Assumption and Hypothesis Testing

As one can see from the frequency distribution described above, the most common reported road safety violations were swerving to avoid an oncoming vehicle, disregarding speed limits (points 3-5 in the 5-point scale), talking on the phone while driving, not wearing a seatbelt, and participation in unofficial races with other drivers. These are also the key driving behaviours identified through the preliminary literature review, so they become the key points of interest in the analysis in this section. The analysis of risky driving behaviours will be performed by focusing on these types of violations. In addition, the relationship between years of driving experience and risky behaviour is analysed. The testing of each of the research hypotheses is described below.

6.4.1. Driving Experience and Risky Driving

Hypothesis 1: Less experienced drivers get involved in risky driving more frequently

The first hypothesis tested in this study deals with the relationship between years of driving experience and various self-reported risky driving behaviours. The research compared the risky behaviour of drivers with more than seven years’ experience, with the behaviour of drivers with less than seven years’ experience. A statistically significant difference was found between the two groups, as shown in Table 6.2. The strength of correlation between risky behaviour and years of experience was only statistically significant only at the 0.1 level.

Table 6.2

ANOVA: years of driving experience and risky behaviours

df F Sig. USUALLY violate other general traffic rules 3.00 3.85 0.01 People stopped by the police for road safety violations are simply

unlucky because many people do that

3.00 3.28 0.02

Miss ‘yield' or 'stop' signs 3.00 3.79 0.01

Steer the wrong way into a skid 3.00 5.21 0.00

Do you know such road safety campaigns as Salamaty and Enough in the KSA? (if yes, please say a few words about them, what they are about, etc)

3.00 3.80 0.01

USUALLY jump orange and red lights 3.00 4.94 0.00

The ANOVA test revealed that in this study, years of driving experience had a strong impact on many driving behaviours – see Table 6.2. All driving behaviours exhibited results valid at the .01 significance level – a vital precondition for interpretation of statistical findings stemming from the non-normalcy and non-homogeneity of data distribution. Correlations of

years of experience with violations of general traffic rules, missing the ‘yield’ and ‘stop’ signs, and jumping orange and red lights were found to be statistically significant.

After identification of differences in survey responses among respondents, the researcher explored the cross-tabulation of means for these items and years of driving experience to find out which group takes more risks overall. Statistical findings of the cross-tabulation may be seen in Table 6.3.

Table 6.3

Cross Tabulation for Years of Driving Experience and Means for Survey Responses

Variable 0-7 years Over 7 years Total

USUALLY jump the orange and red lights 5.73 4.57 4.76

USUALLY violate other general traffic rules 5.52 4.67 4.81

People stopped by the police for road safety violations are simply unlucky because many people do that

5.08 4.68 4.74

Miss ‘yield’ or ‘stop’ signs 5.48 4.66 4.79

Steer the wrong way into a skid 6.16 4.84 5.04

As one can see, more experienced drivers reported much more frequent risk taking and violations. More experienced drivers missed more ‘yield’ and ‘stop signs’ (M=4.66, compared to 6.16 for less experienced drivers). They scored lower on all items that showed a statistically significant difference in the ANOVA test, reporting more frequently steering the wrong way into a skid (M=4.84 as compared to M=6.16 for less experienced drivers), and also reported violating general traffic rules more often (M=4.67) (Appendix B.9). Therefore, it is possible to conclude from this ANOVA test analysis that drivers with less than 7 years of driving experience exhibited very different attitudes to risks during driving, and reported significantly different driving behaviours, compared to drivers with more than seven years’ experience.

This data indicates that more experienced drivers are indeed more prone to taking risks on the roads, and are more likely to be involved in risky driving because of their over-reliance on their driving experience. Possible explanations for this have already been explored in prior research. Number of years of driving experience has been found by many researchers to produce a significant impact on risky driving. Knight, Iverson and Harris (2012) pointed out in their study that most young people are not risk-takers or sensation-seekers, and the only

typical risky behaviour among young drivers is that of speeding. Cavendish, Guppy and Hand (2012) also claimed that driver-specific attitudes and experiences play a role in predicting risk-taking, culpable near-misses, and accident experiences. Both Porter (2011) and Transport Committee (2007) experts have pointed out that young inexperienced drivers are involved in car crashes at a considerably higher rate than more experienced drivers; however, RTAs may happen because of drivers’ errors rather than risk-taking.

6.4.2. Speeding as a Complex Risky Driving Behaviour

Hypothesis 2: The acceptability of speeding as a normal behaviour on the roads leads to frequent speeding.

To test this hypothesis, non-parametric correlation analysis was used – Spearman’s rho. As the analysis of the data’s normalcy and homogeneity showed, the dataset used in this survey analysis is non-normally distributed and skewed. Hence, Spearman’s rho was applied as a non-parametric counterpart of Pearson’s correlation coefficient. This type of analysis is suitable for cases in which the relationships between variables are not linear; it is universal for any kind of monotonicity (Turner, 2014). To identify the details of speeding as one of the most common risky driving behaviours, the researcher selected the set of behaviours and attitudes from the survey specifically linked to speeding:

1. ‘speed limits are often set too low’

2. ‘become impatient with a slow driver in the outer lane and overtake on the inside’ 3. ‘usually exceed speed limits by more than 10 km/hour’

4. ‘get involved in unofficial races with drivers’ 5. ‘speeding is rarely a cause of road accidents’

This constellation of items was tested to see how strongly the presence of one of them affects the proneness of drivers to possess the other items in the list. Strength of correlation was considered statistically significant only at the significance level of .01. Findings of this correlation analysis may be seen in Table 6.3.

As shown by the correlation analysis presented above, those who consider that speed limits are often set too low are also highly prone to overtaking drivers whom they consider slow (correlation coefficient of 0.39, p<0.01) and they are also more likely to exceed speed limits by more than 10 km/hour (correlation coefficient of .61 and sig. value = .00). Becoming impatient with slow drivers was also very strongly correlated with getting involved in unofficial races (correlation coefficient of 0.26, p<0.01), while the latter behaviour was also

strongly correlated with believing that speeding was not a problem (correlation coefficient of 0.60, p<0.01).

Table 6.4

Correlation on Speeding Behaviours and Attitudes

Spearman’s rho 1 2 3 4 5

1. Speed limits are often set too low, so many people ignore them

.39** .61** .16* .04

2. Become impatient with a slow driver in the outer lane and overtake on the inside

.39** .22** .19*

3. USUALLY exceed speed limits by more than 10 km/hour

.26** .22**

4. Get involved in unofficial ‘races’ with other drivers

.60**

5. Speeding is rarely a cause of road accidents **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Hence, the correlation tests showed that most common risky driving behaviours included exceeding of speed limits by 10 km/hour, overtaking slow drivers, and involvement in unofficial races with other drivers, and revealed that a negligent attitude to speeding was a probable cause of RTAs. All these dimensions appeared to be strongly correlated with each other, showing that these aspects of speeding are present on Saudi roads. It is also vital to point out that speeding behaviours are strongly correlated with speeding attitudes, which provides an additional insight into the problem of speeding on Saudi roads. Drivers who speed most frequently also perceive speeding as normal and do not associate the incidence of RTAs with this behaviour. Hence, it is evident that speeding behaviours have speeding attitudes as their starting point, and attitudes should be changed first if changes in driving behaviours are to occur. This connection was earlier identified by Ross and Antonowicz (2004) who advocate the use of cognitive strategies in the treatment of bad drivers to change their attitudes, and later by Knipling and Bergoffen (2011) who point out the pervasive impact of the driver’s personalities and driving styles on driving behaviours. Therefore, issues such as attitudes and awareness should be targeted with a level of attention equivalent to that given to unsafe behaviors.

Since speeding behaviours and attitudes were found to be strongly correlated, it was further hypothesised that speeding as the most common and acceptable driving safety violation may also be related to other types of violations. In this section, the correlation between speeding and mobile phone use during driving is tested with the help of Spearman’s rho and non- parametric correlation analysis. The set of speeding indicators was taken from the testing of the previous hypothesis, while attitudes, awareness and behaviours in regard to mobile phone use were selected as follows:

1. ‘usually drive while talking on the cell phone or texting’

2. ‘Using a mobile phone is NOT a problem as drivers can drive safely when using it’ 3. ‘I am NOT aware of the risks associated with using a mobile phone when driving’ Findings from this correlation analysis are presented in Table 6.4. The strength of the correlation was considered statistically significant only at the .01 significance level.

All variables related to the use of mobile phones while driving were very strongly correlated with the speeding behaviours and attitudes. Those who indicated that they ‘usually drive while talking on a cell phone or texting’ were found to: consider speed limits to be too low, and believe it is reasonable to ignore them (correlation coefficient of 0.31, p<0.01), report getting irritated by slow drivers and overtaking them unsafely (correlation of 0.32, p<0.01), and to report frequently exceeding speed limits by more than 10 km/hour (correlation of 0.39, p<0.01). Those talking over the phone while driving were also prone to getting involved in unofficial races with drivers (correlation of 0.40, p<0.01). More significantly, talking over the phone while driving was strongly correlated with negative perceptions regarding road safety. Drivers who reported talking on the phone while driving also said they did not consider the use of a mobile phone while driving (correlation of 0.36, p<0.01) and speeding (correlation of 0.26, p<0.01) to be problems.

Table 6.5

Correlation of Speeding and Mobile Phone Use

1 2 3 4 5 6 7 8

1. Speed limits are often set too low. so many people ignore them

.39** .60** .31** .16* .19* .04 .11

2. Become impatient with a slow driver in the outer lane and overtake on the inside

.39** .32** .22** .22** .19* .21**

3. USUALLY exceed speed limits by more than 10 km/hour

.39** .26** .28** .22** .20*

4. USUALLY drive while talking on the cell phone or texting

.40** .36** .26** .29**

5. Get involved in unofficial ‘races’ with other drivers

.64** .60** .66**

6. Using a mobile phone is NOT a problem as drivers can drive safely when using it

.54** .66**

7. Speeding is rarely a cause of road accidents

.61**

8. I am NOT aware of the risks associated with using a mobile phone when driving

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Attitude towards using a mobile phone during driving was also strongly correlated with speeding behaviours (exceeding speed limits by more than 10 km/hour – correlation of 0.28, p<0.01; getting involved in unofficial races with other drivers – correlation of 0.64, p<0.01) and bad attitudes toward speeding (not considering speeding a cause of RTAs – correlation of 0.54, p<0.01), and bad attitudes to mobile phone use (absence of awareness regarding the risks of mobile phone use – correlation of 0.66, p<0.01). A similarly alarming tendency was found regarding a lack of awareness of the risks associated with mobile phone use: it was very strongly correlated with all speeding behaviours and attitudes except for considering speed limits too low.

Based on the correlation analysis presented above, one may infer that using a mobile phone while driving, and considering this normal and acceptable, and a lack of awareness of the risks associated with this behaviour are typical of drivers who exhibit frequent speeding

behaviours on the Saudi roads and who also regard speeding as normal. Use of mobile phones was found to be an unsafe driving behaviour by Dragutinovic and Twisk (2005), Bush III (2014), and National Safety Council (2012). Even the use of hand-free cell phones was regarded a risky behaviour because of the distraction that talking or texting causes. Dragutinovic and Twisk (2005) even found out that the use of mobile phones when driving causes slower reactions to traffic signals, slower breaking reactions, and increased risk-taking on the roads. Such evidence suggests that when attempting to bring about an improvement to the driving culture in the KSA, campaign designers should approach these two types of behaviour as a cluster, given the strength of the correlation between all speeding-related and mobile phone-related attitudes and behaviours.

6.4.5. Speeding and Seat Belt Use

Next, the variety of speeding behaviours and attitudes were correlated with failure to wear seat belts. One question from the survey was analysed: ‘I never wear a seatbelt’. Here, the strength of statistical significance was also measured only at the .01 level of significance. The findings of this analysis may be seen in Table 6.5.

Table 6.6

Correlation of Speeding and Non-Wearing of Seat Belt

1 2 3 4 5 6 7

1. Speed limits are often set too low, so many people ignore them

.39** .61** .32** .16* .04 .27**

2. Become impatient with a slow driver in the outer lane and overtake on the inside

.39** .18* .22** .19* .32**

3. USUALLY exceed speed limits by more than 10 km/hour

.50** .26** .22** .27**

4. NEVER wear a seatbelt .28** .22** .28**

5. Get involved in unofficial ‘races’ with other drivers

.60** .61**

6. Speeding is rarely a cause of road accidents .51**

7. Close following is NOT a big problem while driving

**. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

The findings presented in Table 6.5 suggest that failure to wear a seat belt was very strongly correlated with speeding behaviours and attitudes. Those who reported not using a seatbelt were also found to be significantly prone to regarding speed limits as too low (correlation of 0.32, p<0.01), not regarding speeding a cause of RTAs (correlation of 0.22, p<0.01), and not considering close following a high-risk behaviour (correlation of 0.27, p<0.01). Moreover, those who reported never using a seatbelt also claimed that they often exceeded speed limits by more than 10 km/hour (correlation of 0.50, p<0.01), and belonged to the cohort of drivers regularly involved in unofficial races with other drivers. Hence, as one can see, not wearing a seatbelt was found to be strongly correlated with all speeding-related behaviours and attitudes, showing that those who exceeded speed limits and disregarded speed limits were also prone to neglecting seat belt use regulations.

Seat belt use is a serious road safety problem in the KSA. As pointed out by Khan (2015), a significant number of motorists appeared ignorant of traffic rules, and reported a consistent failure to use seat belts. Moreover, failure to use of children’s and infants’ car seats and seatbelts is a catastrophe in the KSA which is associated with a disproportionately high death toll among Saudi children – they are either damaged by airbags or otherwise injured during RTAs (Blue Abaya, 2013). These findings should also be considered in the design of road safety campaigns, since understanding how attitudes and behaviours increase the incidence of other risky behaviours may increase insights into how risky driving behaviours generally form in their complexity.