Research Study Proposal Part IV: Target Population Selection

Research Study Proposal Part Iv Target Population Selectionselect T

Research Study Proposal – Part IV: Target Population Selection Select the population, sample size, and data collection methods appropriate for your problem statement and research question. Write a 1,050- to 1,400-word paper describing how you will select your target population; your sample size; and your data collection methods. Include the following information in your paper: (Note: what you are doing here is putting more meat on the bones of your presentation in the previous week, but just in narrative form). •Rationale for identified target population •Method for determining appropriate sample size •Describe rationale for selecting the data collection methodology. •Describe descriptive or comparative statistics appropriate for your health care problem •Describe statistical tests that could be used to analyze the data

Paper For Above instruction

In designing a research study within healthcare, careful selection of the target population and appropriate data collection methods are crucial for obtaining valid and reliable results. This paper delineates the process of selecting the target population, determining an appropriate sample size, and choosing suitable data collection methodologies, along with an overview of the relevant statistical analyses tailored to address the research question effectively.

Rationale for the Identified Target Population

The target population should directly align with the research problem and specific aims of the study. For example, if the research focuses on improving management practices for type 2 diabetes patients in outpatient settings, the population would comprise adult patients diagnosed with type 2 diabetes receiving care at selected clinics. The rationale for selecting this population is rooted in the prevalence of the disease, the need for tailored interventions to improve health outcomes, and the accessibility of this population within the clinical setting. Additionally, selecting a well-defined target group enhances the specificity of findings, allowing for meaningful clinical application. The inclusion and exclusion criteria are set to ensure participants are representative of the population experiencing the issue; for instance, excluding minors and those with comorbid conditions that could confound the results, such as renal failure, ensures a homogeneous sample relatively unaffected by variables outside the scope of the study.

Method for Determining the Appropriate Sample Size

The sample size is a fundamental aspect impacting the statistical power of the study. To determine an appropriate size, a power analysis is typically performed using software such as G*Power, considering medium effect sizes, alpha levels of 0.05, and power of 0.8. For instance, if the primary outcome involves changes in HbA1c levels post-intervention, prior literature provides estimates of expected effect sizes that inform the calculation. A typical sample size might range from 100 to 150 participants, accounting for potential attrition. An adequate sample size ensures that the study has sufficient power to detect statistically significant differences or associations, thereby increasing the validity and reliability of the findings.

Rationale for Selecting the Data Collection Methodology

The choice of data collection methods hinges on the research questions and the nature of the variables of interest. For assessing health outcomes such as blood glucose levels, metabolic parameters, or behavioral changes, quantitative data collection techniques such as surveys with validated instruments, clinical measurements, or chart reviews are suitable. In this context, structured questionnaires combined with clinical data facilitate comprehensive assessment. The rationale for using surveys and clinical assessments lies in their ability to produce objective, quantifiable data while ensuring standardized procedures. Furthermore, employing electronic health records (EHRs) for retrospective data collection can enhance efficiency and accuracy. When confidentiality is a concern, anonymous surveys or securely stored data are utilized to protect participant privacy.

Descriptive or Comparative Statistics Appropriate for the Health Care Problem

For analyzing the collected data, descriptive statistics such as means, standard deviations, frequencies, and percentages provide an overview of the sample characteristics and baseline measurements. These statistics help contextualize the data and identify patterns or anomalies within the population. When the research aims to compare groups—for example, patients receiving standard care versus those receiving an intervention—comparative statistics such as t-tests for continuous variables or chi-square tests for categorical variables are appropriate. If the study involves pre- and post-intervention measurements within the same group, paired t-tests can be used. These statistical approaches permit evaluation of differences and effects related to the intervention or exposure, providing insight into the efficacy of healthcare strategies.

Statistical Tests That Could Be Used to Analyze the Data

Depending on the study design and data type, several statistical tests are suitable for detailed analysis. If the primary outcome involves continuous data such as blood glucose levels, parametric tests like ANOVA or t-tests are appropriate for group comparisons. For non-normally distributed data, non-parametric tests such as the Mann-Whitney U test or Wilcoxon signed-rank test can be employed. Regression analyses, including multiple linear regression, can investigate relationships between dependent and independent variables, adjusting for confounders. For categorical outcomes, logistic regression assesses the association between variables and likelihood of specific health events. Advance multimethod analyses may include multivariate techniques like factor analysis or cluster analysis if complex data patterns are being explored. Selecting suitable statistical tests ensures accurate interpretation of results and robust conclusions regarding healthcare interventions or phenomena.

In conclusion, meticulous planning of the target population, sample size determination, data collection methodology, and statistical analysis strategies underpin the success of healthcare research. These elements collectively contribute to producing valid, reliable, and impactful findings that can inform clinical practice and health policy.

References

• Creswell, J. W. (2014). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Sage Publications.
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for familywise and related error concerns. Behavioral Research Methods, 41(4), 1149–1160.
• Polit, D. F., & Beck, C. T. (2017). Nursing Research: Generating and Assessing Evidence for Nursing Practice. Wolters Kluwer.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson Education.
• Hannan, J., & O’Neill, C. (2019). Quantitative methods in health research: Principles and practices. Journal of Clinical Nursing, 28(23-24), 4318–4327.
• McNeill, A., et al. (2020). Using EHR data for health care research: Opportunities and challenges. Journal of Medical Internet Research, 22(12), e15599.
• Heale, R., & Twycross, A. (2015). Validity and reliability in quantitative studies. Evidence-Based Nursing, 18(3), 66–67.
• Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage Publications.
• Grimes, D. A., & Schulz, K. F. (2002). Bias and causal associations in observational research. The Lancet, 359(9302), 248–252.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs. Houghton Mifflin.