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WK 4 RESP. DATA

Self-Study: Comparison of Means, Part 1

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. 155–158) 

· Appendix E, “Research Evidence Appraisal Tool” (pp. 298–302)

· Appendix F, “Nonresearch Evidence Appraisal Tool” (pp. 307–314)

· Appendix G, “Individual Evidence Table” (pp. 315–318) 

· Salkind, N., & Frey, B. (2019). 
Statistics for people who (think they) hate statistics (7th ed.). SAGE Publications.

· Chapter 12, “t(ea) for Two: Tests Between the Means of Different Groups” (pp. 236–239, 243)

· Chapter 13, “t(ea) for Two [Again]: Tests Between the Means of Related Groups” (pp. 253–256, 259)

Required Media

· Niedz, B. (2024). 
Comparison of means, part 1 [Video]. Walden University Canvas.

PowerPoint Presentation

Required Resources for Topic: 
t Test

· Cook, H. E., Garris, L. A., Gulum, A. H., & Steber, C. J. (2024). 
Impact of SMART goals on diabetes management in a pharmacist-led telehealth clinicLinks to an external site.. 
Journal of Pharmacy Practice, 37(1), 54–59.

Required Resources for Topic: Paired 
t Test

· DeFusco, C., Lewis, A., & Cohn, T. (2023). 
Improving critical care nurses perceived self-efficacy in providing palliative care: A quasi-experimental studyLinks to an external site.. 
American Journal of Hospice and Palliative Medicine, 40(2), 117–121.

· Markiewicz, A., Hickman, R. L., McAndrew, N. S., & Reimer, A. (2023). 
Enhancing palliative communication in the intensive care unit through simulation: A quality improvement projectLinks to an external site.. 
Clinical Simulation in Nursing, 77, 1–5.

To prepare:

· Read and view the Learning Resources in Doherty & Skalsky and in Dang et al. (2021) in Required Readings.

· View the video on comparison of means.

Use this Discussion to collaborate with your peers and faculty as an open office hours/ Q&A forum. 

Post answers to the following:

· Interpret independent sample 
t-test results and explain the relevance to the research question (Cook et al., 2024).

· Compare and contrast Cook et al. (independent samples 
t test) to DeFusco et al. (2023) or Markiewicz et al. (2023) (paired 
t test) results.

· How did the subjects differ in the two studies?

· Differentiate between clinical value and statistical significance. 

·

For this Self-Study Discussion, you may post throughout Week 4. 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.

Post answers to any or all of the following:

· Interpret independent sample t-test results and explain the relevance to the research question (Cook et al., 2024).

· Compare and contrast Cook et al. (independent samples t-test) to DeFusco et al. (2023) or Markiewicz et al. (2023) (paired t-test) results.

· How did the subjects differ in the two studies?

· Differentiate between clinical value and statistical significance.

Our interactive discussion addresses the following learning objectives:

· Interpret comparison of mean (
t-test) data in a published study

· Differentiate between dependent observations and independent observations

RESPOND TO THIS DISCUSSION POST

·
                                                                                    Tamekia

·
 

·                 The independent samples t-test conducted by Cook et al. (2024) examined the impact of SMART goals on diabetes management by comparing A1C outcomes between two distinct groups: patients who engaged in SMART goal-setting and those who did not within a pharmacist-led telehealth clinic. Although the SMART goal group demonstrated a greater reduction in A1C levels than the control group, the results were not statistically significant (p = .287), indicating that the null hypothesis—stating no difference between groups—could not be rejected (Cook et al., 2024).

· —stating no difference between groups—could not be rejected (Cook et al., 2024). Despite this, the study suggested that the intervention held clinical significance, as even modest A1C improvements can lead to better long-term management of diabetes (Cook et al., 2024). This case also highlights the potential for a type II error due to the small sample size (n = 100), which may have lacked the power to detect a statistically significant difference (Salkind & Frey, 2019).

·                 In contrast, DeFusco et al. (2023) employed a paired t-test to measure critical care nurses’ self-efficacy in providing palliative care before and after an educational intervention. Because the same group of nurses was assessed at two points (pre- and post-intervention), the paired t-test was appropriate for analyzing the dependent samples (DeFusco et al., 2023). Their study revealed statistically significant improvements (p < .001) in nurses’ perceived self-efficacy, specifically in the psychosocial and symptom management subscales (DeFusco et al., 2023). Unlike Cook et al. (2024), who focused on patient A1C outcomes between two independent patient groups, DeFusco et al. (2023) examined the same subjects’ professional

· confidence over time, making the design more sensitive to detecting changes. This comparison illustrates how independent versus paired t-tests are chosen based on whether groups are independent or related (Salkind & Frey, 2019).

·                  Distinguishing between clinical and statistical significance is essential for interpreting research findings effectively. Cook et al. (2024) presented clinically meaningful outcomes by demonstrating A1C reductions, despite not reaching statistical significance, which is common in studies with limited sample sizes (Salkind & Frey, 2019). Conversely, DeFusco et al. (2023) achieved both clinical and statistical significance, reinforcing the strength of their intervention. While statistical significance (typically p < .05) assesses whether findings are likely due to chance, clinical significance evaluates the practical impact of those findings on patient care or clinical practice (Dang et al., 2021). For DNP-prepared nurses, understanding both aspects is vital when appraising evidence and implementing interventions in practice.

·

·
 References

· Cook, H. E., Garris, L. A., Gulum, A. -H., & Steber, C. J. (2024). Impact of SMART goals on diabetes management in a pharmacist-led telehealth clinic. 
Journal of Pharmacy Practice, 37(1), 54–59.

· DeFusco, C., Lewis, A., & Cohn, T. (2023). Improving critical care nurses perceived self-efficacy in providing palliative care: A quasi-experimental study. 
American Journal of Hospice and Palliative Medicine, 40(2), 117–121.

· 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.

· Salkind, N. J., & Frey, B. B. (2019). 
Statistics for people who (think they) hate

·
statistics (7th ed.). SAGE Publications.

·  

·

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