Statistical Analysis Of Sleep Problems, Alcohol Consumption Essay.

Part a

The independent sample t-test is used to test the differences in “how many alcohol drinks per day” for the persons “trouble falling asleep.” A t-test is used to compare the differences in the dependent variable (how many alcohol drinks per day) for the two independent groups (trouble falling asleep).

Part b

In order to test the differences we test the hypothesis that there are no significant differences in trouble falling asleep.

Null Hypothesis: There are no differences in trouble falling asleep between people taking alcoholic drinks.  

Alternate Hypothesis: There are differences in trouble falling asleep between people taking alcoholic drinks.   

 
From the t-test we find that t(253) = .088, p-value = .930 more than a = .05, level of significance. Thus we reject the Null Hypothesis. Hence there are statistically no significant differences in “alcoholic drinks per day” and persons having “trouble falling asleep.”

Part a 

 
24.9% of total persons are females who have sleep problems. Similarly 18.6% of the total people are males who have sleep problems.  

Part b

The total number of people surveyed is 269 out of which 152 persons have no problem with sleep. Out of the 152 people 81 are females and 71 are males who have no problems with sleep.

Part c 

 
In order to analyse the difference in problem with sleep across gender the Chi-square test is used.

The test shows that c² (1, N = 269) = .422, sig = .516. Since the p-value is more than .05, level of significance, hence there is no significant differences in problem with sleep across sex.

Part a

In order to recode the syntax used is :

Transform > Recode into different variable

In the Old value - range, Lowest through value "6" was written and New value "1" was written. Similarly, in the Old value - range, value through Highest "7" was written and New value "2" was written. 

Next in the variable view the value labels were changed as

1 – “with health problems”

2 – “In good health”

Part b

In order to analyse if there are difference in general health of people with problem with sleep the Chi-square test for association was used. 

 
The likelihood ratio tests shows that there are significant differences in problem with sleep and people with health problems and people with good health, c² (1,266) = 4.877, p-value = 0.027.

Results and Interpretation

Part c

The Chi-square tests shows that there are statistically significant differences in problem with sleep between persons “with health problems” and “in good health” c² (1,266) = 4.909, p-value = 0.027, less than .05 level of significance.

Part d 

 The effect size for the test is 0.136 (phi). Thus the differences in the groups is small.  

Part e

There are statistically significant differences in problem with sleep between persons “with health problems” and “in good health.” 

 
In order to investigate the differences in the sleep and association scale in the three age groups the non-parametric test Kruskal –Wallis test was used.

The Kruskal Wallis test shows that there are statistically no significant differences between the groups, c² (2) = 2.162, p = .339.   

To investigate the differences between the groups one-way ANOVA (between groups with planned comparisons) was used. Further Post Hoc tests were done using the method of Tukey HSD. The groups which were tested for differences were Hospice, Hospital with LCP and Hospital without LCP.   

The Null Hypothesis: There is no difference in mean level of communication between the three groups. 

The Alternate Hypothesis: There is difference in mean level of communication between the three groups. 

The Null Hypothesis should be rejected since p-value < .0001, less than .05, level of significance. Thus, there was statistically significant differences between the three groups at p = .05 level, F(2,255) = 16.6, p < .0001.

The results of one-way ANOVA showed that there were statistically significant differences in the average scores of the three groups for the four composite scales. In order to check for differences in the groups and compare two groups individually the post-hoc analysis was done.

The Post-Hoc analysis shows that there are statistically significant differences between Hospice group and Hospice and LCP group, p-value = .002, less than .05, level of significance. Similarly the Pos-Hoc analysis shows that there are statistically significant differences between Hospice and Hospital without LCP, p-value <.0001, less than .05, level of significance.

On the other hand there are no statistical significant differences between Hospital with LCP and without LCP group, p-value = .86, more than .05, level of significance.

The results show that there are statistically significant differences in communication to dying patients and his families between hospitals with Hospice and Hospitals with LCP. Similarly there are statistically significant differences in communication to dying patients and his families between hospitals with Hospice and Hospitals without LCP. On the other hand there are statistically no significant differences in communication between Hospitals with and without LCP. The communication behaviour of Hospitals were assessed using ECHO-D questionnaire.     

Conclusion

The one-way ANOVA for the spiritual need – next of kin has F(2,255) = 22.6. The results show that there are statistically significant differences between the three hospital groups since p-value for the test < .0001, less than a = .05, level of  significance.

The Post-Hoc analysis shows that there are statistically significant differences between Hospice group and Hospice and LCP group, p-value = .006, less than .05, level of significance.

Similarly the Pos-Hoc analysis shows that there are statistically significant differences between Hospice and Hospital without LCP, p-value = .0001, less than .05, level of significance.

Similarly the Pos-Hoc analysis shows that there are statistically significant differences between Hospital with and without LCP, p-value = .0002, less than .05, level of significance.

The results show that there are statistically significant differences in the spiritual need – next of kin between all the three groups. The spiritual need of the bereaved family was assessed using ECHO-D questionnaire.   

Palliative or end-of-life care is provided to patients who are in their final hours of death. Such a patient would be unable to provide information that has been accessed by Maryland et al. (2014). The evaluation of the support that has been provided to a patient on end-of life can best be provided by the family of the patients. Even though it is a difficult time for the next-of-kin for such a family but the information for research that Maryland et al. (2014) did can only be provided by the patient family. 

One-way ANOVA was used to assess the differences in groups of composite scales and variables. Since Maryland et al. (2014) considered three groups of hospitals – Hospice, hospitals with and without LCP hence one-way ANOVA is a very appropriate test for testing all the three groups at the same time. A t-test on the other hand is used to analyse the differences between two groups only.

A chi-square test was done between RAAPS users and non-users and race. The chi-square for race was, c² (2,N = 210) =  1.2865, p = .53.

Since, the p-value (.53) for Chi-square is more than a = .05, level of significance, hence there is no significant association between race and RAAPS users and non-users.

A chi-square test was done and statistically significant differences were found between % of adolescent patients and RAAPS users and non-users, c² (1, N = 154) = 7.3780, p = .01.  

The Null hypothesis for the question is: There is no association between RAAPS users and non-users for years in practice.

The Alternate hypothesis: There is an association between RAAPS users and non-users for years in practice.

From the Chi-square test it can be said that there is no association between RAAPS users and non-users for the years in practice, c² (1, N = 157) = 6.2587, p = .01. Thus, we accept the Null Hypothesis, since p-value .01 is less a = .05, level of significance.

Table 2 of the paper by Darling-Fisher et al. (2014) presents the comparisons of demography of the patients between RAAPS users and non-users.   7 demographic characteristics are tested for differences in RAAPS assessment. For 5 of the demographic profiles the p-value is less than a = .05, level of significance. Thus for 5 of the demographic profiles – provider type, practice setting, % adolescent patients, years in practice and US practice region there are statistically significant differences between RAAPS users and non-users. For the demographic profile race and provider age in years there is statistically no significant differences between RAAPS users and non-users.