Self-Study: Comparing Counts (Chi Square)
Throughout the course, there will be a self-study Discussion pertaining to an important concept or topic covered within the assigned week. These Discussions are designed to give you the opportunity to collaborate with your peers and faculty, test your knowledge, ask questions, practice research analysis, and assist your peers.
You are not required to post to this forum; however, you are encouraged to post, review the posts of others, as well as answer questions and/or provide clarity and collaboration with your peers. Discussions will be graded as either Complete or Incomplete.
Resources
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
WEEKLY RESOURCES
Required Resources
· Dang, D., Dearholt, S. L., Bissett, K., Ascenzi, J., & Whalen, M. (2021). Johns Hopkins evidence-based practice for nurses and healthcare professionals: Model & guidelines (4th ed.). Sigma Theta Tau International Honor Society of Nursing.
· Chapter 6, “Evidence of Appraisal: Research” (pp. 254–267)
· Salkind, N., & Frey, B. (2019). Statistics for people who (think they) hate statistics (7th ed.). SAGE Publications.
· Chapter 5, “Creating Graphs: A Picture Really Is Worth a Thousand Words” (pp. 110–115)
· Chapter 18, “Chi-Square and Some Other Nonparametric Tests: What to Do When You’re Not Normal” (pp. 337–342, 345–346)
·
Document:
Creating Charts in Excel (Excel)
Download Creating Charts in Excel (Excel)
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Document:
Critical Assessment, Appendix E: Research Evidence Appraisal Tool (Word document)
Download Critical Assessment, Appendix E: Research Evidence Appraisal Tool (Word document)
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Document:
Critical Assessment, Appendix G: Individual Evidence Summary Tool (Word document)
Download Critical Assessment, Appendix G: Individual Evidence Summary Tool (Word document)
Required Media
· Niedz, B. (2024). Inferential comparison of counts (chi square) [Video]. Walden University Canvas.
Optional Resources
· Conner, C. (2018).
Evaluating the impact of an early warning scoring system in a community hospital settingLinks to an external site.
(Publication No. 10747387) [Doctoral dissertation, Walden University]. ProQuest Dissertations and Theses Global.
· Moradkhani, B., Mollazadeh, S., Niloofar, P., Bashiri, A., & Oghazian, M. B. (2021).
Association between medication adherence and health-related quality of life in patients with chronic obstructive pulmonary diseaseLinks to an external site..
Journal of Pharmaceutical Health Care and Sciences, 7(1).
· Owusu, M. (2023).
Language barrier: An unmet challenge for low screening of colorectal cancer among Hispanic Americans in TexasLinks to an external site.
(Publication No. 30567908) [Doctoral dissertation, Walden University]. ProQuest Dissertations and Theses Global.
To prepare:
· Read and view the Learning Resources.
Use this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum.
Post answers to the following:
· Find the Chi Sq in Moradkhani et al. (2021), Conner (2018), or Owusu (2023) from the Optional Resources, and critique its use in the study.
· Tania
· In the dissertation by
Conner (2018) titled
Evaluating the Impact of an Early Warning Scoring System in a Community Hospital Setting, the Chi-square (χ²) test was used to examine whether implementing an Early Warning Scoring (EWS) system influenced the frequency of rapid response team (RRT) activations, intensive care unit (ICU) transfers, and code blue events. Specifically, the test compared pre- and post-intervention frequencies to assess the EWS system’s effectiveness.
· Conner reported a statistically significant decrease in code blue events post-intervention, with a Chi-square value of χ² = 4.60, p = 0.032. The use of the Chi-square test was appropriate, given that the variables in question (e.g., occurrence of code blue events) were categorical. This statistical approach is useful in determining whether distributions of categorical variables differ from one another across groups (Sedgwick, 2019). The results suggest that the EWS system may have had a beneficial impact on identifying patient deterioration earlier and reducing critical incidents.
· However, while the statistical test was suitable, several limitations should be addressed. Most notably, effect size measures, such as Phi coefficient or Cramér’s V, were not reported. Without these, it’s difficult to determine the practical or clinical relevance of the findings. A statistically significant result (p < .05) does not automatically imply a strong or meaningful association (Lakens, 2021).
· Additionally, Conner did not clearly discuss whether the assumptions of the Chi-square test—particularly the requirement for expected frequencies of at least 5 per cell—were met. Failure to meet these assumptions can reduce the validity of the results. Also, the Chi-square test does not account for confounding factors such as staffing levels, patient acuity, or seasonal trends. A multivariate analysis, such as logistic regression, could have helped control for these potential confounders and provided more robust conclusions (Sharma et al., 2020).
· Despite these caveats, Conner’s use of the Chi-square test effectively illustrated key patterns and associations between EWS implementation and critical event reduction. To fully inform evidence-based practice, future studies should incorporate effect size reporting, assumption verification, and complementary statistical analyses to better interpret both statistical and clinical significance.
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References
· Conner, C. (2018).
Evaluating the impact of an early warning scoring system in a community hospital setting. (Publication No. 10747387) [Doctoral dissertation, Walden University]. ProQuest Dissertations and Theses Global.
Links to an external site.
· Lakens, D. (2021). Sample size justification. Collabra:
Psychology, 7(1), 1–16.
to an external site.
· Sedgwick, P. (2019). Understanding the chi-squared test.
BMJ, 364, l1296.
to an external site.
· Sharma, A., Minh Duc, N. T., Luu Lam Thang, T., Nam, N. H., Ng, S. J., Abbas, K. S., … & Huy, N. T. (2020). A consensus-based checklist for reporting of survey studies (CROSS).
Journal of General Internal Medicine, 36(10), 3179–3187.
to an external site.
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