assumptions Test  
The residuals are assumed to be generally distributed in multivariate normality-multiple regression. However, no multiple regression presupposes that the independent variables are not substantially connected. The variance inflation factor (VIF) values are used to test this assumption [62]. \begin{table}
\begin{tabular}{l c c} \hline Kaiser–Meyer–Olkin Measure of Sampling Adequacy & 0.783 \\ \hline \multirow{3}{*}{Bartlett’s Test of Sphericity} & Approximate Chi–Square & 1795.927 \\ \cline{2-3}  & Df & 171 \\ \cline{2-3}  & Sig & 0.000 \\ \hline \end{tabular}
\end{table} 
Table 7: KMO and Bartlett’s Test. \begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & **Password Security** & **Browser Security** & **Social Media Activities** & **Level of Awareness** \\ \hline Password Security & 1 & & \\ Browser Security & 0.023 & 1 & \\ Social Media Activities & 0.298 & \(-\)0.074 & 1 & \\ Level of Awareness & 0.277 & 0.184 & 0.366 & 1 \\ \hline \end{tabular}
\end{table} 
Table 8: Correlation component matrix. \begin{table}
\begin{tabular}{c c} \hline
**Correlation Value (r)** & **Interpretation** \\ \hline
0 & No correlation \\ \textgreater{}0–0.25 & Low Correlation \\ \textgreater{}0.25–0.5 & Moderate Correlation \\ \textgreater{}0.5–0.75 & High Correlation \\ \textgreater{}0.75–0.99 & Very High Correlation \\
1 & Perfect Correlation \\ \hline \end{tabular}
\end{table} 
Table 9: Guidelines for providing an interpretation of correlation coefficients. Normality Test  
The normality assumption is related to the residual distributions. This is considered customarily distributed, and the regression line is fitted to the data so that the mean of the residuals is zero. The normality test [63] results using the normal p-plot with 450 respondents can be seen in Figure 2. The normal p-plot shows that all data points are spread around the line. This indicates that the data has met the assumption of normality. The results of the multicollinearity test using the VIF (variance inflation factor) value can be seen in Table 10.  
The VIF value in Table 10 shows that the VIF value of the three variables is less than 10 (VIF < 10), so it can be concluded that all the independent variables used in this study do not experience multicollinearity. Heteroskedasticity Test  
This study detects the occurrence of heteroskedasticity [64] by looking at the pattern of data points on the scatter-plot graph. Figure 3 shows that the observation points spread randomly and do not form a design or line. The plot also indicates whether the data distribution is around the zero point. This suggests that the regression model is free from heteroskedasticity problems, and the heteroskedasticity assumption has been fulfilled. Multiple Linear Regression Model and Coefficient of Determination (R2)  
All classical assumption tests have been fulfilled; the next step is to build multiple linear regression model equations. The regression model equation that is produced can be used to analyze the effect of password security, browser security, and social media activities on cybersecurity awareness. The regression coefficient values are shown in Table 11. \begin{table}
\begin{tabular}{l c} \hline \hline
**Variable** & **VIF Value** \\ \hline Password Security (X\({}_{1}\)) & 1.100 \\ Browser Security (X\({}_{2}\)) & 1.008 \\ Social Media Activities (\(\Upsilon\)) & 1.105 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Multicollinearity Test. Figure 2: Normality test. The multiple linear regression model formed based on the regression coefficients in Table 11 is as follows:  
\[\text{Cybersecurity Awareness}=4.301+(0.147)\text{ X}_{1}+(0.188)\text{ X}_{2}+(0.241)\text{ X}_{3}\]  
The regression coefficient value above can be explained such that if the level of student knowledge of password security increases by 1%, then the level of cybersecurity awareness will increase by 14.7%. Likewise, if the level of student knowledge of browser security rises by 1%, then cybersecurity awareness will increase by 18.8%.