## 3 Methodology
### Design, Variables, and Statistical Analysis  
The analysis investigated how both subjective and physiological measures reflect navigator stress and workloads during simulated ship encounters. The dependent variables included the perceived stress (from the result of the PSS), workload (from the weighted scale of the NASA-TLX), and physiological responses (HRV metrics, including the time-domain MRR, SDNN, and RMSSD and frequency-domain LF/HF ratio). These were modeled against independent variables such as age, experience, the encounter segment, and the navigational context. Table 4 presents an overview of the hypothesized variable relationships and the initial modeling framework. It is important to note that while the PSS and NASA-TLX were administered once per participant at the global level (before and after the simulation), the HRV indicators were collected separately for each segment. Consequently, each segment was included as an independent variable to capture physiological differences across encounter contexts and their contribution to global stress outcomes. This modeling choice acknowledges that while subjective reports are global and not tied to individual segments, localized physiological changes may still relate to overall stress. This assumption is well aligned with the human factors literature, which links localized physiological arousal with global perceptions of cognitive and emotional demand in simulation-based settings. To explore the dynamics between subjective and physiological responses, the analysis was structured into two exploratory phases:  
1. Step 1--Subjective Stress Analysis: The first part assessed how demographic and encounter-related variables, specifically age and experience, influenced the subjective stress levels measured using the PSS. Since the PSS scores did not meet the normality assumption (as assessed via the Shapiro-Wilk test and residual plots), non-parametric statistical methods were used. These included:  
1. Kruskal-Wallis H test for non-parametric comparisons across experience groups. 2. Mann-Whitney U test for pairwise comparisons (e.g., young vs. old participants). 3. NASA-TLX scores were collected but not used in the primary statistical tests in this step. 2. Step 2--Physiological Response Analysis (HRV): \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Dependent Variable** & **Type** & **Independent Variable** \\ \hline
1. & PSS & Subjective (self-report) & Age, Experience \\
2. & NASA-TLX & & PSS, Age, Experience \\
3. & HRV-SDN & Physiological & PSS, Age, Experience \\
4. & HRV-RMSSD & Physiological & PSS, Asea-TLX, Segment \\
5. & HRV-LF/HF Ratio & & PSS, Segment \\
6. & HRV—MRR & & PSS, Segment \\ \hline \end{tabular}
\end{table} 
Table 4: Overview of hypothesized relationships between dependent and independent variables used in initial model construction prior to selection. The final model specifications were refined based on statistical significance and fit indices. This step consisted of two complementary analyses:  
- Step 2a--Subjective Stress/Workload \(\rightarrow\) Physiological Responses:  
- The first part of the analysis examined how subjective stress and an individual's workload relate to physiological responses, measured through HRV features:  
* Time-domain features: SDNN, RMSSD;
* Frequency-domain feature: LF/HF ratio;
* Mean RR interval. - Where appropriate, a one-way ANOVA was used to test for HRV differences across PSS categories. [PERSON]'s correlation was also used to explore monotonic relationships between subjective, workload, and physiological indicators. - Step 2b--Physiological Responses \(\rightarrow\) Subjective Workload (NASA-TLX):  
To further explore the predictive relationship, multiple linear regression models were constructed using NASA-TLX subscales (e.g., frustration, effort, mental demand) as dependent variables. The predictors included HRV-based physiological responses (e.g., RMSSD, MRR, LF/HF ratio) derived from key encounter segments.