Sample size calculation in scientific research.
Abstract
This article provides an overview of common approaches and factors that affect sample size determination in various study designs, including experimental, observational, qualitative, and survey studies. The methodology used to determine sample size is discussed, along with the challenges, limitations, and importance of reporting sample size determination in research studies and provides guidelines for doing so. The article concludes with the need for researchers to carefully consider sample size determination when designing studies to ensure valid and reliable results. The article provides a useful resource for researchers and practitioners involved in study design and implementation.
References
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.
Feise, R. J. (2002). Do multiple outcome measures require p-value adjustment?. BMC medical research methodology, 2(1), 8.
Gamboa, M.E. (2018). Estadística aplicada a la investigación educativa. Dilemas Contemporáneos: Educación, Política y Valores, 5(2).
Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: the pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 19-24.
Hsieh, F. Y., & Wang, J. J. (2017). Estimating sample size for medical studies using effect size. Statistics in Medicine, 36(23), 3760-3775.
Kheifets, L., et al. (2010). Childhood leukemia and EMF: review of the epidemiologic evidence. Bioelectromagnetics, 31(4), 200-211.
Konietschke, F., Bathke, A. C., Hothorn, L. A., & Brunner, E. (2012). Testing and estimation of purely nonparametric effects using standardized information criteria. Journal of Computational and Graphical Statistics, 21(4), 936-955.
Li, B., Leal, S. M., & Abecasis, G. R. (2017). A robust general framework for the analysis of association studies in pedigrees. Nature Genetics, 49(10), 1521-1527.
Mekonnen, F. A., et al. (2019). The prevalence and associated factors of depression among rural communities in the northwest of Ethiopia: a cross-sectional study. BMC psychiatry, 19(1), 68.
Park, S. J., & Kim, S. H. (2019). The impact of sample size and effect size on type I error and power in hypothesis testing. Communications for Statistical Applications and Methods, 26(1), 45-55.
Perneger, T. V. (1998). What's wrong with Bonferroni adjustments. BMJ, 316(7139), 1236-1238.
Ravanshad, S., et al. (2019). Relationship between dietary intake and breast cancer risk in Iranian women: a case-control study. European journal of clinical nutrition, 73(9), 1207-1215.
Schönbrodt, F. D., & Perugini, M. (2013). At what sample size do correlations stabilize? Journal of Research in Personality, 47(5), 609-612.
