# Individual Differences in Psychological Stress Associated with Data Breach Experiences
## 3 Results
### Hierarchical Multiple Regression Predicting IES-R Scores  
A hierarchical multiple regression analysis was used to identify demographic characteristics and psychological measures predictive of IES-R scores. Digital security practices and digital threat awareness were not included in the analysis because they were not significantly correlated with the IES-R scores (Table 7). All variance inflation factors (VIFs) were below 2.8, which indicated that there was no problematic multicollinearity between the predictors (a VIF greater than 4.0 is generally considered to reflect a multicollinearity issue). A summary of the regression analysis is shown in Table 8, and the full results are shown in Table 9. The DBSI scores were entered first (Model 1) to control for the severity of the data breach in subsequent steps of the analysis, thereby ensuring that the analysis identified demographic and psychological variables that accounted for variance in the IESR-R scores not attributable to the severity of the data breach incident. As expected, the DBSI scores accounted for a significant percentage of the variance in the IES-R scores, \(\Delta R^{2}\) = 5.7%, \(F\)(1, 521) = 31.78, \(p\) < 0.001, as more severe data breaches were associated with higher IES-R scores, \(\beta\) = 0.240, \(t\)(521) = 5.64, \(p\) < 0.001. Demographic variables were added in Model 2, which included gender, sample (student or community), age, and years of education. Together, these variables accounted for a significant percentage of the variance in the IES-R scores above and beyond that accounted for by the DBSI scores, \(\Delta R^{2}\) = 2.4%, \(F\)(4, 517) = 3.31, \(p\) = 0.011. As can be seen in Table 9, however, gender was the only demographic variable that was statistically significant, \(\beta\) = 0.135, \(t\)(517) = 3.04, \(p\) = 0.002, \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **1.** & **2.** & **3.** & **4.** & **5.** & **6.** & **7.** \\ \hline
1. IES-R total score & — & & & & & & \\
2. IES-R intrusion & 0.92 ** & — & & & & & \\
3. IES-R avoidance & 0.82 ** & 0.56 ** & — & & & & \\
4. IES-R hyperarousal & 0.89 ** & 0.86 ** & 0.54 ** & — & & & \\
5. Negative emotionality & 0.12 ** & 0.08 & 0.11 & 0.11 & — & & \\
6. PSWQ & 0.28 ** & 0.29 ** & 0.15 ** & 0.31 ** & 0.15 ** & — & \\
7. GAD-7 & 0.32 ** & 0.32 ** & 0.21 ** & 0.33 ** & 0.11 ** & 0.77 ** & — \\ \hline \end{tabular}  
* IES-R = Impact of Events Scale—Revised. PSWQ = Penn State Worry Questionnaire; GAD-7 = Generalized Anxiety Disorder-7.
* \(p\) < 0.01. *
* \(p\) < 0.001.  
\end{table} 
Table 6: Correlations among psychological measures and IES-R scores. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **1.** & **2.** & **3.** & **4.** & **5.** & **6.** & **7.** & **8.** \\ \hline
1. IES-R total score & — & & & & & & & \\
2. IES-R intrusion & 0.92 ** & — & & & & & & \\
3. IES-R avoidance & 0.82 ** & 0.56 ** & — & & & & & \\
4. IES-R hyperarousal & 0.89 ** & 0.86 ** & 0.54 ** & — & & & \\
5. Digital security practices & \(-\)0.06 & \(-\)0.06 & \(-\)0.04 & \(-\)0.07 & — & & & \\
6. Digital security expertise & \(-\)0.13 ** & \(-\)0.11 & \(-\)0.11 * & \(-\)0.11 ** & 0.38 ** & — & \\
7. Digital threat awareness & \(-\)0.09 & \(-\)0.08 & \(-\)0.09 & \(-\)0.04 & 0.29 ** & 0.50 ** & — & \\
8. Post-data breach security & 0.18 ** & 0.18 ** & 0.12 ** & 0.18 ** & 0.27 ** & 0.22 ** & 0.14 ** & — \\ \hline \end{tabular}  
* IES-R = Impact of Events Scale—Revised.
* \(p\) < 0.01. *
* \(p\) < 0.001.  
\end{table} 
Table 7: Correlations among digital security measures and IES-R scores. with women having higher IES-R scores than men after controlling for DBSI scores and the other demographic variables. For Model 3, negative emotionality, PSWQ, and GAD-7 scores were added to the analysis, and together these accounted for a significant percentage of variance, \(\Delta R^{2}=9.3\%\), \(F(3,\,514)=19.26\), \(p<0.001\).