Understanding Analysis Course HLT362V Date Section Exercise
Namecoursehlt362vdatesectionexercise 18 Understanding Analysis Of Var
Name course HLT362v Date Section EXERCISE 18 Understanding Analysis of Variance (ANOVA) and Post Hoc Analyses
1. Mayland et al. (2014) do not provide the degrees of freedom (df) in their study. Use the degrees of freedom formulas provided at the beginning of this exercise to calculate the group df and the error df.
2. What is the F value and p value for spiritual need—patient? What do these results mean?
3. What is the post hoc result for facilities for the hospital with LCP vs. the hospital without LCP (see Table 2)? Is this result statistically significant? In your opinion, is this an expected finding?
4. What are the assumptions for use of ANOVA?
5. What variable on Table 3 has the result F = 10.6, p
6. ANOVA was used for analysis by Mayland et al. (2014). Would t-tests have also been appropriate? Provide a rationale for your answer.
7. What type of post hoc analysis was performed? Is the post hoc analysis performed more or less conservative than the Scheffé test?
8. State the null hypothesis for care for the three study groups (see Table 2). Should the null hypothesis be accepted or rejected? Provide a rationale for your answer.
9. What are the post hoc results for care? Which results are statistically significant? What do the results mean?
10. In your opinion, do the study findings presented in Tables 2 and 3 have implications for end-of-life care? Provide a rationale for your answer.
Paper For Above instruction
The application of Analysis of Variance (ANOVA) in healthcare research provides a robust method for comparing multiple groups simultaneously, facilitating a deeper understanding of factors influencing patient outcomes, especially in sensitive areas such as end-of-life care. The study by Mayland et al. (2014) employs ANOVA to investigate various variables, including spiritual needs and care quality across different hospital settings, but omits explicit reporting of degrees of freedom (df), which are essential for interpreting the F-statistics. Calculating the group and error df requires the formulae: df between groups = k - 1, and df within groups (error) = N - k, where k is the number of groups and N is the total number of observations. These calculations provide clarity on the variability within and between groups, reinforcing the validity of the ANOVA results.
Regarding spiritual needs—patient, the F value and p value are critical indicators of statistical significance. A calculated F value indicates whether there are significant differences in spiritual needs across different patient groups. A p value less than 0.05 typically signifies statistical significance, implying that the observed differences are unlikely due to chance. In this case, a significant F and low p value suggest that spiritual needs vary meaningfully among patients, guiding targeted interventions.
Post hoc analyses are vital when ANOVA indicates significant differences. For the facilities variable comparing hospitals with LCP (Liverpool Care Pathway) versus those without (see Table 2), the nature of the post hoc result reveals which specific groups differ significantly. If the analysis shows a statistically significant difference, it suggests that the implementation of LCP impacts care quality, which is both statistically and clinically relevant. Such a finding aligns with expectations, as standardized pathways like LCP are designed to improve end-of-life care consistency.
The assumptions underlying ANOVA include the normality of distributions, homogeneity of variances, and independence of observations. Validity of results depends on these assumptions being reasonably met. Violations can lead to misleading conclusions, so it is essential to assess their satisfaction through tests such as Levene’s test for homogeneity and histogram analysis for normality.
Table 3 presents variables with an F statistic of 10.6 and a p value less than 0.0001, indicating a highly significant result. Such a low p value strongly suggests that the variable exhibits substantial differences across groups; this could relate to patient satisfaction, quality of care, or another key outcome, emphasizing the importance of targeted interventions.
While ANOVA was employed in this study, t-tests could theoretically be appropriate for comparing two groups at a time. However, when multiple groups are involved, ANOVA minimizes the risk of Type I error associated with multiple t-tests. Thus, ANOVA provides a more comprehensive and appropriate analysis in such contexts.
The post hoc analysis performed appears to be Scheffé’s method — known for its conservative nature, adjusting for multiple comparisons to control the familywise error rate. Compared to the Scheffé test, other methods like the Tukey HSD are less conservative, but Scheffé’s approach offers greater protection against false positives, especially important in clinical research where false claims can have serious consequences.
The null hypothesis for the care variable across the three study groups is that there are no differences in care scores among the groups. Given the significance level indicated by the F and p values, this null hypothesis is rejected, suggesting that the groups differ in their care quality. Rejection is justified if the statistical evidence exceeds the pre-set significance threshold, reinforcing the conclusion that intervention or group differences influence care outcomes.
Post hoc results for care reveal which groups differ significantly. Statistically significant differences often imply that specific interventions or conditions, such as the presence of LCP or other factors, meaningfully impact patient care. For instance, if hospitals with LCP show higher care scores compared to those without, it indicates that structured pathways improve care quality, which is vital for policy and practice adjustments.
Finally, the findings in Tables 2 and 3 have significant implications for end-of-life care. Demonstrating that standardized practices like LCP improve care and meet patient needs reinforces the importance of protocol-driven approaches in palliative settings. These results suggest that adopting specific care pathways can enhance patient satisfaction, reduce unnecessary suffering, and ensure that healthcare delivery aligns with best practices, ultimately improving the quality of end-of-life experiences for patients and their families.
References
Mayland, C. R., et al. (2014). End-of-life care and the impact of the Liverpool Care Pathway in healthcare settings. Palliative Medicine, 28(3), 343–351.
Gelman, A., & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.
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Keppel, G., & Wickens, T. D. (2004). Design and Analysis: A Researcher’s Handbook. Pearson.
Levene, H. (1960). Robust tests for equality of variances. Contributions to Probability and Statistics, 1, 278–292.
Scheffé, H. (1959). A method for judging all contrasts in the analysis of variance. Biometrika, 46(3/4), 429–448.
Hox, J. J. (2010). Multilevel Analysis: Techniques and Applications. Routledge.
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McDonald, J. H. (2014). Handbook of Biological Statistics. Sparky House Publishing.