Sampling Strategy In This Week's Discussion ✓ Solved

Sampling Strategysampling Strategyin This Weeks Discussion And Applic

Sampling Strategy Sampling Strategy In this week's Discussion and Application assignments, you will continue to work on your Final Project. In this Discussion, you will describe your sampling strategy for your chosen research proposal. To prepare for this Discussion: Review Chapter 8 in the Frankfort-Nachmias and Nachmias book and the statistical power discussion on the Research Methods Knowledge Base website in this week's Learning Resources. Consider the quantitative research plan you are developing for your Final Project and your research questions, hypotheses, variables, and analysis. What kind of power would be necessary for your research problem to be able to tell you what you would like to learn? What kind of sampling strategy would you recommend for your plan? What is your rationale for this choice? With these thoughts in mind: Post by Day 3 a 3- to 5-paragraph description in which you describe inputs to compute sample size for your chosen research problem.

Sample Paper For Above instruction

In designing a research study, selecting an appropriate sampling strategy is crucial to ensure that the results are valid, reliable, and generalizable to the population of interest. The sampling strategy hinges upon several factors, including the research questions, hypotheses, variables, and the statistical power needed to detect effects. For my research proposal, which aims to examine the relationship between social media usage and academic performance among college students, I have identified specific inputs to help determine the appropriate sample size and sampling method. These inputs include the expected effect size, significance level (alpha), power level (1 - beta), and the population variability.

The effect size represents the magnitude of the expected relationship or difference and influences the required sample size to detect significant effects. Based on prior literature indicating a moderate correlation between social media usage and academic performance, I anticipate a medium effect size (Cohen’s f² = 0.15). To ensure sufficient statistical power, I plan to set the alpha level at 0.05, which balances the risk of Type I error, and aim for a power level of 0.80, which is standard for behavioral research. These inputs are fed into power analysis calculations, often performed with software such as G*Power, to estimate the minimum sample size needed to confidently detect the anticipated effect.

Regarding the sampling strategy, I recommend stratified random sampling to enhance the representativeness of the sample. Since the population of college students varies across different majors, academic years, and demographics, stratified sampling allows for proportional representation of these subgroups. This approach minimizes sampling bias and improves the generalizability of the findings. For example, I would stratify the population by year of study and major before randomly selecting participants within each stratum. This method ensures that key subgroups are adequately represented, making the results more applicable across the diverse student body.

In conclusion, the inputs required for calculating the sample size involve understanding the anticipated effect size, setting appropriate levels of significance and power, and considering the variability within the population. The selected stratified random sampling strategy is justified by its ability to produce a representative sample that reflects the diversity of the college student population. By carefully defining these inputs and choosing the appropriate sampling method, the research design will be robust enough to address the research questions with sufficient statistical power and generalizability.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
• Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). Worth Publishers.
• Research Methods Knowledge Base. (n.d.). Statistical Power. Retrieved from https://conjointly.com/kb/statistical-power/
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). GPower 3: A flexible statistical power analysis program for social, behavioral, and biomedical sciences. Behavior Research Methods*, 41(2), 1149-1160.
• Jolly, V. (2019). Sample size estimation in social science research. International Journal of Social Research Methodology, 22(4), 349-362.
• Litwin, M. (1995). How to measure survey reliability and validity. Sage Publications.
• Patel, V., et al. (2017). Strategies for sample size determination in health research: A systematic review. BMC Medical Research Methodology, 17, 160.
• Seaman, J. W., et al. (2015). The importance of sample size calculations for research validity. Research in Social and Administrative Pharmacy, 11(4), 519-524.
• Solomon, M. R. (2018). Consumer behavior: Buying, having, and being (12th ed.). Pearson.
• Thompson, B. (2012). Sampling. In Salkind, N. J. (Ed.), Encyclopedia of research design (pp. 991-993). Sage Publications.