Report on Health Statistic Assessment

Health Statistic Assessment
Analytical Report
Student Name:
Student Number:
Lecturer Name:
28th October 2017

Introduction
In this paper, I sought to analyse data on psychological survey. There are four research
questions that this study sought to answer. The four research questions are;
1. Is there an association between having a low (below 15) pre-flood psychological score
and living alone? If so, what this the nature of the association?
2. Are age, social support score and family functioning score predictors of the pre-flood
psychological score? Which of these three variables explains most of the variation in
pre-flood psychological score? How does the inclusion of place of residence as a
predictor change the fitted model? Using the minimum model, which contains only
the significant variables, what is the predicted pre-flood psychological score for a 35-
year old male living in a rural area with a social support score of 40 and a family
functioning score of 22?
3. Is there a difference in the post-flood psychological score between men according to
the level of impact of the 2011 flood? If there is a difference, which groups are
different?
4. Is the mean change in psychological score between the pre and post-flood survey the
same for men who experienced no or limited flood impact compared to men who
experienced moderate/major flood impact?

Results
Research question 1:
To answer this research question, I had to apply a Chi-Square test of association. The pre-
flood psychological score was given as a numerical variable and I had to recode where scores
below 15 were recoded as low and scores above 15 were recoded as high. I ended up with
two categorical variables making Chi-Square test an ideal test to test for the association.
Brief overview of the statistical methods you used
For this analysis, I used Chi-Square test of association. Also called Pearson's chi-square test
or the chi-square test of independence, is used to discover if there is a relationship between
two categorical variables. The null hypothesis for the test is that there is no association
between the variables.
Using SPSS I had to run the test and the results are displayed below;
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Exact Sig.
(2-sided)
Exact Sig.
(1-sided)
Pearson Chi-Square .100a 1 .752
Continuity
Correctionb
.000 1 1.000
Likelihood Ratio .099 1 .752
Fisher's Exact Test 1.000 .514
Linear-by-Linear
Association
.099 1 .753
N of Valid Cases 157
a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is 3.57.
b. Computed only for a 2x2 table
The p-value for the Pearson Chi-Square test is 0.752 (a value greater than 5% level of
significance). We therefore fail to reject the null hypothesis and conclude that there is no
significant association between having a low (below 15) pre-flood psychological score and living
alone.
Research question 2:

In this section, I aimed at finding out whether age, social support score and family function
score predict the pre-flood psychological score.
Brief overview of the statistical methods you used
For this analysis, I used multiple regression analysis. Regression analysis refers to a set of
statistical processes that are used to estimate the relationships among variables. The test
includes many techniques for modelling and analysing several variables, when the focus is on
the relationship between a dependent variable (also known as response variable) and one or
more independent variables (explanatory variables).
Modelling
Using regression model I sought to predict the following model;
y=β0 +β1 x1 + β2 x2 + β3 x3
Where;
y=Pre−flood psychological score
x1= Age
x2=Social support score
x3=family function score
Regression Coefficients-model 1
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
(Constant) 14.866 1.261 11.794 .000
Age in years -.018 .015 -.087 -1.210 .228
Social support
scale (pre flood)
.070 .018 .280 3.766 .000
Family functioning
scale (pre flood)
-.073 .036 -.149 -2.016 .045
R-Squared = 0.140

F(3, 170) = 9.221, p-value = 0.000
As can be seen in the regression analysis results table above, the value of R-Squared is 0.140;
this implies that 14% of the variation in the dependent variable is explained by the three
explanatory variables. It can also be seen that two of the three variables are significant in the
model. The two significant variables are Family functioning scale (pre flood) and Social
support scale (pre flood).
I also sought to find out which of the three variables explains most of the variation in pre-
flood psychological score. From the same regression results, it was found that the variable
that explains most of the variation in pre-flood psychological score is the Social support scale
(pre flood) since it had a larger value for the standardized coefficient.
Next I added place of residence as a predictor into the model to see how it affects the fitted
model.
Regression Coefficients-model 2
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
(Constant) 15.077 1.270 11.873 .000
Age in years -.016 .015 -.079 -1.090 .277
Social support
scale (pre flood)
.074 .019 .299 3.858 .000
Family functioning
scale (pre flood)
-.064 .038 -.132 -1.713 .089
Living alone? -.587 .594 -.075 -.990 .324
R-Squared = 0.145
F(4, 168) = 7.130, p-value = 0.000
By adding the variable place of residence into the model, the value of R-squared changed to
0.145; implying that 14.5% of the variation in the dependent variable is explained by the four

explanatory variables in the model; this shows a very small change. Also, the added variable
(place of residence), was found to be insignificant in the model. However, it should be noted
that addition of this variable renders the variable (Family functioning scale) insignificant in
the model.
Using the minimum model, which contains only the significant variables, the final regression
model is given as;
y=15.077+ 0.7 4 x1
Where,
y is the dependent variable (Psychological domain (pre flood)) while x1 is the significant
predictor variables which is the Social support scale (pre flood).
So the predicted pre-flood psychological score for a 35-year old male living in a rural area
with a social support score of 40 and a family functioning score of 22 is given as follows;
y=15.077+0.74∗( 40 ) =44.677
Hence the predicted pre-flood psychological score for the given values is 44.677.
Research question 3:
In this section, I sought to test whether there a difference in the post-flood psychological
score between men according to the level of impact of the 2011 flood.
Brief overview of the statistical methods you used
For this analysis, I used analysis of variance (ANOVA) test. ANOVA refers to a statistical
model that is used to analyse the differences among group means and their associated
procedures for variables with more than two factors. This research question involves one
dependent variable and an independent variable with three factors hence ANOVA test was
ideal for use.

ANOVA
Psychological domain (post flood)
Sum of
Squares
df Mean
Square
F Sig.
Between Groups 52.820 2 26.410 7.318 .001
Within Groups 407.796 113 3.609
Total 460.616 115
The p-value as can be seen from the above table is 0.001 (a value less than 5% level of
significance), we therefore reject the null hypothesis and conclude that there are differences
in the mean post-flood psychological score between men according to the level of impact of
the 2011 flood.
I conducted a post-hoc analysis using LSD to Bonferroni where we found out that the average
post-flood psychological scores was significantly higher in the no impact condition (M =
15.69, SD = 2.01) than in the moderate/major impact condition (M = 14.66, SD = 2.00), p
= .001. There was however no significant difference in the mean post-flood psychological
scores between the other groups.
Research question 4:
This section sought to answer the last research question. The question I sought to answer was
whether the mean change in psychological score between the pre and post-flood survey the
same for men who experienced no or limited flood impact compared to men who experienced
moderate/major flood impact. I used an independent t-test to answer this.
Brief overview of the statistical methods you used
For this analysis, I used an independent samples t-test. Also known as student's t-test, the test
refers to inferential statistical test that determines whether there is a statistically significant
difference between the means in two unrelated groups. This research question involves one

dependent variable and an independent variable with two unrelated (independent) factors
hence independent t-test test was ideal for use.
Mean change in psychological score between the pre and post-flood
Group Statistics
Impact of the floods for you
in terms of the property you
were living in
N Mean Std.
Deviation
Std. Error
Mean
No or limited flood impact 63 -.3993 2.39085 .30122
Moderate/major flood impact 52 .7770 1.57561 .21850
I performed an independent samples t-test to compare the mean change in psychological
score between men who experienced moderate/major flood impact and those who
experienced no or limited flood impact. Results showed that the mean change in
psychological score between the pre and post-flood survey was significantly different for men
who experienced no or limited flood impact compared to men who experienced
moderate/major flood impact (p-value = 0.03). Among the men who experienced no or
limited flood impact, the mean change in in psychological score between the pre and post-
flood survey was -0.3993 while the mean change in in psychological score between the pre
and post-flood survey for those who experienced moderate/major flood impact was 0.7770.
Conclusion
This study sought to investigate four research questions. Four different statistical tests were
employed to analyse the research questions. For the first research question, I used Chi-Square
test of association where I found out that there is no significant association between having a
low (below 15) pre-flood psychological score and living alone. For the second research question, I
multiple regression analysis. The third research question applied ANOVA test while the last part
employed independent t-test.

.
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