## 3 Research Methodology
### Data Analysis Techniques  
Normality tests are sensitive to the sample size. The Shapiro-Wilk and Kolmogorov-Smirnov tests are the most well-known normality tests [66]. The Shapiro-Wilk test is a commonly used approach for determining data normality in samples size of fewer than 50 participants [67]. This test has become a famous normality check test because of its good power properties [68]. It determines the deviation from normality due to either skewness or kurtosis, or both [69]. It leads us to good results even with a small sample size. The Kolmogorov-Smirnov test also checks the normality of data, which is more general but less powerful than the first one [68]. In this test, the distribution of the statistic is independent of the cumulative distribution function being tested, and the test is precise. In this research, both tests were performed as the sample size was 32. The null hypothesis for both tests is that data are normally distributed, and the alternative hypothesis is that data are not normally distributed. The significance value (\(p\)) was taken (0.05) for the sample size to test the normality. If the \(p\)-value comes out greater than 0.05, we must fail to reject the null hypothesis that data are normally distributed. If it is less than 0.05, we must reject the null hypothesis that data are not normally distributed. We performed the parametric or non-parametric test based on these test results. A non-parametric Kruskal-Wallis H test determines statistically significant differences between three or more independently sampled groups [70]. This test has four assumptions: (i) the dependent variable is measured at the ordinal level, (ii) the independent variable should have two or more categories, (iii) there is no relationship between the observations in each group or among the group themselves, and (iv) the determination shape of each distribution is necessary for the interpretation of results. The null hypothesis Kruskal-Wallis H test is that there is a significant difference in sample distribution, and the alternative hypothesis is that there is no significant difference in sample distribution. The significant \begin{table}
\begin{tabular}{c c c} \hline \hline
**Domain** & **Elements** & **Question** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Attentional \\ Demand (D) \\ \end{tabular} } & Instability of situation & How much was the situation in the surroundings changing during the experimental session? \\  & Complexity of situation & How complex was the surrounding situation? \\  & Variability of situation & Were several different factors in the surrounding environment \\  & & changing? \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Attentional \\ Supply (S) \\ \end{tabular} } & Arousal & How alert were you to observe the surrounding situation? \\  & Concentration & How much did you concentrate on the surrounding? \\  & Division of attention & What proportion of attention was devoted to surroundings instead of \\  & & the design review task? \\  & Spare Mental Capacity & How much mental capacity must be spared for the surroundings? \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Understanding \\ (U) \\ \end{tabular} } & Information Quantity & How much information about the surrounding did you take in? \\  & Information Quality & How well did you understand/comprehend the information about \\  & & the surroundings that you took in? \\  & Familiarity & How familiar were you with the surroundings during the task? \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Situational awareness rating technique questions. \(p\)-value was taken (0.05) to check the significant difference. If the \(p\)-value comes out less than 0.05, we must fail to reject the null hypothesis that there is a statistically significant difference in the sample distribution. If it is greater than 0.05, we must reject the null hypothesis that there is no statistically significant difference in the sample distribution.