# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 2 Materials and Methods
### Data Analysis  
To quantitatively assess the stability and deviation of key performance metrics in XR + eye-tracking safety training, statistical analyses were performed using SPSS version 25.0 (SPSS for Mac, Chicago, IL, USA) and MATLAB R2024b. Data processing focused on five core performance indicators: reaction time, operational accuracy, attention focus, error count, and corrective response time, evaluated across different experimental conditions. Independent-samples \(t\)-tests were used to compare these metrics between training groups,and Cohen's d was calculated to determine the effect size for each comparison. One-way ANOVA was conducted to assess differences between experienced and inexperienced participants, with partial eta squared (\(\eta^{2}\)) reported as the effect size. Bonferroni correction was applied to control for Type I error in multiple comparisons. All tests were two-tailed, with an alpha level set at 0.05. Confidence intervals were computed at the 95% level to ensure statistical precision and reliability. To determine the stability of gaze fixation, response accuracy, and reaction time, a matched samples \(t\)-test was applied to compare three repeated measurements within each experimental group. This statistical approach ensured that performance variations observed over multiple trials were significant rather than occurring due to random fluctuations. By analyzing the consistency of responses across sessions, the test provided insights into whether real-time eye-tracking feedback contributed to sustained performance improvements. A priori power analysis (\(\alpha\) = 0.05, power = 0.80, medium effect size f = 0.25) indicated a minimum of 52 participants. With 59 participants, our achieved power was 0.88.  
For intra-group comparisons, a one-way analysis of variance (ANOVA) was used to examine deviation trends in eye-tracking fixation points and response accuracy across the different training conditions. This method allowed for the evaluation of whether participants in the VR + eye-tracking group exhibited lower deviation in visual attention distribution and greater response precision compared to those in the VR-only training group. By identifying statistical differences between groups, this analysis provided empirical evidence on how gaze-based feedback enhances training efficiency.  
Additionally, error count data were analyzed using repeated measures ANOVA to track the reduction in procedural mistakes over time. This method ensured that the observed performance improvements were attributable to training effects rather than individual variability. Linear regression modeling was also employed to explore potential correlations between attention fixation stability and operational accuracy, offering further insights into the cognitive mechanisms underlying XR-based safety training.  
By integrating these statistical techniques, this study establishes a robust quantitative foundation for assessing the effectiveness of real-time gaze-tracking feedback in enhancing safety training outcomes. The combination of matched samples \(t\)-tests, ANOVA, and regression modeling ensures that the findings are statistically valid, providing evidence-based insights into the potential of XR + eye-tracking technology in industrial safety training applications. Anonymized data are publicly available at [[https://osf.io/e3](https://osf.io/e3) btx/?view_only=69e4278 af69f4f98877f2 cf1d43510a7]([https://osf.io/e3](https://osf.io/e3) btx/?view_only=69e4278 af69f4f98877f2 cf1d43510a7) (accessed on 26 April 2024).