Sample Size / Power Analysis for Dissertations
Basically, every study needs data, the question is how much data are needed. The sample size determination using a power analysis is the process of figuring that question out and will be of particular interest to both your committee for Proposal and IRB approval. Details below briefly examine how a power analysis calculates sample size, before going into how to use free resources to determine your study’s sample size in just a few minutes.
Power Analysis.
For a given statistical test, the sample size is calculated from statistical power, effect size, and significance level. Furthermore, it helps determine the required sample size for your study, as a larger effect size may necessitate a smaller sample to detect meaningful relationships. In addition, statistical power is influenced by effect size, as a larger effect size typically leads to greater power to detect the relationship. Finally, the significance level, or alpha, also interacts with the effect size, with larger effect sizes making it easier to achieve statistical significance. In other words, while power reflects the likelihood of correctly rejecting a false null hypothesis, beta represents the chance of failing to reject a false null hypothesis. Thus, a higher power corresponds to a lower probability of making a type II error, indicating the study’s ability to detect true effects.
Free Resources.
Moreover To facilitate sample size determination, our team has calculated the sample size for a large variety of statistical tests for small, medium, and large effect sizes. Additionally, you can easily sign up for a free trial of Intellectus Statistics to generate your write-up. For further information, learn more about sample size and power analysis here.
needs assessment. For further assistance with your proposal, call (877-437-8622) or email us at [email protected].
Power Analysis Resources
Abraham, W. T., & Russell, D. W. (2008). Statistical power analysis in psychological research. Social and Personality Psychology Compass, 2(1), 283-301.
Bausell, R. B., & Li, Y. -F. (2002). Power analysis for experimental research: A practical guide for the biological, medical and social sciences. Cambridge, UK: Cambridge University Press.
Bonett, D. G., & Seier, E. (2002). A test of normality with high uniform power. Computational Statistics & Data Analysis, 40(3), 435-445.
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Cohen, J. (1969). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates. 
Goodman, S. N. & Berlin, J. A. (1994). The use of predicted confidence intervals when planning experiments and the misuse of power when interpreting results. Annals of Internal Medicine, 121(3), 200-206.
Jones, A., & Sommerlund, B. (2007). A critical discussion of null hypothesis significance testing and statistical power analysis within psychological research. Nordic Psychology, 59(3), 223-230.
Lipsey, M. W. (1990). Design sensitivity: Statistical power for experimental research. Newbury Park, CA: Sage Publications. View
MacCallum, R. C., Browne, M. W., & Cai, L. (2006). Testing differences between nested covariance structure models: Power analysis and null hypotheses. Psychological Methods, 11(1), 19-35.
Murphy, K. R., & Myors, B. (2004). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests (2nd ed.).Mahwah, NJ: Lawrence Erlbaum Associates. View
Murphy, K. R., Myors, B., & Wolach, A. (2008). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests (3rd ed.).Mahwah, NJ: Lawrence Erlbaum Associates. View
Sahai, H., & Khurshid, A. (1996). Formulas and tables for the determination of sample sizes and power in clinical trials involving the difference of two populations: A review. Statistics in Medicine, 15(1), 1-21.