Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach

Abstract

The accuracy in parameter estimation approach to sample size planning is developed for the coefficient of variation, where the goal of the method is to obtain an accurate parameter estimate by achieving a sufficiently narrow confidence interval. The first method allows researchers to plan sample size so that the expected width of the confidence interval for the population coefficient of variation is sufficiently narrow. A modification allows a desired degree of assurance to be incorporated into the method, so that the obtained confidence interval will be sufficiently narrow with some specified probability (e.g., 85% assurance that the 95% confidence interval width will be no wider than ω units). Tables of necessary sample size are provided for a variety of scenarios that may help researchers planning a study where the coefficient of variation is of interest plan an appropriate sample size in order to have a sufficiently narrow confidence interval, optionally with some specified assurance of the confidence interval being sufficiently narrow. Freely available computer routines have been developed that allow researchers to easily implement all of the methods discussed in the article.

References

  1. Algina, J., &Olejnik, S. (2000). Determining sample size for accurate estimation of the squared multiple correlation coefficient.Multivariate Behavioral Research,35, 119–136.

    Article  Google Scholar 

  2. Babkoff, H., Kelly, T. L., &Naitoh, P. (2001). Trial-to-trial variance in choice reaction time as a measure of the effect of stimulants during sleep deprivation.Military Psychology,13, 1–16.

    Article  Google Scholar 

  3. Bedeian, A. G., &Mossholder, K. W. (2000). On the use of the coefficient of variation as a measure of diversity.Organizational Research Methods,3, 285–297.

    Article  Google Scholar 

  4. Cohen, J. (1988).Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  5. Cohen, J. (1994). The earth is round (p<.05).American Psychologist,49, 997–1003.

    Article  Google Scholar 

  6. Dinges, D. F., &Kribbs, N. B. (1991). Performance while sleepy: Effects of experimentally-induced sleepiness. In T. H. Monk (Ed.),Sleep, sleepiness, and performance (pp. 97–128). New York: Wiley.

    Google Scholar 

  7. Efron, B., &Tibshirani, R. J. (1993).An introduction to the bootstrap. New York: Chapman & Hall/CRC.

    Google Scholar 

  8. Frith, U., &Frith, C. (2001). The biological basis of social interaction.Current Directions in Psychological Science,10, 151–155.

    Article  Google Scholar 

  9. Guenther, W. C. (1981). Sample size formulas for normal theory T tests.American Statistician,35, 243–244.

    Article  Google Scholar 

  10. Hahn, G., &Meeker, W. (1991).Statistical intervals: A guide for practitioners. New York: Wiley.

    Google Scholar 

  11. Haldane, J. B. S. (1955). The measurement of variation.Evolution,9, 484.

    Article  Google Scholar 

  12. Hayashi, R. (2000). Correlation between coefficient of variation of choice reaction time and components of event-related potentials (P300): Effect of benzodiazepine.Journal of the Neurological Sciences,178, 52–56.

    Article  PubMed  Google Scholar 

  13. Hunter, J. E., &Schmidt, F. L. (2004).Methods of meta-analysis: Correcting error and bias in research findings. Newbury Park, CA: Sage.

    Google Scholar 

  14. Johnson, N. L., Kotz, S., &Balakrishnan, N. (1995).Continuous univariate distributions (2nd ed., Vol. 2). New York: Wiley.

    Google Scholar 

  15. Johnson, N. L., &Welch, B. L. (1940). Applications of the noncentral t distribution.Biometrika,31, 362–389.

    Google Scholar 

  16. Kelley, K. (2007a). Confidence intervals for standardized effect sizes: Theory, application, and implementation.Journal of Statistical Software,20, 1–24.

    Google Scholar 

  17. Kelley, K. (2007b). Methods for the Behavioral, Educational, and Social Sciences (MBESS) [Computer software and manual]. Retrievable from www.cran.r-project.org/.

  18. Kelley, K. (2007c). Methods for the behavioral, educational, and social sciences: An R package.Behavior Research Methods,39, 979–984.

    Article  PubMed  Google Scholar 

  19. Kelley, K. (2007d).Sample size planning for the squared multiple correlation coefficient: Accuracy in parameter estimation via narrow confidence intervals. Manuscript submitted for publication.

  20. Kelley, K., &Maxwell, S. E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant.Psychological Methods,8, 305–321.

    Article  PubMed  Google Scholar 

  21. Kelley, K., & Maxwell, gnS. E. (in press). Sample size planning for multiple regression: Power and accuracy for omnibus and targeted effects. In J. Brannon, P. Alasuutari, & L. Bickman (Eds.),Sage handbook of social research methods. Thousand Oaks, CA: Sage.

  22. Kelley, K., Maxwell, S. E., &Rausch, J. R. (2003). Obtaining power or obtaining precision: Delineating methods of sample size planning.Evaluation & the Health Professions,26, 258–287.

    Article  Google Scholar 

  23. Kelley, K., &Rausch, J. R. (2006). Sample size planning for the standardized mean difference: Accuracy in parameter estimation via narrow confidence intervals.Psychological Methods,11, 363–385.

    Article  PubMed  Google Scholar 

  24. Kirk, R. (2001). Promoting good statistical practice: Some suggestions.Educational & Psychological Measurement,61, 213–218.

    Article  Google Scholar 

  25. Kosslyn, S. M., Cacioppo, J. T., Davidson, R. J., Hugdahl, K., Lovallo, W. R., Spiegel, D., &Rose, R. (2002). Bridging psychology and biology: The analysis of individuals in groups.American Psychologist,57, 341–351.

    Article  PubMed  Google Scholar 

  26. Kraemer, H. C., &Thiemann, S. (1987).How many subjects?: Statistical power analysis in research. Newbury Park, CA: Sage.

    Google Scholar 

  27. Kupper, L. L., &Hafner, K. B. (1989). How appropriate are popular sample size formulas?The American Statistician,43, 101–105.

    Article  Google Scholar 

  28. Lipsey, M. W. (1990).Design sensitivity: Statistical power for experimental research. Newbury Park, CA: Sage.

    Google Scholar 

  29. Mace, A. E. (1964).Sample size determination. New York: Reinhold.

    Google Scholar 

  30. McKay, A. T. (1932). Distribution of the coefficient of variation and the extended “t” distribution.Journal of the Royal Statistical Society,95, 695–698.

    Article  Google Scholar 

  31. Meehl, P. E. (1997). The problem is epistemology, not statistics: Replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions. In L. L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.),What if there were no significance tests? (pp. 393–426). Mahwah, NJ: Erlbaum.

    Google Scholar 

  32. Monchar, P. H. (1981). Regional educational inequality and political instability.Comparative Education Review,25, 1–12.

    Article  Google Scholar 

  33. Murphy, K. R., &Myors, B. (1998).Statistical power analysis: A simple and general model for traditional and modern hypothesis tests. Mahwah, NJ: Erlbaum.

    Google Scholar 

  34. Neyman, J. (1937). Outline of a theory of statistical estimation based on the classical theory of probability.Philosophical Transactions of the Royal Society A,236, 333–380.

    Article  Google Scholar 

  35. R Development Core Team (2007). R: A language and environment for statistical computing [Computer software and manual], R Foundation for Statistical Computing. Retrieved from www.r-project.org.

  36. Reed, G. F., Lynn, F., &Meade, B. D. (2002). Use of coefficient of variation in assessing variability of quantitative assays.Clinical & Diagnostic Laboratory Immunology,9, 1235–1239.

    Google Scholar 

  37. Rozeboom, W. W. (1966).Foundations of the theory of prediction. Homewood, IL: Dorsey.

    Google Scholar 

  38. Salmon, P., &Hall, G. M. (1997). A theory of postoperative fatigue: An interaction of biological, psychological, and social processes.Pharmacology Biochemistry & Behavior,56, 623–628.

    Article  Google Scholar 

  39. Schmidt, F. L. (1996). Statistical significance testing and cumulative knowledge in psychology: Implications for training of researchers.Psychological Methods,1, 115–129.

    Article  Google Scholar 

  40. Shafir, S. (2000). Risk-sensitivity foraging: The effect of relative variability.Oikos,88, 663–669.

    Article  Google Scholar 

  41. Sheret, M. (1984). Note on methodology: The coefficient of variation.Comparative Education Review,28, 467–476.

    Article  Google Scholar 

  42. Smithson, M. (2001). Correct confidence intervals for various regression effect sizes and parameters: The importance of noncentral distributions in computing intervals.Educational & Psychological Measurement,61, 605–632.

    Article  Google Scholar 

  43. Sokal, R. R., &Braumann, C. A. (1980). Significance tests for coefficients of variation and variability profiles.Systematic Zoology,29, 50–66.

    Article  Google Scholar 

  44. Steiger, J. H. (2004). Beyond theF test: Effect size confidence intervals and tests of close fit in the analysis of variance and contrast analysis.Psychological Methods,9, 164–182.

    Article  PubMed  Google Scholar 

  45. Steiger, J. H., &Fouladi, R. T. (1997). Noncentrality interval estimation and the evaluation of statistical methods. In L. L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.),What if there were no significance tests? (pp. 221–257). Mahwah, NJ: Erlbaum.

    Google Scholar 

  46. Task Force on Reporting of Research Methods in AERA Publications (2006).Standards for reporting on empirical social science research in aera publications. Washington, DC: American Educational Research Association.

    Google Scholar 

  47. Thompson, B. (2002). What future quantitative social science research could look like: Confidence intervals for effect sizes.Educational Researcher,31, 25–32.

    Article  Google Scholar 

  48. Velleman, P. F., &Wilkinson, L. (1993). Nominal, ordinal, interval, and ratio typologies are misleading.American Statistician,47, 65–72.

    Article  Google Scholar 

  49. Volkow, N. D., Zhu, W., Felder, C. A., Mueller, K., Welsh, T. F., Wang, G.-J., &de Leon, M. J. (2002). Changes in brain functional homogeneity in subjects with Alzheimer’s disease.Psychiatry Research: Neuroimaging,114, 39–50.

    Article  PubMed  Google Scholar 

  50. Weber, E. U., Shafir, S., &Blais, A.-R. (2004). Predicting risk sensitivity in humans and lower animals: Risk as variance or coefficient of variation.Psychological Review,111, 430–445.

    Article  PubMed  Google Scholar 

  51. Wilkinson, L., &The American Psychological Association Task Force on Statistical Inference (1999). Statistical methods in psychology: Guidelines and explanations.American Psychologist,54, 594–604.

    Article  Google Scholar 

  52. Williams, K. Y., &O’Reilly, C. A., III (1998). Demography and diversity in organizations: A review of 40 years of research.Research in Organizational Behavior,20, 77–140.

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ken Kelley.

Additional information

This work was sponsored in part by a Proffitt Fellowship for Educational Research.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kelley, K. Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach. Behavior Research Methods 39, 755–766 (2007). https://doi.org/10.3758/BF03192966

Download citation

Keywords

  • Confidence Interval Width
  • Dence Interval
  • Lower Confidence Limit
  • Noncentrality Parameter
  • Narrow Confidence Interval