Analyzing a Heath Care Data Set - Essay Example

Analyzing a Heath Care Data Set

The dependant variable in the Yoga Stress (PSS)Study Data Set is the Psychological Stress Score. Both Pre-Intervention Psychological Stress Score and Post-Intervention Stress Score are Quantitative Interval Variables. Age is a Quantitative Independent variable in the Study dataset. Gender, Military, and Race are Nominal categorical variables in the Study dataset. Education is a Categorical Ordinal variable. Our variables of interest are the Pre-Intervention Psychological Stress Score and Post-Intervention Psychological Stress Score, from whom the effect of Intervention will be analyzed and interpreted.

Testing for outliers' availability in the variables using the SPSS (Analyse-Descriptive Statistics-Explore) Command, Age, and Pre- Intervention Psychological Stress Score have no outlier. Post-Intervention Psychological Stress Score has an outlier "36". This is summarised by the respective variable Box plots and descriptive summary in the attached SPSS .sav file.

The observed values and the Expected values of the Pre-Intervention Psychological Stress Score do not vary more from a straight line, as shown in the Pre-Intervention Psychological Stress Score Q-Q plots, suggesting that the assumption of Normality is met and hence Pre-Intervention Psychological Stress Score variable is normally distributed. The Sig. value under the Shapiro-Wilk test of normality column in the SPSS summary table 1 is 0.202, which is greater than 0.05; hence we can conclude that "Pre-Intervention Psychological Stress Score" is normally distributed and it has no outliers.

The observed values and the Expected values of the Post-Intervention Psychological Stress Score vary more from a straight line, as shown in the Post-Intervention Psychological Stress Score Q-Q plots, suggesting that the assumption of Normality is not met. The Sig. value under the Shapiro-Wilk test of normality column in the SPSS summary table 1 is 0.006, which is less than 0.05; hence we can conclude that "Post-Intervention Psychological Stress Score” is not normally distributed and it has an outlier.

The observed values and the Expected values of the Age do not vary more from a straight line, as shown in the Age Q-Q plots, suggesting that the assumption of Normality is met and hence Age variable is normally distributed. The Sig. value under the Shapiro-Wilk test of normality column in the SPSS summary table 1 is 0.605, which is greater than 0.05; hence we can conclude that "Age" is normally distributed, and it has no outliers.

Tests of Normality

Kolmogorov-Smirnova

Statistic

Shapiro-Wilk

df

AGE

.095

20

.200*

.963

20

.605

PRE_PSS

.131

20

.200*

.936

20

.202

POST_PSS

.209

20

.022

.855

20

.006

*. This is a lower bound of the true significance.

a. Lilliefors Significance Correction

SPSS Summary Table 1

Males had more Pre-Intervention Psychological Stress Scores and Post-Intervention Psychological Stress Score compared women, as shown in the side to side SPSS generated box plots. Native Americans had the highest Pre-Intervention Psychological Stress Score and post-Intervention Psychological Stress Score than the races, while Africans had the least Stress Scores. Graduates had the highest Pre-Intervention Psychological Stress Score and Post-Intervention Psychological Stress Score among the education levels while HS had the least. Active duty military had the highest Pre-Intervention Psychological Stress Score and Post-Intervention Psychological Stress Score.

Since Gender and Race satisfy all assumptions for the chi-square test for independence, they are all Categorical Nominal Independent Group; we conducted the test using SPSS to obtain the summary table 2.

Chi-Square Tests

Value

df

Asymptotic SignificanceSignificance (2-sided)

Pearson Chi-Square

3.667a

5

.598

Likelihood Ratio

4.132

5

.531

N of Valid Cases

20

a. 12 cells (100.0%) have expected count less than 5. The minimum expected count is .50.

Summary table 2

At a significance 0.05, we fail to reject the null hypothesis since p value=0.598>significance level 0.05; hence there is no enough evidence to support the claim that there is an association between Gender and Race in the Study dataset.

We found that protest scores followed a normal distribution from the previous normality test, while post-test scores did not. Therefore, we will conduct a Wilcoxon signed-rank test to assess whether the population means ranks differ for the paired groups. Since all the assumptions are satisfied, we perform the test using SPSS, following the attached SPSS Output file procedures.

Ranks

N

Mean Rank

Sum of Ranks

POST_PSS - PRE_PSS

Negative Ranks

Positive Ranks

Ties

Total

17a

1b

2c

20

9.65

7.00

164.00

7.00

a. POST_PSS < PRE_PSS

b. POST_PSS > PRE_PSS

c. POST_PSS = PRE_PSS

Test Statistics

POST_PSS - PRE_PSS

Z

-3.425b

Asymp. Sig. (2-tailed)

.001

a. Wilcoxon Signed Ranks Test

b. Based on positive ranks.

The test statistic is z=-3.425.This means that 1 patients had increases Stress score after intervebtion,2 patients had no change after intervention and 17 patients had reduced Stress score after intervention.

The critical value at the two-tailed test at 0.05 level of SignificanceSignificance and n=20 will be equivalent to 52. Since 1, we reject the null hypothesis. There is enough evidence at α=0.05 to support the claim that Phycological Stress Score decreased after Intervention.

The procedures used are in the attached .sav output file 2

.sav assessment 4 output file

Demographic table descriptive statistics are significant in identifying which group requires more attention to mental health. There is more attention needed in addressing stress in the African American race than other races. It is significant to determine which specific area requires more research and identify the most vulnerable groups to stress-related disorders. Considering these age groups, people under 36 years are more susceptible. They help to formulate the solutions of the problems based on the age groups where. When more young people are affected, you look at issues such as employment being one of the root problems, so solving it will require reducing unemployment.

The Chi-square test is highly sensitive to the sample size; as sample size increases, the absolute difference becomes a smaller and smaller proportion of the expected value. When the expected frequency in a table cell is less than 5, chi-square can lead to biased conclusions.