# Individual Differences in Psychological Stress Associated with Data Breach Experiences
## 3 Results
### Hierarchical Multiple Regression Predicting IES-R Scores
However, of these three measures, only the GAD-7 scores were a significant predictor, \(\beta=0.250\), \(t(514)=3.91\), \(p<0.001\), with higher trait anxiety associated with higher IES-R scores. Notably, gender remained a significant predictor in Model 3, \(\beta=0.088\), \(t(514)=2.05\), \(p=0.041\), which indicated that women had higher IES-R scores than men even after accounting for their higher GAD-7 scores (as shown in Table 4). For Model 4, the addition of smartphone use, social media use, and browser use did not increase the percentage of variance explained, \(\Delta R^{2}=0.8\%\), \(F(3,\,511)=17.5\), \(p=0.157\). Finally, for Model 5, which added digital security expertise and post-data breach security, the percentage of variance explained increased significantly, \(\Delta R^{2}=1.9\%\), \(F(2,\,509)=6.01\), \(p=0.003\), with post-data breach security being a significant predictor, \(\beta=0.145\), \(t(509)=3.44\), \(p<0.001\). Higher scores on the post-data breach security measure were associated with higher IES-R scores after controlling for the other predictors. The total percentage of variance accounted for by Model 5 was \(20.1\%\) (adjusted \(R^{2}=18.1\%\)), \(F(13,\,509)=9.86\), \(p<0.001\), and the significant predictors were DBSI scores, GAD-7 scores, and post-data breach security. Higher scores on each of these measures were uniquely associated with higher IES-R scores. The same predictors were statistically significant when using robust standard errors to correct for possible heteroscedasticity. Excluding the DBSI score predictor from the regression analysis reduced the total \(R^{2}\) to \(16.7\%\), \(F(12,\,510)=8.50\), \(p<0.001\). GAD-7 scores and scores on post-data breach security remained statistically significant predictors (\(p<0.001\)). In addition, digital security expertise was significant, \(\beta=-0.10\), \(t(510)=2.06\), \(p=0.040\). An additional exploratory regression analysis was carried out for the participants with high IES-R scores (>24, according to the IES-R criteria). This analysis identified predictors of IES-R scores among the participants who had experienced higher levels of data-breach-induced stress (\(N=112\)). The predictors were entered in the same order as they were in the analysis of the full sample. A summary of this analysis is shown in Table 10, and the full results are shown in Table 11. As expected, DBSI scores accounted for a significant percentage of the variance in IES-R scores, \(\Delta R^{2}=14.1\%\), \(F(1,\,109)=17.85\), \(p<0.001\), with more severe data breaches associated with higher IES-R scores, \(\beta=0.375\), \(t(109)=4.23\), \(p<0.001\). 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 IES-R scores above and beyond that accounted for DBSI scores, \(\Delta R^{2}=8.6\%\), \(F(4,\,105)=2.92\), \(p=0.025\), although none of the individual demographic predictors were statistically significant. For Model 3, negative emotionality, PSWQ, and GAD-7 scores were added, which did not increase \(R^{2}\) significantly, \(\Delta R^{2}=4.0\%\),