The research questions you are required to answer are:
1. Is there an association between having a low (below 15) preflood 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 preflood psychological score Which of these three variables explains most of the variation in preflood 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 preflood psychological score for a 35year 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 postflood 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 postflood survey the same for men who experienced no or limited flood impact compared to men who experienced moderate/major flood impact.
Answer:
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;
 Is there an association between having a low (below 15) preflood psychological score and living alone? If so, what this the nature of the association?
 Are age, social support score and family functioning score predictors of the preflood psychological score? Which of these three variables explains most of the variation in preflood 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 preflood psychological score for a 35year old male living in a rural area with a social support score of 40 and a family functioning score of 22?
 Is there a difference in the postflood psychological score between men according to the level of impact of the 2011 flood? If there is a difference, which groups are different?
 Is the mean change in psychological score between the pre and postflood survey the same for men who experienced no or limited flood impact compared to men who experienced moderate/ma
jor flood impact?
Results
Research question 1:
To answer this research question, I had to apply a ChiSquare test of association. The preflood 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 ChiSquare test an ideal test to test for the association.
Brief overview of the statistical methods you used
For this analysis, I used ChiSquare test of association. Also called Pearson's chisquare test or the chisquare 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;
ChiSquare Tests 


Value 
df 
Asymp. Sig. (2sided) 
Pearson ChiSquare 
.100^{a} 
1 
.752 
Continuity Correction^{b} 
.000 
1 
1.000 
Likelihood Ratio 
.099 
1 
.752 
LinearbyLinear 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 pvalue for the Pearson ChiSquare 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) preflood psychological score and living alone.
In this section, I aimed at finding out whether age, social support score and family function score predict the preflood 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).
Regression Coefficientsmodel 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 
RSquared = 0.140 F(3, 170) = 9.221, pvalue = 0.000 
As can be seen in the regression analysis results table above, the value of RSquared 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 preflood psychological score. From the same regression results, it was found that the variable that explains most of the variation in preflood 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 Coefficientsmodel 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 
RSquared = 0.145 F(4, 168) = 7.130, pvalue = 0.000 
By adding the variable place of residence into the model, the value of Rsquared 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;
Where,
is the dependent variable (Psychological domain (pre flood)) while is the significant predictor variables which is the Social support scale (pre flood).
So the predicted preflood psychological score for a 35year 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;
Hence the predicted preflood psychological score for the given values is 44.677.
In this section, I sought to test whether there a difference in the postflood 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 
Between Groups 
52.820 
2 
26.410 
Within Groups 
407.796 
113 
3.609 
Total 
460.616 
115 

The pvalue 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 postflood psychological score between men according to the level of impact of the 2011 flood.
I conducted a posthoc analysis using LSD to Bonferroni where we found out that the average postflood 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 postflood psychological scores between the other groups.
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 postflood 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 ttest to answer this.
Brief overview of the statistical methods you used
For this analysis, I used an independent samples ttest. Also known as student's ttest, 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 ttest test was ideal for use.
Mean change in psychological score between the pre and postflood
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 ttest 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 postflood survey was significantly different for men who experienced no or limited flood impact compared to men who experienced moderate/major flood impact (pvalue = 0.03). Among the men who experienced no or limited flood impact, the mean change in in psychological score between the pre and postflood survey was 0.3993 while the mean change in in psychological score between the pre and postflood 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 ChiSquare test of association where I found out that there is no significant association between having a low (below 15) preflood 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 ttest.
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