Risky Behaviours Among University Students

This study examines the risk-taking behaviours among different ages and gender in Australia and their correlation with car crashes among university students. It also explores other factors such as depression and living arrangements. Findings suggest that risk-taking behaviours, driver aggression, and study modes are vital factors in the incidence of car crashes among university students.

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Title Risky behaviours among University Students: A reason for the increased
incidences of car crashes in Australia.
Risky behaviours have been known for a very long time to be the reason for the
increased car crashes among young drivers in Australia. This study examines the risk-
taking behaviours among different ages and gender in Australia. Other aspects such as
depression are also covered. A sample of 38,681 drivers aged 16 years to 59 years
was randomly selected from motor vehicle license holders across a number of
campuses in Victoria, Queensland, N.S.W, and A.C.T were given a questionnaire on
attitudes towards general risk-taking behaviours and driving behaviours.
Multivariate analysis has shown that the calculated p-value for all the variables was
higher than 0.05 which was the set alpha value. The conclusion of this study is: that
living arrangement statuses, driving distance, risk-taking behaviours, and driver-
aggression and study modes are vital factors in the incidence of car crash among
university students in Australia. However, the multivariate logistic regression analysis
indicated that there is unit changes in the student living arrangements and the RTA
model used also demonstrated that the log odds of changes by 0.036 while the driver’s
aggressiveness predicted a change of log odds by 0.62. An increase in a car crash as
the driver can be explained by the student's gender, age and risk-taking behaviours. It
is the duty of the public health professionals to come up with the best strategies
modifying risk-taking behaviours among university students.

INTRODUCTION
More often than not cases of tragic car accidents have been recorded in Australia with
the victims being young males aged between 17-24 who are just 12.7% of motor
vehicle license holders yet account for 20.3% of the total road crashes (Bridie, 2018).
Over the years, motorcycle usage among young adults in Australia has seen
significant increase since 1998, where in 2007 the annual increase was 7% (Johnston
et al., 2008). Despite a large number of fatalities reported, a measure that has been
adopted to reduce the cases has not proven to be of much success calling for a shift on
other measures that could help reduce the cases. The focus needs to be shifted from
driver training and education to identifying risk-taking behaviors and the environment
surrounding the occurrence of these accidents.
A car accident statistics report of Australia in 2019 showed that males made up 72%
of all road fatalities between the years 2013-2019. It was realized that the males were
responsible for the crashes indicating a higher rate of risk-taking behaviors amongst
them compared to the females. The risk-taking behaviors included driving while
drunk, over speeding and the use of mobile phones while driving. However, no data
has lined any of these behaviors to the likely percentage of occurrence of the crashes.
The aim of this research paper is to examine how risk-taking behaviors and the likely
occurrence of a crash among Australian university students are correlated.
MATERIAL AND METHODS
Design and Setting
A longitudinal cohort study was carried out on a number of campuses across Victoria,
Queensland, N.S.W, and A.C.T. A sample of 38,681 students was randomly selected

and those who consented to the study were issued with the questionnaires. The data
obtained was then entered in Microsoft Excel with the help of the data entry form that
was designed for the questionnaires and the data was then analyzed using the Excel
software.
RESULTS
From the survey, we had a total of N= 38,681 participants. The average age of the
participants was 20.50 years with the youngest being 16 years and the oldest being 59
years of age. The age distribution was 30.7% a total of 11879 is 18 years. 19-21 years
were 11672 representing 30.2%. 22-25 years were 5494 representing 14.2% and
lastly, 26 years and above were 3755 representing 9.7%. Participants below the age of
18 were excluded from the study due to consent issues.
It was also observed that the largest cohort joined the campus in 2012 representing
17.3% followed by the 2011 group representing 16.5% and lastly 8.4% being the 2005
group. The highest percentage of respondents in the study were female a total of
28232 representing 73% and 23% (10449) being males. In the study, the majority of
the respondents 53.9% (20840) attended the institution from home followed by 28.4%
(10991) who had private accommodation and lastly 6850(17.7%) who were
accommodated by the institution.
From the survey, the majority of the respondents38.9% (15038) was education
students, 30.3% (11729) were health science students, 23.3% (9004) were art and
science students and finally 588 students representing 1.5% were students studying
philosophy and theology.34620 students representing 89.5% were taking a single
degree while 4061 which represents 10.5% were undertaking double degrees. The
majority of the respondents 89.9% were pursuing a full-time mode of education with

the minority 10.5% followed the part-time mode. The majority of the respondents
83.3% were domestic students while 16.7% were international. The majority of the
respondents were metro students representing 70.4% while the nonmetro students
represented 13%. 41% of the participants were from NSW followed by 35.1% from
Victoria, 19.5% from Queensland and finally 4.5% being from ACT city.
This overview look into reasoned that there is no huge contrast in methods for driving
animosity, rush and hazard acceptance among male and female students. To concoct
the end, the p-value was compared with the dimension of the set value. The p-value
was more prominent than the expressed alpha value of 0.05. The investigation
likewise reached the resolution that there is no critical distinction in methods for rush,
hazard acceptance and hostility between the metropolitan statuses. Every one of the
factors yielded a p-value which is greater than the alpha values of 0.05 henceforth the
solid indicting for the end. There was sufficient proof to support the case that there is
no huge contrast in methods for rush and driver animosity between the investigation
modes as the p-value acquired was more noteworthy compared to the set alpha value.
Be that as it may, a critical contrast was seen in methods for hazard acceptance in the
methods of concentrates since the p-value was 0.024 which is under 0.05. The review
likewise understood that there is no noteworthy proof to support the case that there is
no distinction in methods for hostility, thrill, driver animosity and hazard acceptance
between RTA statuses. This determination was made after a comparison of the p-
value determined which not exactly the expressed alpha values were. The study
likewise settled that there is adequate proof to support the case that there is no
distinction in mean in the depression between the two sexual orientations. This was
proven by the p-value determined to be 0.597 which is more noteworthy than the set
alpha value which is 0.05.

The research also found is no sufficient evidence to support the claim that; there is no
difference in the mean of the student's depression and metro statuses. This decision
was reached after a p-value of 0.74 was obtained. The calculated value is greater than
the set alpha value of 0.05. The study also found that there is not enough evidence to
support the claim that there is no significant difference in mean in the students’
depression and their study modes. This conclusion was made after the calculated p-
value yielded 0.8 which is higher than the set alpha value 0f 0.05. On fee statuses, the
study found out that there is enough evidence to support the claim that there is no
significant difference in depression mean of students between fee statuses. A
conclusion that was reached after the calculated p-value yielded 0.96 which was
higher than the set alpha value of 0.05.
Multivariate logistic regression was performed using the RTA one crash and the result
was found that each unit of the student’s living arrangement, the model estimated that
the log odds of RTA changes by 0.036. It was also found that driver aggression
increases the estimated log odds of road accidents by 0.62.
DISCUSSION
The respondents' rate was 93%, indicating the highest rate for any study. This was
consistent with similar studies in this area. It has been seen from the study that male
students recorded the highest risk of the car crash. This is consistent with Bridie's
study that recorded young males aged between 17-24 years who represent 12.7% of
motor vehicle license holders in Australia accounts for 20.3% of the total road crashes
‘(Bridie, 2018)’. Other studies on injury outcome found that drivers seeking thrill
have fewer chances of incurring injury because they are better in handling risky
activities. In this study, the thrill-seeking behaviors among males and females had not

been considered a serious cause for a car crash in Australia. According to some
researchers, motorcyclists’ involved n accidents are young male adults. Most of these
motor vehicle crashes were as result of risky riding behaviors such as excessive
speeding, drug and alcohol use and disobedience of traffic use (Bambach et al., 2012).
The multivariate regression analysis carried out indicated that driver aggression
increases the estimated log odds of a car crash by 0.62. This is explained by risk-
taking behaviors to all drivers of both genders.
When other variables like depression, education statuses, etc were included in the
logistic regression model, the living arrangement was found to increase the logit odds
of OB by 0.06. This also increases risky-behaviors resulting in an increase in a car
crash. However, the parents with a university education were found to have a decrease
of logit odds of OB by 2.65.
High driver aggression and high risk-acceptance are some of the indicators that lead
to his high car crash among the university students, driving distance and living
arrangements have also scored a significant association with the crash. While it has
been known that male drivers aged between 18-25 years are associated with high
chances of a car crash as a driver (Brodie, 2009), the study has found no significant
score related to the claim. The bottleneck in the public-health sector is to identify the
best strategies modifying risk-taking behaviours among university students.
CONCLUSION
This research has shown that living arrangement statuses, driving distance, risk-taking
behaviours, and driver-aggression and study modes are vital factors in the incidence
of car crash among university students in Australia. The public–health professionals

should put this into consideration to develop the best interventions to reduce fatalities
among young drivers.

References
Brodie, L., Lyndal, B., & Elias, I. J. (2009). Heavy vehicle driver fatalities: Learning's
from fatal road crash investigations in Victoria. Accident Analysis &
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Government, A. (2019, January 22 Wednesday). Retrieved from budgetdirect.com.au.
Scott-Parke, B. (2018, January 1). Let's stop blaming young drivers for their deadly
road crashes. abc.net.au.
Scott-Parker, B., & Oviedo-Trespalacios, O. (2017). Young driver risky behavior and
predictors of crash risk in Australia, New Zealand, and Colombia: Same but
different?. Accident Analysis & Prevention, 99, 30-38.
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Table 1: Predictors of mean aggression thrill seeking and risk accepting between
different genders
Independent Samples Test
Levene's Test for Equality
of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Differen
ce
95%
CI
Lower
95%
CI
Upper
Risk accep Equal variances
assumed
1.830 .176 -.829 5146 .407 -.100 -.338 .137
Equal variances
not assumed
-.829 5145.978 .407 -.100 -.338 .137
thrill Equal variances
assumed
40881.922 .000 -65.305 5146 .000 -.622 -.641 -.603
Equal variances
not assumed
-64.849 2555.000 .000 -.622 -.641 -.603
Table 2: Predictors of mean aggression thrill seeking and risk accepting between
different metropolitan background statuses.
Levene's Test
for Equality
of Variances
t-test for Equality of Means
F Sig. t df Sig.
(2-
tailed)
Mean
Difference
Std. Error
Difference
95%
IC
Lower
95%
CI
Upper
Thrill Equal
variance
1.8 .174 .68 3223 .493 .013 .019 -.024 .050

assumed 45 6 6
Equal
variance
not
assumed
.69
2
7048.
178
.489 .013 .019 -.024 .049
Driver
Aggress
.
Equal
variance
assumed
1.0
60
.303 .71
4
3223
6
.475 .048 .067 -.083 .178
Equal
variance
not
assumed
.71
9
7029.
087
.472 .048 .066 -.082 .177
Risk
acc.
Equal
variance
assumed
3.2
28
.072 -.8
66
3223
6
.386 -.058 .067 -.189 .073
Equal
variance
not
assumed
-.8
74
7040.
476
.382 -.058 .066 -.188 .072
Table 3: Gender, age, living arrangement and driver aggression are highly associated with car
crash in Australia
B S.E. Wald df Sig. Exp(B) 95% C.I.for EXP(B
Lower Upper
Age -.052 .006 79.986 1 .000 .949 .938 .960

Gender -.616 .052 139.183 1 .000 .540 .488 .599
Living_Arrange .036 .031 1.370 1 .242 1.036 .976 1.101
Fee_Status .532 .066 65.224 1 .000 1.702 1.496 1.937
Drivers_Aga .621 .024 663.539 1 .000 1.861 1.775 1.951
Thrill .548 .079 47.880 1 .000 1.729 1.481 2.020
Risk acc .611 .010 3986.925 1 .000 1.842 1.807 1.877
Distance
Driving
-.060 .049 1.509 1 .219 .942 .857 1.036
Consent -
16.626
.346 2313.704 1 .000 .000
Table 4: Prediction table on how parent’s education level and student’s living arrangement
affects young car fatalities in Australia.
B S.E. Wald df Sig. Exp(B) 95% C.I
For EXP(B)
Lower Upper
Age -.035 .004 63.931 1 .000 .965 .957 .974
Gender -.373 .039 92.415 1 .000 .688 .638 .743
Living_Arrange .063 .022 8.458 1 .004 1.065 1.021 1.110
Depression 2.707 .056 2317.572 1 .000 14.978 13.416 16.723
Owob-par 1.956 .170 131.591 1 .000 7.068 5.061 9.872

Edu-par -
2.654
.068 1514.337 1 .000 .070 .062 .080
BL-owob -.032 .037 .777 1 .378 .968 .901 1.040
Consent -
2.268
.195 135.973 1 .000 .103

Figure 1: Age of University Student (N= 38,681)
Figure 2: Annual Student Admission in Different University in Australia in 2005-
2012

Figure 3: Student Accommodation Arrangement in Australia.
Figure 5: Student registration per faculty.

Figure 6: Degree-Type
Figure 7: Degree Mode

Figure 8: Distribution the students according to metro status
Figure9: Number of University students per state in Australia