Hmgt 495 Week 5 General Rules For Discussions At CU

Hmgt 495 Week 5 General Rules For Discussions Comply With Customary

HMGT 495 Week 5 · General Rules for Discussions · Comply with customary rules of online etiquette · Do not repeat thoughts already posted by a classmate or your faculty member. Repetitive comments may be deleted and not count toward your participation grade. · Do not get off topic · Relate your posts to the course content · No word count maximum or minimum, brevity is appreciated · Do not upload a document in a discussion thread · If referring to a link, please embed it in your post, don’t expect your classmates or faculty to copy/paste the link to see it.

Topic: Question 2) Why are interpersonal skills important in health care management?

Week 5 Discussion HMGT 400 · Specific Rules for Discussions 1. Each post must be at least 130 words long or it receives no credit. Only the first initial postings and the first response postings meeting the 130-word requirement will be graded. 2. The initial posting must be submitted by Thursday night at 11:59 pm EST. The response’s postings are due Sunday night at 11:59 pm. 3. First Initial & response’s postings must include at least two references – one internal (course readings, course modules, primers, webliography, etc.) and one external (other authoritative sources beyond our course material). Note: No wiki or blog references.

Discussion 5: Research Methods and Techniques, Part 2 Previous Next Show Description Hide Description Discussion 5: PART A: Research Methods and Techniques, Part 2 In last week we discussed research techniques, this week discussion focuses on the other aspects of research, please pick up one of the following areas and discuss: 1) Different types of bias in research and discuss. 2) Experimental and Non-Experimental Research 3) Distinguishing between a Sample and a Population and then compare at least two sampling methods (e.g., random sampling vs. stratified random sampling or convenience and quota sampling methods). 4) What specific steps would you have taken to obtain a representative sample?

PART B: Week-5: Exercise Exercise: Determining the sample size and ANOVA 1) Let us consider the question of how large the sample size should be to obtain an estimate of a population proportion at a specified level of precision. In a survey, the planning value for the population proportion p is 0.35. How large a sample should be taken to provide a 95% confidence interval with a margin of error of 0.05 (Click here to see the Formula). 2) Use the following data and calculate F-test using ANOVA one-way in Excel (Click on ANOVA to see an example in an Excel file) and attach your response or Excel file with your post. You can copy this or download TABLE from here. See more details here LINK) Table 2: Data for height for four groups of children

Paper For Above instruction

This discussion encompasses several critical concepts in healthcare management, focusing on interpersonal skills, research methodologies, and statistical techniques essential for effective decision-making and improved patient outcomes. The importance of interpersonal skills in healthcare management cannot be overstated, as they facilitate effective communication, teamwork, and patient-centered care. These skills include empathy, active listening, cultural competence, and conflict resolution, which are vital in building trust and ensuring collaboration among healthcare professionals and between providers and patients (Kearney-Nunnery, 2019). Strong interpersonal skills contribute to better patient satisfaction, adherence to treatment plans, and overall healthcare quality. Moreover, these skills enhance leadership and organizational effectiveness by fostering a positive work environment and promoting teamwork (Hojat, 2018). Consequently, healthcare managers who develop and nurture interpersonal skills can lead more cohesive teams, improve patient care outcomes, and address complex healthcare challenges effectively from a management perspective.

Turning to the research aspect, understanding research methodologies such as biases, sampling techniques, and statistical analysis like ANOVA is fundamental in healthcare research to ensure validity, reliability, and applicability of findings. Different types of bias — including selection bias, measurement bias, and confounding variables — can distort research results if not properly addressed (Creswell & Creswell, 2018). Recognizing and mitigating these biases ensure that research findings truly reflect reality, which is crucial when making policy or clinical decisions.

Experimental and non-experimental research designs serve different purposes in healthcare research. Experimental research, particularly randomized controlled trials (RCTs), allows for establishing causality by controlling variables, making it highly valuable in testing new treatments or interventions (Friedman, Furberg, & DeMets, 2019). Conversely, non-experimental designs, such as observational studies, provide insights where experimental manipulation is impractical or unethical, aiding in understanding disease patterns and healthcare utilization without intervention (Creswell & Creswell, 2018). Both are vital, depending on the research question at hand.

Understanding the distinction between a sample and a population is critical. A population includes all members of a defined group, such as all patients with a specific condition in a country, whereas a sample is a subset representing that population. Sampling methods like simple random sampling ensure each member has an equal chance of selection, reducing bias, whereas stratified sampling involves dividing the population into homogeneous subgroups to improve precision (Patten & Newhart, 2017). Choosing appropriate sampling methods is essential for obtaining a representative sample that accurately reflects the population, which is fundamental in generalizing study results.

To obtain a representative sample, specific steps include defining the population clearly, selecting appropriate sampling techniques, calculating the necessary sample size to ensure statistical power, and employing randomization to prevent bias. Utilizing stratified random sampling, for example, ensures that key subgroups are proportionally represented, thereby improving the generalizability of findings (Creswell & Creswell, 2018). Adequate pilot testing, rigorous data collection protocols, and ethical considerations further ensure data accuracy and reliability.

Regarding sample size determination, the formula for estimating a population proportion with a specified margin of error involves statistical calculations based on desired confidence levels. For p=0.35, to achieve a 95% confidence interval with a ±0.05 margin of error, the required sample size is approximately 350, calculated using standard sample size formulas (Subash et al., 2018). This ensures the estimate is precise enough for practical decision-making.

Finally, the application of ANOVA (Analysis of Variance) in healthcare research allows for comparing multiple group means simultaneously. Using Excel, one can perform a one-way ANOVA to analyze data such as height measurements across different age groups of children under five. Proper execution of this statistical method involves computing the F-statistic, which indicates whether there are significant differences among the groups. Accurate results depend on correct data entry, setting assumptions, and interpreting the output (Norusis, 2018).

References

• Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.
• Friedman, L. M., Furberg, C., & DeMets, D. L. (2019). Fundamentals of clinical trials. Springer.
• Hojat, M. (2018). Empathy in health profession education and patient care. Springer.
• Kearney-Nunnery, R. (2019). Nursing leadership and management. F.A. Davis Company.
• Norusis, M. J. (2018). SPSS statistics guide for research design and analysis. Pearson.
• Patten, M. L., & Newhart, M. (2017). Understanding research methods: An overview of the basics. Routledge.
• Subash, S., Baskaran, R., & Rajendran, V. (2018). Sample size calculation for estimating a population proportion. International Journal of Medical Science and Public Health, 7(6), 439-442.
• Hojat, M. (2018). Empathy in health profession education and patient care. Springer.
• Creswell, J. W. (2018). Qualitative inquiry and research design: Choosing among five approaches. Sage publications.