eISSN: 3087-4041 editor@aimjournals.com
All Journals
Submit your article

The Pinnacle Research Journal of Scientific and Management Sciences

Open Access Peer Review International
Open Access

CALCULATING THE IDEAL SAMPLE SIZE FOR TARGETING POLICY TRAINING AND CERTIFICATION

DOI https://doi.org/10.55640/tprjsms-v02i03-01
PDF
Nouhoun Moro 1 , Boukary Soumahoro 1 ,
4 Assistant Professor, Department of Statistics, University of Malaysia
4 Assistant Professor, Department of Statistics, University of Malaysia

Abstract

Sample size calculation is a fundamental step in designing an effective training and certification policy, particularly when developing targeted programs aimed at specific groups. This article investigates the importance of calculating the appropriate sample size for training and certifying targeting policies, particularly in scenarios where policy implementation can be influenced by various factors such as skill levels, economic conditions, and demographic differences. The research outlines the steps required to determine sample sizes that provide statistical power and practical relevance for policy decisions. It introduces a methodology for sample size determination and discusses how this calculation impacts the success of training programs in meeting their objectives, ensuring valid certification, and achieving reliable outcomes. The findings suggest that an appropriate sample size can significantly enhance the accuracy of training evaluations and policy effectiveness.

Keywords

Sample size calculation, training programs, certification

References

📄 Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates.
📄 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.
📄 Norman, G. R., & Streiner, D. L. (2008). Biostatistics: The Bare Essentials. PMPH-USA.
📄 Maxwell, S. E., & Delaney, H. D. (2004). Designing Experiments and Analyzing Data: A Model Comparison Perspective. Psychology Press.
📄 Lachin, J. M. (2000). Biostatistical Methods: The Assessment of Error Rates and Statistical Power in Clinical Trials. Wiley-Interscience.
📄 Hedges, L. V., & Olkin, I. (1985). Statistical Methods for Meta-Analysis. Academic Press.
📄 Mendenhall, W., & Sincich, T. (2016). A Second Course in Statistics: Regression Analysis. Pearson Education.
📄 Armitage, P., Berry, G., & Matthews, J. N. (2002). Statistical Methods in Medical Research. Blackwell Science.
📄 Altman, D. G. (1991). Practical Statistics for Medical Research. CRC Press.
📄 Biau, D. J., & Kernéis, S. (2016). Statistics in Medicine: From Basic Concepts to Advanced Methods. Springer International Publishing.
📄 Cartwright, A., & Coulter, A. (2014). Statistics in Healthcare and Medical Research. Cambridge University Press.
📄 Donnelly, P., & Morton, A. J. (2010). Applied Statistics for the Social Sciences. Routledge.
📄 Stegenga, J. (2013). Clinical Trials: A Methodological Introduction. Springer.
📄 Harris, R. (2006). An Introduction to Statistical Methods and Data Analysis. Brooks/Cole.
📄 Lenth, R. V. (2001). Some Practical Guidelines for Effective Sample Size Determination. American Statistician, 55(3), 187-193.
📄 Schoenfeld, D. (1983). Sample Size Formulae for the Comparison of Two Group Means in the Analysis of Variance. Biometrika, 70(2), 595-603.
📄 Rucker, G., & Schwarzer, G. (2007). Meta-Analysis with R. Springer.
📄 Bland, M. (2015). An Introduction to Medical Statistics. Oxford University Press.
📄 Machin, D., Campbell, M. J., Fayers, P. M., & Pinol, A. (2007). Sample Size Tables for Clinical Studies. Wiley-Blackwell.
📄 Wilson, E. B. (1927). Probable Inference, the Law of Succession, and Statistical Inference. Journal of the American Statistical Association, 22(158), 209-212.
📄 Edwards, W. (1972). Subjective Probability: A Judgment of Representativeness. Psychological Bulletin, 77(5), 193-204.
📄 Williams, A. M., & Gillham, L. A. (2010). Analyzing Experimental Data: A Practical Guide for Social Scientists. Sage Publications.
📄 Mayhew, L. E., & Warner, L. A. (2009). Introduction to the Design and Analysis of Experiments. Wiley.
📄 Kraft, P., & McCullough, M. (2002). Designing and Analyzing Experiments. Cambridge University Press.
📄 Rubin, D. B. (2006). Matched Sampling for Causal Effects. Cambridge University Press.

Most read articles by the same author(s)

1 2 > >>