Analysis of Dataset: Descriptive Statistics and T-tests

1
Assessment Task 1: Analysis of dataset

2
1. A. The descriptive statistics of age are as follow,
Mean = 20.5 years
Standard deviation = 4.89 years
Minimum = 16 years
Maximum = 59 years
1. B. Frequency and percentage distribution of categorical variable of age has been
presented in Table 1.
Table 1: Frequency and percentage distribution of categorical variable of age
Note: Age of the participants was categorised according to the description in the table
2. Descriptive statistics for demographics variables has provided in Table 2.
Table 2: Descriptive Statistics for Demographic Variables of ACU

3
Note: Values are number (n) and percentage (%) in parenthesis, except for age which are Mean (SD)
Format Source: Keijzers et al. (2011)
Average age of students was 20.5 years with standard deviation of 4.89 years. Among
38681 participants, 28232 (73%) were females. Majority of the students N =20840
(53.9%) stayed at home during first year of study. Most of the students were domestic
students N = 32238 (83.3%). Prior enrolment details revealed that N = 27223 (70.4%)
students were from metropolitans. Single degree was undertaken by almost all of the
students N = 34620 (89.5%). Among the enrolled students, top three faculties where
students enrolled were Education (N = 15038, P = 38.9%), Health Sciences (N=11729,
P = 30.3%), and Arts and Sciences (N = 9004, P = 23.3%). Students’ enrolment
gradually increased from N = 3259 (8.4%) in 2005 to N = 6697 (17.3%) in 2012.

4
3. A. T-tests for mean of driver aggression, thrill seeking, and risk acceptance
with respect to gender (Kim, 2015).
Table 3: Descriptive statistics of response variables in accordance to gender
a. driver_agg = Driver aggression score, risk_accep = risk acceptance behaviour score, thrill = thrill
seeking behaviour score
b. N = Number of observations or samples for each gender
c. Mean = average aggression scores for genders
d. Std. Deviation = Standard Deviation of scores from mean
e. Std. Error Mean = standard deviation divided by square root of sample size (n) measuring
standard deviation of the sample mean.
Table 4: T-test statistics for assessing difference with respect to gender
f. F = Levene’s test statistics for equality of variances for the two genders.
g. Sig. = Maximum probability of equal variances for male and females.
h. t = t-test statistics for the difference between two means (males – females) =
difference in sample means−difference in population means
pooled sample s . d / √ sample size
i. Sig (2-tailed) = Maximum probability of equality of two means.

5
j. Mean Difference = difference between means of aggression between male and females.
k. Std. Error Difference = standard error or sample mean of the differences between males and
females.
l. 95% confidence interval = 95% probability that the difference between two means of males and
females will lie between the lower and upper limits.
Table 3 indicated that average of the RTA factors, such as aggression, thrill, and
risk acceptance were almost same across the two genders. Table 4 indicated that
there is no statistical evidence to state any difference in these scores with respect to
gender of the participants.
b. T-tests for mean of driver aggression, thrill seeking, and risk acceptance with
respect to metropolitan background status.
Table 5: Descriptive statistics of response variables in accordance to metropolitan background status
a. driver_agg = Driver aggression score, risk_accep = risk acceptance behaviour score, thrill = thrill
seeking behaviour score
b. N = Number of observations or samples for metropolitan background status
c. Mean = average aggression scores for metropolitan background status
d. Std. Deviation = Standard Deviation of scores from mean
e. Std. Error Mean = standard deviation divided by square root of sample size (n) measuring
standard deviation of the sample mean.

6
Table 6: T-test statistics for assessing difference with respect to metropolitan background status
f. F = Levene’s test statistics for equality of variances for metropolitan background status
g. Sig. = Maximum probability of equal variances for metropolitan background status.
h. t = t-test statistics for the difference between two means (Metro – Non-metro) =
difference in sample means−difference in population means
pooled sample s . d / √ sample size
i. Sig (2-tailed) = Maximum probability of equality of two means.
j. Mean Difference = difference between means of aggression between two metropolitan
background status.
k. Std. Error Difference = standard error or sample mean of the differences between two
metropolitan background status.
l. 95% confidence interval = 95% probability that the difference between two means of
metropolitan background status will lie between the lower and upper limits.
Table 5 indicated that average of the RTA factors, such as aggression, thrill, and
risk acceptance were almost same across the two metropolitan background status.
Table 6 indicated that there is no statistical evidence to state any difference in these
scores with respect to metropolitan background status of the participants. It was
also noted from Levene’s test that there was no statistical evidence of unequal
variances as the p-values were all greater than 0.05 (the alpha or level of
significance) (Nordstokke, & Zumbo, 2010).