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2014 | 21 | 4 |

Tytuł artykułu

Using power analysis to estimate appropriate sample size

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The main aim of this paper is to provide some practical guidance to researchers on how statistical power analysis can be used to estimate sample size in empirical design. The paper describes the key assumptions underlying statistical power analysis and illustrates through several examples how to determine the appropriate sample size. The examples use hypotheses often tested in sport sciences and verified with popular statistical tests including the independent-samples t-test, one-way and two-way analysis of variance (ANOVA), correlation analysis, and regression analysis. Commonly used statistical packages allow researchers to determine appropriate sample size for hypothesis testing situations listed above.

Słowa kluczowe

Wydawca

-

Rocznik

Tom

21

Numer

4

Opis fizyczny

p.195-206,fig.,ref.

Twórcy

autor
  • Department of Psychology, University School of Physical Education, Poznan, Poland
autor
  • Faculty of English, Adam Mickiewicz University, Poznan, Poland
autor
  • Department of Foundations of Psychological Research, Institute of Psychology, Adam Mickiewicz University, Poznan, Poland
autor
  • Faculty of English, Adam Mickiewicz University, Poznan, Poland

Bibliografia

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  • 2. Fritz CO, Morris PE, Richler JJ. Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen. 2012; 141(1): 2-18.
  • 3. Ferguson GA, Takane Y. Analiza statystyczna w psychologii i pedagogice (Statistical analysis in psychology and education). Warszawa: Wydawnictwo Naukowe PWN. 2007.
  • 4. King BM, Minium EM. Statistical reasoning in psychology and education. 4th ed. Hoboken, NJ: John Wiley & Sons, Inc. 2003.
  • 5. Cohen J. A power primer. Psychol Bull. 1992; 112(1): 155-159.
  • 6. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum. 1988.
  • 7. Seltman HJ. Experimental design and analysis. Pittsburgh: Carnegie Mellon University. 2012.
  • 8. Kaplan D. The SAGE handbook of quantitative methodology for the social sciences. London, UK: Sage Publications, Inc. 2004.
  • 9. Good PI. Resampling methods: a practical guide to data analysis. 3rd ed. Boston, MA: Birkhäuser. 2005.
  • 10. Kraemer HC, Thiemann S. How many subjects?: Statistical power analysis in research: Sage Publications, Inc. 1987.
  • 11. Ajeneye F. Power and sample size estimation in research. Biomed Sci. 2006; 50: 988-990.
  • 12. Hill R. What sample size is "enough" in internet survey research? IPCT-J. 1998; 6(3-4): 1-12.
  • 13. Houser J. How many are enough? Statistical power analysis and sample size estimation in clinical research. J Clin Res Best Pract. 2007; 3(3): 1-5.
  • 14. Martínez-Mesa J, González-Chica DA, Bastos JL, et al. Sample size: how many participants do I need in my research? An Bras Dermatol. 2014; 89(4): 609-615.
  • 15. Eng J. Sample size estimation: How many individuals should be studied? Radiology. 2003; 227(2): 309-313.
  • 16. Burmeister E, Aitken LM. Sample size: How many is enough? Aust Crit Care. 2012; 25(4): 271-274.
  • 17. Wątroba J. Przystępnie o statystycznym podejściu do testowania hipotez badawczych i szacowania liczebności próby. StatSoft Polska. 2011: 33-43.
  • 18. Wątroba J. Praktyczne aspekty szacowania liczebności próby w badaniach empirycznych. StatSoft Polska. 2013: 5-15.
  • 19. Brzeziński J. Metodologia badań psychologicznych (Methodology of psychological research). Warszawa: Wydawnictwo Naukowe PWN. 1996.
  • 20. Tomczak M, Tomczak E. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. TSS. 2014; 21(1): 19-25.
  • 21. Aranowska E, Rytel J. Istotność statystyczna - co to naprawdę znaczy? (Statistical significance - what does it really mean?). 1997; 40(3-4): 249-260.
  • 22. Levine G, Parkinson S. Experimental methods in psychology. New York, NY: Psychology Press. 2014.
  • 23. Aberson CL. Applied power analysis for the behavioral sciences. New York, NY: Routledge. 2010.
  • 24. Olinsky A, Schumacher P, Quinn J. The importance of teaching power in statistical hypothesis testing. Int J Math Teach Learn. 2012; 1.
  • 25. Wilson VanVoorhis CR, Morgan BL. Understanding power and rules of thumb for determining sample sizes. Tutor Quant Methods Psychol. 2007; 3(2): 43-50.
  • 26. Borkowf CB, Johnson LL, Albert PS. Power and sample size calculations. In: Gallin JI and Ognibene FP, eds., Principles and practice of clinical research. Amsterdam: Academic Press. 2012: 271-283.
  • 27. Rosner B. Fundamentals of biostatistics. 7th ed. Boston, MA: Brooks/Cole Cengage Learning. 2011.
  • 28. Howell DH. Statistical methods for psychology. Belmont, CA: Wadsworth, Cengage Learning. 2012.
  • 29. Steiger JH. Beyond the F test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis. Psychol Methods. 2004; 9(2): 164-182.
  • 30. Steiger JH, Fouladi RT. Noncentrality interval estimation and the evaluation of statistical models. In: Harlow LL, Mulaik SA, and Steiger JH, eds., What if there were no significance tests? Mahwah, NJ: Erlbaum. 1997: 221-257.

Typ dokumentu

Bibliografia

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