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PUBH620 Biostatistics - Australian Catholic University
Learning Outcome 1: Distinguish between different statistical tests, especially in terms of application and interpretation.
Learning Outcome 2: Develop a sound statistical approach to the analysis and interpretation of public health data and communicate findings in an academic-standard output.
Learning Outcome 3: Critique public health research on the basis of its statistical methods, analysis and interpretation.
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Question:
Purpose - To further develop students' analytical capabilities and ability to articulate their analysis in a form commonly encountered in practice: an academic journal article.
Present your results for your chosen OPTION ROAD TRAFFIC ACCIDENTS OR OPTION 2 DEPRESSION AND OBESITY in the format of a manuscript suitable for submission to the Medical Journal of Australia.
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Assessment Task 1: Analysis of dataset
Descriptive statistics deals with the collection and presentation of data in the form of tables, graphs and diagrams. In addition, the findings like averages and other statistical measures were also included in the descriptive statistics
Inferential statistics covers hypothesis testing procedures and it is basically used for analysis of data and drawing conclusions with the available information from the sample data. The reliability analysis was also used in this section to validate the data taken into consideration
This study mainly focuses on the use of statistics and its various applications on various fields. Hypothesis testing procedure was mainly used to analyze and interpret the data. In various heath fields too statistics was widely used. In descriptive statistics, mean, median and mode are considered as the best measures of central tendency. Median is the appropriate measure of central tendency as it is not affected by outliers or extreme values. When the population from which the sample is taken is normally distributed, then mean is the appropriate measures of central tendency. When the distribution is skewed, then, the standard deviation could not be considered as the appropriate measure of central tendency. In such situations inter quartile range can be used as an appropriate measure of central tendency. Parametric tests also was not appropriate when the distribution is skewed and non parametric test was used as an alternate option whenever the distribution fails to validate the normality assumption
For the variables that are qualitative, we construct frequency distribution and pie charts, for the quantitative variables, descriptive statistics and histogram was constructed
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1.
a.
|
Descriptives
|
|
|
Statistic
|
Std. Error
|
|
AGE
|
Mean
|
20.50
|
.025
|
|
95% Confidence Interval for Mean
|
Lower Bound
|
20.45
|
|
|
Upper Bound
|
20.55
|
|
|
5% Trimmed Mean
|
19.77
|
|
|
Median
|
19.00
|
|
|
Variance
|
23.893
|
|
|
Std. Deviation
|
4.888
|
|
|
Minimum
|
16
|
|
|
Maximum
|
59
|
|
|
Range
|
43
|
|
|
Interquartile Range
|
3
|
|
|
Skewness
|
3.339
|
.012
|
|
Kurtosis
|
14.795
|
.025
|
The mean age is $ 20.5 ± 4.89 and the median age is 19. The recorded minimum and maximum age of the respondents is 16 years and 59 years respectively. Here, we see that the mean age is greater than the median age, indicating that the distribution of age is skewed right. Thus, the assumption of age distribution follows normal distribution is violated in this case
b.
|
Age Category
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
Aged 18 years at time of enrolment
|
17760
|
45.9
|
45.9
|
45.9
|
|
Aged 19 to 21 at time of enrolment
|
11672
|
30.2
|
30.2
|
76.1
|
|
Aged 22 to 25 at time of enrolment
|
5494
|
14.2
|
14.2
|
90.3
|
|
Aged 26 or more at time of enrolment
|
3755
|
9.7
|
9.7
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
Regarding the age at the time of enrolment, about 45.9% of the respondents enrolled at the age of 18 years, 30.2% of the respondents enrolled at the age between 19 years and 21 years, 14.2% of the respondents enrolled at the age between 22 years and 25 years and between 9.7% of the respondents enrolled after 25 years of age
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2.
|
STATE
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
NSW
|
15860
|
41.0
|
41.0
|
41.0
|
|
Victoria
|
13571
|
35.1
|
35.1
|
76.1
|
|
Queensland
|
7528
|
19.5
|
19.5
|
95.5
|
|
ACT
|
1722
|
4.5
|
4.5
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
Regarding the state, about 41% of the respondents enrolled were from NSW and 35.1% of the respondents enrolled were from Victoria
|
GENDER
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
Male
|
10449
|
27.0
|
27.0
|
27.0
|
|
Female
|
28232
|
73.0
|
73.0
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
About 27% of the respondents included in this study are males and 73% of them are females, indicating that the study was dominated by female counterparts
|
LIVING_ARRANGE
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
At home
|
20840
|
53.9
|
53.9
|
53.9
|
|
College/student accom
|
6850
|
17.7
|
17.7
|
71.6
|
|
Independently
|
10991
|
28.4
|
28.4
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
About 53.9% of the respondents who enrolled in this study said that they are living in their home, 17.7% of the respondents uses college accommodation facilities and 28.4% of the respondents said that they are living independently
|
FACULTY
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
Arts and Sciences
|
9004
|
23.3
|
23.3
|
23.3
|
|
Education
|
15038
|
38.9
|
38.9
|
62.2
|
|
Health Sciences
|
11729
|
30.3
|
30.3
|
92.5
|
|
Theology and Philosophy
|
588
|
1.5
|
1.5
|
94.0
|
|
Business
|
2322
|
6.0
|
6.0
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
DEGREE_TYPE
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
Single
|
34620
|
89.5
|
89.5
|
89.5
|
|
Double
|
4061
|
10.5
|
10.5
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
METRO
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
Metro
|
27223
|
70.4
|
84.4
|
84.4
|
|
Non-metro
|
5015
|
13.0
|
15.6
|
100.0
|
|
Total
|
32238
|
83.3
|
100.0
|
|
|
Missing
|
System
|
6443
|
16.7
|
|
|
|
Total
|
38681
|
100.0
|
|
|
|
STUDY_MODE
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
FT
|
34770
|
89.9
|
89.9
|
89.9
|
|
PT
|
3911
|
10.1
|
10.1
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
FEE_STATUS
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
Domestic
|
32238
|
83.3
|
83.3
|
83.3
|
|
International
|
6443
|
16.7
|
16.7
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
dist_driving
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
Valid
|
Less than 10 km a week
|
14601
|
37.7
|
37.7
|
37.7
|
|
More than 10 km a week
|
24080
|
62.3
|
62.3
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
RTA_one_crash
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
Valid
|
No RTAs
|
33628
|
86.9
|
86.9
|
86.9
|
|
One RTA or more
|
5053
|
13.1
|
13.1
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
BL_owob
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
Valid
|
Normal weight or underweight
|
14601
|
37.7
|
37.7
|
37.7
|
|
Overweight or obese
|
24080
|
62.3
|
62.3
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
owob_par
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
Valid
|
No obese parents
|
14455
|
37.4
|
37.4
|
37.4
|
|
At least one obese parent
|
24226
|
62.6
|
62.6
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
depression
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
Valid
|
Not depressed
|
34720
|
89.8
|
89.8
|
89.8
|
|
Depressed
|
3961
|
10.2
|
10.2
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
OB
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
Valid
|
Not obese
|
33628
|
86.9
|
86.9
|
86.9
|
|
Obese
|
5053
|
13.1
|
13.1
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
edu_par
|
|
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
.00
|
16826
|
43.5
|
43.5
|
43.5
|
|
1.00
|
12918
|
33.4
|
33.4
|
76.9
|
|
2.00
|
8937
|
23.1
|
23.1
|
100.0
|
|
Total
|
38681
|
100.0
|
100.0
|
|
|
Descriptives
|
|
|
Statistic
|
Std. Error
|
|
driver_agg
|
Mean
|
7.52
|
.022
|
|
95% Confidence Interval for Mean
|
Lower Bound
|
7.47
|
|
|
Upper Bound
|
7.56
|
|
|
5% Trimmed Mean
|
7.52
|
|
|
Median
|
8.00
|
|
|
Variance
|
18.868
|
|
|
Std. Deviation
|
4.344
|
|
|
Minimum
|
0
|
|
|
Maximum
|
15
|
|
|
Range
|
15
|
|
|
Interquartile Range
|
7
|
|
|
Skewness
|
-.004
|
.012
|
|
Kurtosis
|
-1.173
|
.025
|
|
thrill
|
Mean
|
5.00
|
.006
|
|
95% Confidence Interval for Mean
|
Lower Bound
|
4.99
|
|
|
Upper Bound
|
5.01
|
|
|
5% Trimmed Mean
|
5.00
|
|
|
Median
|
5.00
|
|
|
Variance
|
1.497
|
|
|
Std. Deviation
|
1.224
|
|
|
Minimum
|
3
|
|
|
Maximum
|
7
|
|
|
Range
|
4
|
|
|
Interquartile Range
|
2
|
|
|
Skewness
|
-.004
|
.012
|
|
Kurtosis
|
-.998
|
.025
|
|
risk_accep
|
Mean
|
8.53
|
.022
|
|
95% Confidence Interval for Mean
|
Lower Bound
|
8.49
|
|
|
Upper Bound
|
8.57
|
|
|
5% Trimmed Mean
|
8.53
|
|
|
Median
|
9.00
|
|
|
Variance
|
18.985
|
|
|
Std. Deviation
|
4.357
|
|
|
Minimum
|
1
|
|
|
Maximum
|
16
|
|
|
Range
|
15
|
|
|
Interquartile Range
|
7
|
|
|
Skewness
|
-.006
|
.012
|
|
Kurtosis
|
-1.188
|
.025
|
Aggression Scores
The mean aggression score of drivers is 7.52 ± 4.34 with a median value of 8 and the recorded minimum and maximum aggression score of driver is 0 and 15 respectively. The mean score for thrill seeking behaviours is 5 ± 1.224 with a median value of 5 and the recorded minimum and maximum score for thrill seeking behaviour is 3 and 7 respectively. The mean score of risk acceptance behaviours is 8.53 ± 4.36 with a median value of 9 and the recorded minimum and maximum score of risk acceptance behaviour is 1 and 16 respectively.
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3.
Test Used: Independent Sample t test
Independent Variable: Gender
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
|
Group Statistics
|
|
|
GENDER
|
N
|
Mean
|
Std. Deviation
|
Std. Error Mean
|
|
driver_agg
|
Male
|
10449
|
7.52
|
4.335
|
.042
|
|
Female
|
28232
|
7.51
|
4.347
|
.026
|
|
thrill
|
Male
|
10449
|
5.00
|
1.217
|
.012
|
|
Female
|
28232
|
5.00
|
1.226
|
.007
|
|
risk_accep
|
Male
|
10449
|
8.59
|
4.358
|
.043
|
|
Female
|
28232
|
8.51
|
4.357
|
.026
|
|
Independent Samples Test
|
|
|
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% Confidence Interval of the Difference
|
|
Lower
|
Upper
|
|
driver_agg
|
Equal variances assumed
|
.117
|
.732
|
.083
|
38679
|
.934
|
.004
|
.050
|
-.093
|
.102
|
|
Equal variances not assumed
|
|
|
.083
|
18712.803
|
.934
|
.004
|
.050
|
-.093
|
.102
|
|
thrill
|
Equal variances assumed
|
.847
|
.357
|
-.370
|
38679
|
.711
|
-.005
|
.014
|
-.033
|
.022
|
|
Equal variances not assumed
|
|
|
-.371
|
18783.250
|
.710
|
-.005
|
.014
|
-.033
|
.022
|
|
risk_accep
|
Equal variances assumed
|
.054
|
.817
|
1.571
|
38679
|
.116
|
.078
|
.050
|
-.019
|
.176
|
|
Equal variances not assumed
|
|
|
1.571
|
18663.180
|
.116
|
.078
|
.050
|
-.019
|
.176
|
From the above output, we see that
- There is no significant difference in scoring the driver aggression between male and female counterparts (p - value = 0.934 > 0.05)
- There is no significant difference in scoring the thrill seeking behaviour between male and female counterparts (p - value = 0.711 > 0.05)
- There is no significant difference in scoring the risk acceptance between male and female counterparts (p - value = 0.116 > 0.05)
Test Used: Independent Sample t test
Independent Variable: Metro
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
|
Group Statistics
|
|
|
METRO
|
N
|
Mean
|
Std. Deviation
|
Std. Error Mean
|
|
driver_agg
|
Metro
|
27223
|
7.53
|
4.345
|
.026
|
|
Non-metro
|
5015
|
7.49
|
4.303
|
.061
|
|
thrill
|
Metro
|
27223
|
5.01
|
1.224
|
.007
|
|
Non-metro
|
5015
|
4.99
|
1.207
|
.017
|
|
risk_accep
|
Metro
|
27223
|
8.53
|
4.358
|
.026
|
|
Non-metro
|
5015
|
8.59
|
4.304
|
.061
|
|
Independent Samples Test
|
|
|
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% Confidence Interval of the Difference
|
|
Lower
|
Upper
|
|
driver_agg
|
Equal variances assumed
|
1.060
|
.303
|
.714
|
32236
|
.475
|
.048
|
.067
|
-.083
|
.178
|
|
Equal variances not assumed
|
|
|
.719
|
7029.087
|
.472
|
.048
|
.066
|
-.082
|
.177
|
|
thrill
|
Equal variances assumed
|
1.845
|
.174
|
.686
|
32236
|
.493
|
.013
|
.019
|
-.024
|
.050
|
|
Equal variances not assumed
|
|
|
.692
|
7048.178
|
.489
|
.013
|
.019
|
-.024
|
.049
|
|
risk_accep
|
Equal variances assumed
|
3.228
|
.072
|
-.866
|
32236
|
.386
|
-.058
|
.067
|
-.189
|
.073
|
|
Equal variances not assumed
|
|
|
-.874
|
7040.476
|
.382
|
-.058
|
.066
|
-.188
|
.072
|
From the above output, we see that
- There is no significant difference in scoring the driver aggression between metro and non-metro residents (p - value = 0.475> 0.05)
- There is no significant difference in scoring the thrill seeking behaviour between metro and non-metro residents (p - value = 0.493> 0.05)
- There is no significant difference in scoring the risk acceptance between metro and non-metro residents (p - value = 0.386> 0.05)
Test Used: Independent Sample t test
Independent Variable: Study Mode
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
|
Group Statistics
|
|
|
STUDY_MODE
|
N
|
Mean
|
Std. Deviation
|
Std. Error Mean
|
|
driver_agg
|
FT
|
34770
|
7.51
|
4.344
|
.023
|
|
PT
|
3911
|
7.54
|
4.339
|
.069
|
|
thrill
|
FT
|
34770
|
5.00
|
1.224
|
.007
|
|
PT
|
3911
|
5.00
|
1.225
|
.020
|
|
risk_accep
|
FT
|
34770
|
8.51
|
4.355
|
.023
|
|
PT
|
3911
|
8.68
|
4.374
|
.070
|
|
Independent Samples Test
|
|
|
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% Confidence Interval of the Difference
|
|
Lower
|
Upper
|
|
driver_agg
|
Equal variances assumed
|
.323
|
.570
|
-.309
|
38679
|
.757
|
-.023
|
.073
|
-.166
|
.121
|
|
Equal variances not assumed
|
|
|
-.310
|
4834.453
|
.757
|
-.023
|
.073
|
-.166
|
.121
|
|
thrill
|
Equal variances assumed
|
.222
|
.637
|
.132
|
38679
|
.895
|
.003
|
.021
|
-.038
|
.043
|
|
Equal variances not assumed
|
|
|
.132
|
4829.635
|
.895
|
.003
|
.021
|
-.038
|
.043
|
|
risk_accep
|
Equal variances assumed
|
.045
|
.832
|
-2.269
|
38679
|
.023
|
-.167
|
.073
|
-.311
|
-.023
|
|
Equal variances not assumed
|
|
|
-2.261
|
4823.706
|
.024
|
-.167
|
.074
|
-.311
|
-.022
|
From the above output, we have
» The mean driver aggression scoring is not high for part time students when compared to that of full time students (p - value = 0.757 > 0.05)
» The mean thrill seeking behaviour scoring is not high for part time students when compared to that of full time students (p - value = 0.895> 0.05)
» The mean risk acceptance behaviour scoring is high for part time students when compared to that of full time students (p - value = 0.023< 0.05)
Test Used: Independent Sample t test
Independent Variable: RTA and one or more
Dependent Variables: Driver Aggression Score, Thrill Score and Risk Acceptance Score
|
Group Statistics
|
|
|
RTA_one_crash
|
N
|
Mean
|
Std. Deviation
|
Std. Error Mean
|
|
driver_agg
|
No RTAs
|
33628
|
6.79
|
4.116
|
.022
|
|
One RTA or more
|
5053
|
12.34
|
2.218
|
.031
|
|
thrill
|
No RTAs
|
33628
|
4.80
|
1.159
|
.006
|
|
One RTA or more
|
5053
|
6.34
|
.686
|
.010
|
|
risk_accep
|
No RTAs
|
33628
|
7.91
|
4.210
|
.023
|
|
One RTA or more
|
5053
|
12.68
|
2.748
|
.039
|
|
Independent Samples Test
|
|
|
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% Confidence Interval of the Difference
|
|
Lower
|
Upper
|
|
driver_agg
|
Equal variances assumed
|
3179.609
|
.000
|
-93.863
|
38679
|
.000
|
-5.552
|
.059
|
-5.668
|
-5.436
|
|
Equal variances not assumed
|
|
|
-144.454
|
11183.466
|
.000
|
-5.552
|
.038
|
-5.627
|
-5.476
|
|
thrill
|
Equal variances assumed
|
1715.363
|
.000
|
-92.063
|
38679
|
.000
|
-1.539
|
.017
|
-1.572
|
-1.507
|
|
Equal variances not assumed
|
|
|
-133.493
|
10036.697
|
.000
|
-1.539
|
.012
|
-1.562
|
-1.517
|
|
risk_accep
|
Equal variances assumed
|
1951.956
|
.000
|
-78.154
|
38679
|
.000
|
-4.775
|
.061
|
-4.895
|
-4.655
|
|
Equal variances not assumed
|
|
|
-106.209
|
9076.181
|
.000
|
-4.775
|
.045
|
-4.863
|
-4.687
|
From the above output, we have
- The mean driver aggression scoring is high for one or more RTA when compared to that of no RTA students (p - value = 0.000 < 0.05)
- The mean thrill seeking behaviour scoring is high for one or more RTA when compared to that of no RTA students (p - value = 0.000 < 0.05)
- The mean risk acceptance behaviour scoring is high for one or more RTA when compared to that of no RTA students (p - value = 0.000 < 0.05)
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4.
Test Used: Chi Square Test for Independence
Independent Variable: Gender
Dependent Variables: Depression Scores
|
Count
|
|
|
GENDER
|
Total
|
|
Male
|
Female
|
|
depression
|
Not depressed
|
9365
|
25355
|
34720
|
|
Depressed
|
1084
|
2877
|
3961
|
|
Total
|
10449
|
28232
|
38681
|
|
Chi-Square Tests
|
|
|
Value
|
df
|
Asymptotic Significance (2-sided)
|
Exact Sig. (2-sided)
|
Exact Sig. (1-sided)
|
|
Pearson Chi-Square
|
.280a
|
1
|
.597
|
|
|
|
Continuity Correctionb
|
.260
|
1
|
.610
|
|
|
|
Likelihood Ratio
|
.279
|
1
|
.597
|
|
|
|
Fisher's Exact Test
|
|
|
|
.598
|
.305
|
|
Linear-by-Linear Association
|
.280
|
1
|
.597
|
|
|
|
N of Valid Cases
|
38681
|
|
|
|
|
|
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 1070.00.
|
|
b. Computed only for a 2x2 table
|
Here, we see that the value of chi square test statistic is 0.597 > 0.05, indicating that the variables Gender and Depression are independent of each other
Test Used: Chi Square Test for Independence
Independent Variable: Metro
Dependent Variables: Depression Scores
|
Count
|
|
|
METRO
|
Total
|
|
Metro
|
Non-metro
|
|
depression
|
Not depressed
|
24443
|
4495
|
28938
|
|
Depressed
|
2780
|
520
|
3300
|
|
Total
|
27223
|
5015
|
32238
|
|
Chi-Square Tests
|
|
|
Value
|
df
|
Asymptotic Significance (2-sided)
|
Exact Sig. (2-sided)
|
Exact Sig. (1-sided)
|
|
Pearson Chi-Square
|
.114a
|
1
|
.736
|
|
|
|
Continuity Correctionb
|
.097
|
1
|
.755
|
|
|
|
Likelihood Ratio
|
.113
|
1
|
.737
|
|
|
|
Fisher's Exact Test
|
|
|
|
.743
|
.378
|
|
Linear-by-Linear Association
|
.114
|
1
|
.736
|
|
|
|
N of Valid Cases
|
32238
|
|
|
|
|
|
a. 0 cells (0.0%) have expected count less than 5. The minimum expected count is 513.35.
|
|
b. Computed only for a 2x2 table
|
Here, we see that the p - value of chi square test statistic is 0.736 > 0.05, indicating that the variables metro and depression are independent of each other
Test Used: Chi Square Test for Independence
Independent Variable: Study Mode
Dependent Variables: Depression Scores
|
|
STUDY_MODE
|
Total
|
|
FT
|
PT
|
|
depression
|
Not depressed
|
31241
|
3479
|
34720
|
|
Depressed
|
3529
|
432
|
3961
|
|
Total
|
34770
|
3911
|
38681
|
|
Chi-Square Tests
|
|
|
Value
|
df
|
Asymptotic Significance (2-sided)
|
Exact Sig. (2-sided)
|
Exact Sig. (1-sided)
|
|
Pearson Chi-Square
|
3.072a
|
1
|
.080
|
|
|
|
Continuity Correctionb
|
2.975
|
1
|
.085
|
|
|
|
Likelihood Ratio
|
3.011
|
1
|
.083
|
|
|
|
Fisher's Exact Test
|
|
|
|
.084
|
.042
|
|
Linear-by-Linear Association
|
3.072
|
1
|
.080
|
|
|
|
N of Valid Cases
|
38681
|
|
|
|
|
Here, we see that the p - value of chi square test statistic is 0.08> 0.05, indicating that the variables study mode and depression are independent of each other
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5. Binary Logistic Regression
Age
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
age_category
|
|
|
54.778
|
3
|
.000
|
|
|
|
|
age_category(1)
|
.320
|
.057
|
31.249
|
1
|
.000
|
1.377
|
1.231
|
1.540
|
|
age_category(2)
|
.252
|
.060
|
17.836
|
1
|
.000
|
1.286
|
1.144
|
1.445
|
|
age_category(3)
|
.046
|
.068
|
.447
|
1
|
.504
|
1.047
|
.916
|
1.196
|
|
Constant
|
-2.130
|
.053
|
1617.179
|
1
|
.000
|
.119
|
|
|
|
a. Variable(s) entered on step 1: age_category.
|
The odds ratio for RTA one crash for Students aged 18 years at time of enrolment 1.377, younger aged adults are at 1.4 times higher risk of involving in one or more RTA crash (p < 0.05)
The odds ratio for RTA one crash for Students aged 19 and 21 years at time of enrolment 1.286, younger aged adults are at 1.286 times higher risk of involving in one or more RTA crash (p < 0.05)
The odds ratio for RTA one crash for Students aged 22 to 25 years is1.047, indicating that the students aged 22 - 25 years are at 1.047 times higher risk of involving in one or more crash when compared to students aged 26 years and above and statistically insignificant (p < 0.504)
Gender
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
GENDER(1)
|
.265
|
.033
|
65.690
|
1
|
.000
|
1.303
|
1.222
|
1.389
|
|
Constant
|
-1.972
|
.018
|
11774.749
|
1
|
.000
|
.139
|
|
|
|
a. Variable(s) entered on step 1: GENDER.
|
The odds ratio for RTA one crash for male Students is1.303, indicating that the male students are at 1.303 times higher risk of involving in one or more crash when compared to female students and statistically significant (p < 0.05)
Living arrangements (enter as categorical variable)
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
LIVING_ARRANGE
|
|
|
12.178
|
2
|
.002
|
|
|
|
|
LIVING_ARRANGE(1)
|
.028
|
.035
|
.605
|
1
|
.437
|
1.028
|
.959
|
1.102
|
|
LIVING_ARRANGE(2)
|
.149
|
.045
|
10.958
|
1
|
.001
|
1.160
|
1.063
|
1.267
|
|
Constant
|
-1.938
|
.029
|
4541.261
|
1
|
.000
|
.144
|
|
|
|
a. Variable(s) entered on step 1: LIVING_ARRANGE.
|
The odds ratio for RTA one crash for Students living in college accommodation is1.028, indicating that students who are not with their parents are at slightly higher risk in involving in RTA one or more crash, but statistically insignificant (p = 0.437> 0.05)
The odds ratio for RTA one crash for Students living independently is1.16, indicating that students who are not with their parents and living independently are at slightly higher risk in involving in RTA one or more crash, but statistically significant (p = 0.001 < 0.05)
Domestic/international status
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
FEE_STATUS(1)
|
.217
|
.039
|
31.736
|
1
|
.000
|
1.242
|
1.152
|
1.340
|
|
Constant
|
-1.934
|
.017
|
13306.670
|
1
|
.000
|
.145
|
|
|
|
a. Variable(s) entered on step 1: FEE_STATUS.
|
Odds Ratio: 1.242
P - Value: 0.000
Therefore, the risk of RTA one or more crash is high for students with international status and statistically significant
Driving distance
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
dist_driving
|
-.016
|
.031
|
.268
|
1
|
.605
|
.984
|
.926
|
1.046
|
|
Constant
|
-1.885
|
.024
|
5937.971
|
1
|
.000
|
.152
|
|
|
|
a. Variable(s) entered on step 1: dist_driving.
|
Odds Ratio: 1.242
P - Value: 0.000
Therefore, the variable driving distance is not significant predictor of RTA one or more crash
Aggression
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
driver_agg
|
.457
|
.007
|
4638.311
|
1
|
.000
|
1.580
|
1.559
|
1.601
|
|
Constant
|
-6.495
|
.081
|
6474.348
|
1
|
.000
|
.002
|
|
|
|
a. Variable(s) entered on step 1: driver_agg.
|
Odds Ratio: 1.58
P - Value: 0.000
Therefore, the risk of RTA one or more crash is 1.58 high for students whose driving aggression score increases by one unit and statistically significant
Thrill-seeking
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
thrill
|
1.487
|
.021
|
4901.218
|
1
|
.000
|
4.425
|
4.245
|
4.613
|
|
Constant
|
-10.332
|
.131
|
6179.082
|
1
|
.000
|
.000
|
|
|
|
a. Variable(s) entered on step 1: thrill.
|
Odds Ratio: 4.425
P - Value: 0.000
Therefore, the risk of RTA one or more crash is 4.425 high for students whose thrill seeking score increases by one unit and statistically significant
Risk acceptance
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
risk_accep
|
.335
|
.005
|
3959.400
|
1
|
.000
|
1.397
|
1.383
|
1.412
|
|
Constant
|
-5.433
|
.067
|
6641.557
|
1
|
.000
|
.004
|
|
|
|
a. Variable(s) entered on step 1: risk_accep.
|
Odds Ratio: 1.397
P - Value: 0.000
Therefore, the risk of RTA one or more crash is 1.397 high for students whose risk acceptance score increases by one unit and statistically significant
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6.
Age
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
age_category
|
|
|
54.778
|
3
|
.000
|
|
|
|
|
age_category(1)
|
.320
|
.057
|
31.249
|
1
|
.000
|
1.377
|
1.231
|
1.540
|
|
age_category(2)
|
.252
|
.060
|
17.836
|
1
|
.000
|
1.286
|
1.144
|
1.445
|
|
age_category(3)
|
.046
|
.068
|
.447
|
1
|
.504
|
1.047
|
.916
|
1.196
|
|
Constant
|
-2.130
|
.053
|
1617.179
|
1
|
.000
|
.119
|
|
|
|
a. Variable(s) entered on step 1: age_category.
|
Odds Ratio: 1.377
P - Value: 0.000
Therefore, the risk of obese is 1.377 high for students whose age at the time of enrolment is 18 years and statistically significant
Odds Ratio: 1.286
P - Value: 0.000
Therefore, the risk of obese is 1.286 high for students whose age at the time of enrolment is 19 and 21 years and statistically significant
Odds Ratio: 1.047
P - Value: 0.504
Therefore, the risk of obese is 1.286 high for students whose age at the time of enrolment is 22 and 25 years and statistically significant
Gender
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
GENDER(1)
|
.265
|
.033
|
65.690
|
1
|
.000
|
1.303
|
1.222
|
1.389
|
|
Constant
|
-1.972
|
.018
|
11774.749
|
1
|
.000
|
.139
|
|
|
|
a. Variable(s) entered on step 1: GENDER.
|
Odds Ratio: 1.303
P - Value: 0.000
Therefore, the risk of obese is 1.303 high for male students when compared to that of female counterparts and statistically significant
Living Arrangements
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
LIVING_ARRANGE
|
|
|
12.178
|
2
|
.002
|
|
|
|
|
LIVING_ARRANGE(1)
|
.121
|
.040
|
9.073
|
1
|
.003
|
1.129
|
1.043
|
1.221
|
|
LIVING_ARRANGE(2)
|
-.028
|
.035
|
.605
|
1
|
.437
|
.973
|
.908
|
1.043
|
|
Constant
|
-1.910
|
.021
|
8541.127
|
1
|
.000
|
.148
|
|
|
|
a. Variable(s) entered on step 1: LIVING_ARRANGE.
|
Odds Ratio: 1.028
P - Value: 0.437
Therefore, the risk of obese is 1.028 high for students who use college accommodation and statistically insignificant
Odds Ratio: 0.973
P - Value: 0.001
Therefore, the risk of obese is 0.973lessrisk for students are living independently and statistically significant
Overweight or obese at baseline
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
BL_owob(1)
|
-.016
|
.031
|
.268
|
1
|
.605
|
.984
|
.926
|
1.046
|
|
Constant
|
-1.885
|
.024
|
5937.971
|
1
|
.000
|
.152
|
|
|
|
a. Variable(s) entered on step 1: BL_owob.
|
The variable overweight or obese at baseline act as a protective factor for obese and statistically insignificant I(p > 0.05)
Depression at baseline
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
depression(1)
|
1.787
|
.037
|
2325.604
|
1
|
.000
|
5.970
|
5.552
|
6.420
|
|
Constant
|
-2.197
|
.018
|
15085.364
|
1
|
.000
|
.111
|
|
|
|
a. Variable(s) entered on step 1: depression.
|
Odds Ratio: 5.97
P - Value: 0.000
Therefore, the risk of obese is 5.97 high for Students who are depressed at baseline and statistically significant
Parental factors
|
Variables in the Equation
|
|
|
B
|
S.E.
|
Wald
|
df
|
Sig.
|
Exp(B)
|
95% C.I.for EXP(B)
|
|
Lower
|
Upper
|
|
Step 1a
|
edu_par
|
-2.726
|
.054
|
2535.296
|
1
|
.000
|
.065
|
.059
|
.073
|
|
Constant
|
-.943
|
.017
|
3028.510
|
1
|
.000
|
.390
|
|
|
|
a. Variable(s) entered on step 1: edu_par.
|
Odds Ratio: 5.97
P - Value: 0.000
Therefore, the risk of obese is 0.065 times less risk for Students whose parents education increases by one year and statistically significant.
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