Individual Differences in Psychological Stress Associated with Data Breach Experiences
Bibliographic record
Abstract
Data breach incidents are now a regular occurrence, with millions of people affected worldwide. Few studies have examined the psychological aspects of data breach experiences, however, or the individual differences that influence how people react to these events. In this study, we examined the psychological stress associated with a personal experience with a data breach and several individual differences hypothesized to modulate such stress (age, gender, digital security awareness and expertise, trait anxiety, negative emotionality, and propensity to worry). A student sample (N = 166) and a community sample (N = 359) completed an online survey that asked participants to describe their most serious data breach and then complete the Impact of Events Scale—Revised (IES-R) to answer specific questions about the nature of the stress they experienced after the breach. Standard measures of trait anxiety, negative emotionality, and propensity to worry were also completed. A Data Breach Severity Index (DBSI) was created to quantify the invasiveness and consequences of each participant’s data breach. Hierarchical multiple regression analyses were used to identify demographic variables and psychological characteristics predictive of IES-R scores while controlling for DBSI scores. As expected, more invasive and consequential data breaches were associated with higher IES-R scores (greater data-breach-induced stress). Women had higher IES-R scores than men, and this difference persisted after controlling for gender differences in anxiety, negative emotionality, and propensity to worry. Greater daily social media use was associated with higher IES-R scores, whereas higher digital security expertise was associated with lower IES-R scores. The results illuminate several relationships between demographic and psychological characteristics and data-breach-induced stress that should be investigated further.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".