Sample Size Consideration: Researcher Investment Scenario

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Consider this scenario: You are a researcher investigating risk factors related to pancreatic cancer. In order to promote positive social change, it is important to collect a large enough sample size to justify making generalizations to their population out of people who have pancreatic cancer. In this Discussion, reflect on the number of variables you plan to use and consider the impact that sample size has on generalizability.

As you consider the scenario, be mindful of the number of variables you, as the researcher, intend to use and the type of research design/analysis to be conducted. Also, consider the importance of sample size to generalizability. Review the Learning Resources, specifically the Power Table in the Johnson and Christensen course text.

Based on the literature from your search, what should be the minimum sample size for this study related to pancreatic cancer in order to justify making generalizations from the sample to the population? What information would you need to know in order to use the Power Table to determine an appropriate sample size? Further, explain the possible consequences of having too small of a sample size for this study.

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Determining an appropriate sample size is a critical aspect of research design, especially in studies aiming to generalize findings to larger populations, such as investigations into risk factors for pancreatic cancer. Adequate sample size ensures the statistical power necessary to detect significant effects and reduces the risk of Type II errors. In the context of pancreatic cancer research, where understanding risk factors can lead to public health interventions and policy changes, it is particularly vital to establish an appropriate sample size that balances scientific rigor with practical feasibility.

Based on the literature and statistical guidelines, the minimum sample size for a study investigating pancreatic cancer risk factors varies depending on several factors, including the number of variables studied, the expected effect size, alpha level, and statistical power. For example, using Cohen's guidelines for a multiple regression analysis with several predictors, a common rule of thumb suggests a minimum sample size of 50 to 100 cases per predictor variable (Cohen, 1988). If, for instance, the study intends to analyze five risk factors simultaneously through multiple regression, a sample size of at least 250 to 500 participants would be advisable to ensure sufficient power (Soper, 2014).

To utilize the Power Table from Johnson and Christensen (2020), I would need information including the effect size anticipated based on previous research or pilot studies, the alpha level (commonly set at 0.05), and the desired power level (usually 0.80 or 80%). Effect size estimation is crucial because it impacts the sample size; smaller effects require larger samples to detect. The Power Table provides values based on these parameters, helping determine the minimum sample size needed for reliable results.

If the sample size is too small, several negative consequences could ensue. First, the study may lack sufficient statistical power to detect significant associations between risk factors and pancreatic cancer, leading to Type II errors. This could result in failing to identify important risk factors, thereby hindering the development of preventive strategies. Second, a small sample size may limit the generalizability of the findings, making it difficult to apply results to the broader population (Button et al., 2013). Third, insufficient sample size can undermine the credibility of the research, as results may appear inconclusive or unreliable, reducing the overall impact of the study in advancing scientific knowledge and informing health policies.

In conclusion, careful consideration of sample size using established statistical guidelines and tools such as the Power Table is essential in pancreatic cancer risk factor research. Ensuring an adequately large sample enhances the study's validity, reliability, and applicability, ultimately contributing to better public health outcomes.

References

• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge.
• Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Sage.
• Soper, D. S. (2014). Sample size calculator for multiple regression. Retrieved from https://www.danielsoper.com/statcalc/calculator.aspx?id=83
• Hedeker, D., & Gibbons, R. D. (2006). Longitudinal data analysis. John Wiley & Sons.
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.
• Kim, J., & Kwon, S. (2018). Determining sample size in health research: A practical guide. Asian Journal of Psychiatry, 35, 45-50.
• Wonnacott, R. J., & Wonnacott, T. H. (1990). Introductory statistics (4th ed.). John Wiley & Sons.
• Grimes, D. A., & Schulz, K. F. (2002). Bias and causal associations in observational research. The Lancet, 359(9302), 249–253.
• Lenth, R. V. (2009). Some practical guidelines for effective sample size calculation. The American Statistician, 63(3), 259–266.
• Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D. G., & Newman, T. B. (2013). Designing clinical research. Lippincott Williams & Wilkins.