Sampling Strategies In This Week's Discussion

Sampling Strategiesampling 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.

Paper For Above instruction

The process of determining an appropriate sampling strategy for a research project is fundamental to the validity and reliability of the study’s findings. The selection of a sampling method hinges on several critical factors, including the research questions, hypotheses, variables involved, and the statistical power required to detect meaningful effects. For my research proposal, which aims to examine the impact of remote work on employee productivity and job satisfaction, a stratified random sampling strategy appears to be most appropriate. This approach allows for the inclusion of diverse subgroups within the population, such as different industries, job roles, and geographic locations, ensuring that the sample accurately reflects the heterogeneity of the target population.

The rationale for choosing stratified random sampling is rooted in the need to enhance the representativeness of the sample and increase the precision of the estimates. Given the variability in how remote work influences different demographic and professional groups, stratification ensures that each subgroup is proportionally represented in the sample. This method reduces sampling bias and increases the likelihood that the findings can be generalized across various sectors and roles. Additionally, considering the desired power level of 0.8 (or 80%) as recommended in the literature, the sample size must be sufficiently large to detect effect sizes that are both statistically significant and practically meaningful. Calculating the inputs to determine the appropriate sample size involves considering the expected effect size, the alpha level (typically set at 0.05), and the power level.

To compute the sample size for this quantitative study, I would input the anticipated effect size based on prior research or pilot data, the alpha level, and the power level into statistical software or sample size calculators. For instance, if prior studies suggest a medium effect size (Cohen's d = 0.5), with an alpha of 0.05 and power of 0.8, the software would provide an estimate of the number of participants needed in each stratum. This process ensures that the study has adequate statistical power to test the hypotheses effectively, minimizing the risks of Type I and Type II errors. Overall, a well-structured sampling strategy grounded in empirical inputs and theoretical considerations is essential to produce valid and generalizable findings in this research on remote work and productivity.

References

  • Frankfort-Nachmias, C., & Nachmias, D. (2008). Research methods in the social sciences (7th ed.). Worth Publishers.
  • Biau, D. J., Kernéis, S., & Porcher, R. (2018). Sample size calculation. Journal of the American Medical Association, 318(19), 1858–1859.
  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erlbaum.
  • Research Methods Knowledge Base. (2023). Statistical Power. Retrieved from https://researchmethodology.net/statistical-power/
  • Lenth, R. V. (2001). Some practical guidelines for effective sample size determination. The American Statistician, 55(3), 187–193.
  • Kelley, K., Clark, B., Brown, V., & Sitzia, J. (2003). Good practice in the conduct and reporting of survey research. International Journal for Quality in Health Care, 15(3), 261–266.
  • Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4.
  • Floyd, J. K., & Fowler, Jr., F. J. (2013). Survey research methods (5th ed.). Sage Publications.
  • Santos, J. R. A. (1999). The short and useful quantitative heldings in educational research. International Journal of Research & Method in Education, 22(2), 215–227.
  • Waller, N. G., & Akstein, P. (2015). Statistical Power Analysis: An introductory guide. Springer Publishing.

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