This section describes the statistical analysis of the quantitative data collected in the driving simulator investigations. The aim of the use of statistical tests is to determine whether or not there are statistically significant differences between two or more groups. The selection of correct statistical tests is determined by the data type and purpose of analysis (McCrum- Gardner, 2008). Prior to the selection of the statistical tests for a study, it is important to determine the scale of measurements of nominal, ordinal, and continuous data (McCrum- Gardner, 2008; Field, 2013). Table 3.1 provides an overview of the scale of measurements of the data collected in the driving simulator investigations.
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Table 3.1 Overview of the scale of measurements of the data in driving simulator
investigations
For continuous data, the first step is to assess whether or not the data is normally distributed by plotting a histogram of the data and looking at whether it forms a symmetrical bell shape or using normality tests such as the Kolmogorov-Smirnov and Shapiro-Wilks tests (Elliott and Woodward, 2007; McCrum-Gardner, 2008; Field, 2013). If the data is approximately
normally distributed, they could be analysed using parametric methods, otherwise non- parametric tests are used. After determining to use paramedic or non-parametric tests, the final decision to choose which test to use depends on the purpose of analysis. (McCrum- Gardner, 2008; Field, 2013)
For the data to be analysed with parametric tests, when assessing whether or not there are statistically significant differences between two independent groups, the independent sample t-test was considered suitable (McCrum-Gardner, 2008; Field, 2013). For example, in Section 5.3.3, several independent sample t-tests were carried out to compare the mean takeover times of older and younger drivers in a specific weather condition. In situations when two paired groups were to be compared, the paired sample t-test was considered suitable (McCrum- Gardner, 2008; Field, 2013). For instance, in Section 4.3.4, several paired sample t-tests were implemented to compare the older drivers’ mean reaction times when completely disengaged from driving as opposed to when monitoring driving. When comparing several groups from a mixed factorial between- and within-subjects experimental design (see Section 3.3.7), a mixed
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factorial analysis of variance (ANOVA) is recommended in literature (for example, McCrum- Gardner, 2008; Field, 2013) and has been regarded as a suitable method for data analysis by previous driving simulator studies that have adopted a mixed factorial experimental design (Gouy et al., 2014; Gold et al., 2015; Körber et al., 2016). For example, in Section 5.3.5, a mixed factorial ANOVA was used to examine the effects of weather, age and road type on drivers’ reaction time. In addition, in order to use the mixed factorial ANOVA, it is important that the data meets the assumption of sphericity, which refers to the condition that the
variance of the differences of all possible combinations of related groups are equal (Field, 2013). The assumption of sphericity was examined using Mauchly’s test of sphericity, and if the assumption was not met, a correction was implemented and reported in the results (Field, 2013). And the assumption of variance homogeneity was assessed using the Levene test (Field, 2013). In addition, when assessing the correlation between two variables with continuous data, Pearson’s correlation was used (McCrum-Gardner, 2008; Field, 2013); for example, in Section 4.3.10, it was used to examine the correlation between drivers’ reaction time and takeover time.
Non-parametric tests have less statistical power and are less flexible compared to parametric tests, they are used in the analysis of nominal and ordinal data (McCrum-Gardner, 2008). In terms of analysing nominal data, the Chi-square (X²) test of independence was conducted when the purpose of analysis is to assessing statistically significant relationship between two independent groups (McCrum-Gardner, 2008; Field, 2013); for example, in Section 4.3.3, the Chi-square (X²) test was used to assess if there was statistically significant difference in the numbers of collisions and critical encounters among older and younger drivers. When comparing two paired groups, McNemar test was used (McCrum-Gardner, 2008; Field, 2013); for instance, in Section 5.3.3, a McNemar test was conducted to examine if there was a significantly significant difference in drivers’ numbers of collisions and critical encounters recorded in clear weather and in rain. In regard to analysis the ordinal data, when comparing two independent groups, the Mann-Whitney U test was adopted (McCrum-Gardner, 2008; Field, 2013); for example, in Section 7.3.9, Mann-Whitney U tests were implemented in assessing whether or not there were statistically significant difference in attitudes towards the HMI designs between older and younger drivers. When comparing two paired groups, the Wilcoxon signed-rank test was used (McCrum-Gardner, 2008; Field, 2013); for instance, in Section 7.3.9, it was used to examine if the participants’ attitudes towards two HMI designs were significantly different. Finally, when comparing more than two paired groups, the Friedman test was used (McCrum-Gardner, 2008; Field, 2013); for example, in Section 7.3.9,
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it was used to check if participants’ attitudes towards the four HMI designs were statistically significant different. All the descriptive and inferential statistical analysis were carried out using IBM SPSS Statistics software.
The simulator collects data on the participants’ driving performance at a frequency of 20 sample per second (20 Hz). The data from the driving simulator was in binary form and converted into ASCII format. Then, values of all of the dependent variables were calculated following the definitions in Section 3.3.4.
3.4 Review of the Available Methods for Investigating Older Drivers’ Needs and