Analyzing Hazardous Alcohol Consumption Among University Students in Ireland

This paper analyzes the hazardous alcohol consumption among university students in Ireland. It includes a critical appraisal of the study, descriptive and frequency statistics, and a predictive linear regression model. The paper also discusses the weaknesses and strengths of the study.

Running head: BIOSTATISTICS
Introduction to Biostatistics
Name of Student:
Name of University:
Course ID:

1BIOSTATISTICS
Table of Contents
Question 1........................................................................................................................................2
Item 10.........................................................................................................................................2
Item 12.........................................................................................................................................2
Item 13.........................................................................................................................................2
Item 14.........................................................................................................................................2
Item 15.........................................................................................................................................3
Item 16.........................................................................................................................................3
Item 17.........................................................................................................................................3
Question 2........................................................................................................................................4
Descriptive Statistics:..................................................................................................................4
Frequency Statistics:....................................................................................................................8
Question 2......................................................................................................................................10
Predictive linear regression model:...........................................................................................10
Conclusion:....................................................................................................................................13
References:....................................................................................................................................14

2BIOSTATISTICS
Question 1.
In this assignment, the analyst critically has analysed the paper of Davoren et al., (2015),
on the topic of hazardous alcohol consumption among university students in Ireland. It is a cross-
sectional study that was published in the “CrossMark” publication. The STROBE statement is
constructed based on critical appraisal (Von Elm et al. 2007).
Item 10
Weakness Strength
The overall response rate discussed in terms
of students registered for defined modules
was only 51%. The data is collected from a
target population “Irish University”.
The chosen samples for the study was
elaborated according to STROBES
parameters (Vandenbroucke et al. 2007). The
inclusion and exclusion criteria, age, BMI,
years in village, accommodation, physical
activity and smoking habit is provided in
details.
Item 12
Weakness Strength
Mean and Standard deviation is not much
helpful in studying continuous quantitative
variables.
The different qualitative and quantitative
variables are discriminated as per both types
of sex. The central tendencies, location
measures and spread are calculated and
tabulated.
Item 13
Weakness Strength
The majority of the non-respondents were the
students absent from class during the survey.
The next group is unlikely to have a more
favourable pattern of alcohol consumption
than that observed in this research (Davoren
et al. 2015).
The present study engaged the standardised
processes for the measurement of hazardous
alcohol consumption and rigorous probability
proportion to restrict sampling strategy for the
classroom-based survey.
Item 14
Weakness Strength
The study does not give an insight in the two
locations and the percentage population of the
subjects were recruited.
The case study might be regarded as reporting
of the lower bound estimates of the hazardous
alcohol consumption in Irish University

3BIOSTATISTICS
students. A total of 2275 undergraduate
students finished the classroom survey where
84% students were in the class and 51% of the
students have registered for the relevant
module (Davoren et al. 2015).
Item 15
Weakness Strength
The research study includes a cross-sectional
study of a certain time-period (Davoren et al.
2015). The validity of the outcomes is not
cross-checked throughout a prolonged time
period.
Students with a hazardous consumption
pattern more likely to report smoking, illicit
drug use and course of education. It refers a
decision point with respect to the policies on
the promotion and marketing of alcohol. The
study highlights the requirement for effective
policy making measures in response to the
challenges such as a lower minimum unit
price for alcohol and a restriction on the
sponsorship of sports.
Item 16
Weakness Strength
The data for hazardous alcohol consumption
and non-hazardous alcohol consumption with
respect to adverse consequences are only
represented in the term of percentages. Its
discussion is not elaborately discussed as per
conformation.
Although the response rate is low, the major
national and international research of the
students falls short of expected response rate
of at least 70% in health and well-being
surveys. The immediate adverse of
consequences and long-term risks to physical,
social, mental and well-being.
Item 17
Weakness Strength
The prevalence of HAC was greater in fourth
year students than first year students. The lack
of balance in this case of in sampling is likely
to lead an underestimation of overall
prevalence of HAC. The pattern of alcohol
consumption as per gender is not unique to
the Irish University.
The diversity in pattern of alcohol
consumption established a campus-wide
health promoting university initiative with a
specified point and dedicated resources
concentrated on the problem of high alcohol
consumption. HAC is found to be a public
health problem in Irish university students.

4BIOSTATISTICS
Question 2.
Descriptive Statistics:
Variabl
es
Sex Frequ
ency
Me
an
Stand
ard
Devia
tion
Stan
dard
Erro
r of
mean
Mini
mum
Med
ian
Maxi
mum
95
%
up
per
CI
of
me
an
95
%
lo
we
r
CI
of
me
an
p-
valu
e
“age” Mal
e
161 19.
53
1.43 0.11 17 19 23 19.
75
19.
31
0.08
064
Fem
ale
112 19.
84
1.45 0.14 17 20 24 20.
12
19.
56
“alc” Mal
e
161 10.
48
12.31 0.97 0.5 6 90 12.
42
8.5
4
0.01
847
Fem
ale
112 7.0
5
11.34 1.07 0.5 2 59 9.1
9
4.9
1
“WEM
WBS”
Mal
e
161 44.
1
4.04 0.32 32.5 44 53.3 44.
74
43.
46
0.18
82
Fem
ale
112 43.
49
3.48 0.33 34.7 43.8 51.9 44.
15
42.
83
The average and standard deviation age of the male is less than female. The age of the
male participants ranges from 17 to 23, whereas the age of female participants ranges from 17 to
24. The median age of the males and females are 19 and 20. The lower and lower limits of ages
of males are 19.31 and 19.75 with 95% probability whereas the upper and lower limits of ages of
females are 20.12 and 19.56.
The average and standard deviation of alcohol consumption per capita in a week
measured is greater for males than females. The alcohol consumption per capita in a week for
“Males” ranges from 0.5 litres to 90 litres, whereas the alcohol consumption per capita in a week
for females ranges from 0.5 litres to 59 litres. The alcohol consumption per capita in a week has
median for males and females are 6 and 2. The lower and upper limits of weekly alcohol
consumption of males are 8.54 and 12.42. The lower and upper limits of weekly alcohol
consumption of females are 4.91 and 9.19.
The average and standard deviation of the measured WEMWBS scale for Males is
greater than Females. The observed highest measure and lowest measure in WEMWBS scale for
males vary from 32.5 and 53.3 and females vary from 34.7 and 51.9. The median WEMWBS
scale for males and females is 44 and 43.8 respectively. The lower and lower limits of

5BIOSTATISTICS
WEMWBS scale of males are 43.46 and 44.74. The upper and lower limits of WEMWBS scale
of females are 44.15 and 42.83.
sex = male
Classes
F r e q u e n c i e s
17 18 19 20 21 22 23 24
0 2 0 4 0
sex = female
Classes
F r e q u e n c i e s
17 18 19 20 21 22 23 24
0 2 0 4 0
Distribution of age genderwise
The female frequency distribution of age is normally distributed whereas the male
frequency distribution of age is positively skewed.
sex = male
Classes
F r e q u e n c i e s
0 20 40 60 80
0 4 0 1 0 0
sex = female
Classes
F r e q u e n c i e s
0 20 40 60 80
0 4 0 1 0 0
Distribution of average alcohol consumption genderwise

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The frequency distribution of gender wise average alcohol consumption is highly
positively distributed.
male female
3 5 4 0 4 5 5 0
Distribution of WEMWBS sexwise
sex
W E M W B S o f M a le s a n d F e m a le s
267
The distribution of WEMWBS of males and females refer that the measure of WEMWBS
is greater for males than females.
Hypotheses:
Null hypothesis (H0): The averages of age, alcohol consumption per capita and WEMWBS
scales are equal with respect to both male and female.
Alternative hypothesis (HA): The averages of age, alcohol consumption per capita and
WEMWBS scales are unequal with respect to both male and female.
The independent sample t-tests shows the p-values-
 For age by sex, t = (-1.7545) with 237.12 degrees of freedom creates the p-value =
0.08064. The p-value is greater than 5%. Hence, the null hypothesis is accepted with 95%
evidence. Therefore, the means of ages of males and females are equal.
 For alcohol consumption per week by sex, t = (1.3195) with 258.62 degrees of freedom
creates the p-value = 0.01847. The p-value is lesser than 5%. Hence, null hypothesis is
rejected with 95% evidence. Therefore, the means of alcohol consumption per week of
males and females are unequal (Heeren and D'Agostino 1987).

7BIOSTATISTICS
 For WEMWBS per week by sex, t = (2.3715) with 250.68 degrees of freedom creates the
p-value = 0.1882. The p-value is higher than 5%. Hence, null hypothesis is accepted with
95% evidence. Therefore, the means of WEMWBS score of males and females are equal.
Frequency Statistics:
Variable Levels Gender Frequencies Percentages p-value
“course” “scienceeng” Male 43 15.8% 0.00000264
6
Female 9 3.3%
“artssocsci” Male 15 5.5%
Female 32 11.7%
“lawbus” Male 43 15.8%
Female 38 13.9%
“medhealth” Male 60 22%
Female 33 12.1%
“drug” “user” Male 80 29.3% 0.0004843
Female 32 11.7%
“notuser” Male 81 29.7%
Female 80 29.3%
The percentage of males is greater than females for the course type “scienceeng”,
“lawbus” and “medhealth”. Conversely, the percentage of males is lesser than females for the
course type “artssocsci”. The number of drug users is greater for males and number of drug non-
user is almost equal for both males and females.

8BIOSTATISTICS
scienceeng artssocsci lawbus medhealth
sex
female
male
Frequency Distribution of course as per sex
Types of course
P e r c e n ta g e s
0 5 1 0 1 5 2 0 2 5 3 0
user notuser
sex
female
male
Frequency Distribution of drug as per sex
Types of drug
P ercentages
0 10 20 30 40 50
Hypotheses:
Null hypothesis (H0): The types of courses and types of drugs are independent to the gender
type.
Alternative hypothesis (HA): The types of courses and types of drugs are associated to the
gender type; that is they are dependent to the gender type.
The p-value obtained from Chi-squares test (Chi-square statistic = 28.655 with 3 degrees
of freedom) of two categorical variables “course” and “sex” is 0.000002646 which is less than
0.05. Therefore, the null hypothesis is rejected. It could be concluded that types of sex has no
effect on types of course.
The p-value obtained from Chi-square test (Chi-square statistic = 12.175 with 1 degrees
of freedom) of two categorical variables “drug” and “sex” is 0.0004843 which is less than 0.05.
Hence, the null hypothesis is rejected. It could be interpreted that types of sex has no effect on
types of drugs (user or non-user).

9BIOSTATISTICS
Question 2.
Predictive linear regression model:
35 40 45 50
0 20 40 60 80
Scatterplot
WEMWBS
Average alcohol consumption
sex
male
female
Dependent variable: Average alcohol consumption per week (alc).
Independent variable: The Warwick Edinburgh Mental Well-being Scale (WEMWBS) and
Sex.
lm(alc ~ WEMWBS +sex, data=alcohol)
Variables Values
Multiple R-squared 0.1099
Adjusted R-squared 0.1033
F-statistic 16.67
Degrees of freedom of F-statistics 2 and 270
p-value 0.0000001488
Residual standard error 11.38
Degrees of freedom of F-statistics 270

10BIOSTATISTICS
The value of multiple R2 refers that “WEMWBS” and “sex” explain 10.99% variability
of “Average alcohol consumption per week”. The p-value (0.000000148<0.05) indicates that the
overall model is significant.
Residuals:
Minimum -14.871
First Quartiles -6.073
Median -2.692
Third Quartiles 1.402
Maximum 73.94
Coefficients:
Estimate Std.Error t-value Pr(>|t|)
Intercept 52.2050 8.0280 6.503 3.8e-10***
WEMWBS -0.9462 0.1809 -5.230 3.4e-07***
Sex[T.females] -3.9996 1.4046 -2.847 0.00475**
Signif. Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ‘ 1
The linear regression model is-
Alc = 52.205 – 0.9462 *WEMWBS – 3.9996 *Sex (Neter et al. 1996)
Here, WEMWBS has negative relationship with alcohol consumption per week. Females
consumes lesser alcohol than males.
The p-values of both the explanatory variables show that the two variables “WEMWBS”
and “Sex” both are significant linear predictors of “Average alcohol consumption” at 5% level of
significance (Seber and Lee 2012). Although, the model is not fitted good.

11BIOSTATISTICS
35 40 45 50
0 2 0 4 0 6 0 8 0
WEMWBS
C o m p o n e n t + R e s i d u a l ( a l c )
male female
0 2 0 4 0 6 0
sex
C o m p o n e n t + R e s i d u a l ( a l c )
Component + Residual Plots
-3 -2 -1 0 1 2 3
0 2 4 6
Normal QQ-plot
t Quantiles
S ta n d a r d i z e d R e s i d u a l s 123
107
The normal QQ-plot refers that the fitting of the regression model is not good.

12BIOSTATISTICS
Conclusion:
Among all the qualitative and quantitative variables, sex types put an impact only on
alcohol consumption per week. All other variables are independent to the types of sex. Females
consume lesser cholesterol than males. WEMWBS and sex explains 10.99% variability of
alcohol consumption per capita.

13BIOSTATISTICS
References:
Davoren, M. P., Shiely, F., Byrne, M., & Perry, I. J. (2015). Hazardous alcohol consumption
among university students in Ireland: a cross-sectional study. BMJ open, 5(1), e006045.
Heeren, T. and D'Agostino, R., 1987. Robustness of the two independent samples t‐test when
applied to ordinal scaled data. Statistics in medicine, 6(1), pp.79-90.
Neter, J., Kutner, M.H., Nachtsheim, C.J. and Wasserman, W., 1996. Applied linear statistical
models (Vol. 4, p. 318). Chicago: Irwin.
Seber, G.A. and Lee, A.J., 2012. Linear regression analysis(Vol. 329). John Wiley & Sons.
Vandenbroucke, J. P., Von Elm, E., Altman, D. G., Gøtzsche, P. C., Mulrow, C. D., Pocock, S.
J., ... & Egger, M. (2007). Strengthening the Reporting of Observational Studies in
Epidemiology (STROBE): explanation and elaboration. Annals of internal medicine, 147(8), W-
163.
Von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., &
Strobe Initiative. (2007). The Strengthening the Reporting of Observational Studies in
Epidemiology (STROBE) statement: guidelines for reporting observational studies. Preventive
medicine, 45(4), 247-251.