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GPOWER: A general power analysis program
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  • Published: March 1996

GPOWER: A general power analysis program

  • Edgar Erdfelder1,
  • Franz Faul2 &
  • Axel Buchner3 

Behavior Research Methods, Instruments, & Computers volume 28, pages 1–11 (1996)Cite this article

  • 40k Accesses

  • 2731 Citations

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Abstract

GPOWER is a completely interactive, menu-driven program for IBM-compatible and Apple Macintosh personal computers. It performs high-precision statistical power analyses for the most common statistical tests in behavioral research, that is,t tests,F tests, andχ 2 tests. GPOWER computes (1) power values for given sample sizes, effect sizes andα levels (post hoc power analyses); (2) sample sizes for given effect sizes,α levels, and power values (a priori power analyses); and (3)α andβ values for given sample sizes, effect sizes, andβ/α ratios (compromise power analyses). The program may be used to display graphically the relation between any two of the relevant variables, and it offers the opportunity to compute the effect size measures from basic parameters defining the alternative hypothesis. This article delineates reasons for the development of GPOWER and describes the program’s capabilities and handling.

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References

  • Bargmann, R. E., &Ghosh, S. P. (1964).Noncentral statistical distribution programs for a computer language (IBM Research Report RC-1231). Yorktown Heights, NY: IBM Watson Research Center.

    Google Scholar 

  • Borenstein, M., &Cohen, J. (1988).Statistical power analysis: A computer program. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Borenstein, M., Cohen, J., Rothstein, H. R., Pollack, S., &Kane, J. M. (1990). Statistical power analysis for one-way analysis of variance: A computer program.Behavior Research Methods, Instruments, & Computers,22, 271–282.

    Google Scholar 

  • Borenstein, M., Cohen, J., Rothstein, H. R., Pollack, S., &Kane, J. M. (1992). A visual approach to statistical power analysis on the microcomputer.Behavior Research Methods, Instruments, & Computers,24, 565–572.

    Google Scholar 

  • Bredenkamp, J. (1972).Der Signifikanztest in der psychologischen Forschung [The test of significance in behavioral research]. Frankfurt, Germany: Akademische Verlagsgesellschaft.

    Google Scholar 

  • Bredenkamp, J. (1980).Theorie und Planung psychologischer Experimente [Theory and design of psychological experiments]. Darmstadt, Germany: Steinkopff.

    Google Scholar 

  • Bredenkamp, J., &Erdfelder, E. (1985).Multivariate Varianzanalyse nach dem V-Kriterium [Multivariate analysis of variance using the V-criterion].Psychologische Beiträge,27, 127–154.

    Google Scholar 

  • Buchner, A., Faul, F., &Erdfelder, E. (1992).GPOWER: A priori-, post hoc-, and compromise power analyses for the Macintosh [Computer program]. Bonn, Germany: Bonn University.

    Google Scholar 

  • Cohen, J. (1962). The statistical power of abnormal-social psychological research: A review.Journal of Abnormal & Social Psychology,65, 145–153.

    Article  Google Scholar 

  • Cohen, J. (1965). Some statistical issues in psychological research. In B. B. Wolman (Ed.),Handbook of clinical psychology (pp. 95–121). New York: McGraw-Hill.

    Google Scholar 

  • Cohen, J. (1969).Statistical power analysis for the behavioral sciences. New York: Academic Press.

    Google Scholar 

  • Cohen, J. (1977).Statistical power analysis for the behavioral sciences (rev. ed.). New York: Academic Press.

    Google Scholar 

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

    Google Scholar 

  • Cohen, J. (1992). A power primer.Psychological Bulletin,112, 155–159.

    Article  PubMed  Google Scholar 

  • Cohen, J., &Cohen, P. (1983).Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Cowles, M., &Davis, C. (1982). On the origins of the.05 level of statistical significance.American Psychologist,37, 553–558.

    Article  Google Scholar 

  • Dixon, W. F., &Massey, F. J., Jr. (1957).Introduction to statistical analysis (2nd ed.). New York: McGraw-Hill.

    Google Scholar 

  • Erdfelder, E. (1984). Zur Bedeutung und Kontrolle des β-Fehlers bei der inferenzstatistischen Prüfung log-linearer Modelle [On significance and control of the β error in statistical tests of log-linear models].Zeitschrift für Sozialpsychologie,15, 18–32.

    Google Scholar 

  • Erdfelder, E., &Bredenkamp, J. (1994). Hypothesenprüfung [Evaluation of hypotheses]. In T. Herrmann & W. H. Tack (Eds.),Methodologische Grundlagen der Psychologie (pp. 604–648). Göttingen, Germany: Hogrefe.

    Google Scholar 

  • Faul, F., &Erdfelder, E. (1992).GPOWER: A priori-, post hoc-, and compromise power analyses for MS-DOS [Computer program]. Bonn, Germany: Bonn University.

    Google Scholar 

  • Gigerenzer, G. (1993). The superego, the ego, and the id in statistical reasoning. In G. Keren & C. Lewis (Eds.),A handbook for data analysis in the behavioral sciences: Methodological issues (pp. 311–339). Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Gigerenzer, G., &Murray, D. J. (1987).Cognition as intuitive statistics. Hillsdale, NJ: Erlbaum.

    Google Scholar 

  • Goldstein, R. (1989). Power and sample size via MS/PC-DOS computers.American Statistician,43, 253–260.

    Article  Google Scholar 

  • Hager, W., &Möller, H. (1986). Tables and procedures for the determination of power and sample sizes in univariate and multivariate analyses of variance and regression.Biometrical Journal,28, 647–663.

    Article  Google Scholar 

  • Hardison, C. D., Quade, D., &Langston, R. D. (1983). Nine functions for probability distributions. In SAS Institute, Inc. (Ed.),SUGI supplemental library user’s guide, 1983 edition (pp. 229–236). Cary, NC: SAS Institute, Inc.

    Google Scholar 

  • Huff, C., &Sobiloff, B. (1993). MacPsych: An electronic discussion list and archive for psychology concerning the Macintosh computer.Behavior Research Methods, Instruments, & Computers,25, 60–64.

    Google Scholar 

  • Johnson, N. L., &Kotz, S. (1970).Distributions in statistics: Continuous univariate distributions-2. New York: Wiley.

    Google Scholar 

  • Koele, P. (1982). Calculating power in analysis of variance.Psychological Bulletin,92, 513–516.

    Article  Google Scholar 

  • Koele, P., &Hoogstraten, J. (1980).Power and sample size calculations in analysis of variance (Révész Berichten No. 12). Amsterdam: University of Amsterdam.

    Google Scholar 

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

    Google Scholar 

  • Laubscher, N. F. (1960). Normalizing the noncentralt andF distributions.Annals of Mathematical Statistics,31, 1105–1112.

    Article  Google Scholar 

  • Lehmann, E. L. (1975).Nonparametrics. Statistical methods based on ranks. San Francisco: Holden-Day.

    Google Scholar 

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

    Google Scholar 

  • Milligan, G. W. (1979). A computer program for calculating power of the chi-square test.Educational & Psychological Measurement,39, 681–684.

    Article  Google Scholar 

  • Oakes, M. (1986).Statistical inference: A commentary for the social and behavioral sciences. New York: Wiley.

    Google Scholar 

  • O’Brien, R. G., &Muller, K. E. (1993). Unified power analysis fort-tests through multivariate hypotheses. In L. K. Edwards (Ed.),Applied analysis of variance in behavioral science (pp. 297–344). New York: Marcel Dekker.

    Google Scholar 

  • Onghena, P. (1994).The power of randomization tests for single-case designs. Unpublished doctoral dissertation. Leuven, Belgium: Katholieke Universiteit Leuven.

    Google Scholar 

  • Onghena, P., &Van Damme, G. (1994). SCRT 1.1: Single-case randomization tests.Behavior Research Methods, Instruments, & Computers,26, 369.

    Google Scholar 

  • Patnaik, P. B. (1949). The non-central χ2- andF-distributions and their applications.Biometrika,36, 202–232.

    PubMed  Google Scholar 

  • Pollard, P., &Richardson, J. T. E. (1987). On the probability of making type I errors.Psychological Bulletin,102, 159–163.

    Article  Google Scholar 

  • Press, W. H., Flannery, B. P., Teukolsky, S. A., &Vetterling, W. T. (1988).Numerical recipes in C: The art of scientific computing. Cambridge: Cambridge University Press.

    Google Scholar 

  • Rossi, J. S. (1990). Statistical power of psychological research: What have we gained in 20 years?Journal of Consulting & Clinical Psychology,58, 646–656.

    Article  Google Scholar 

  • Rothstein, H., Borenstein, M., Cohen, J., &Pollack, S. (1990). Statistical power analysis for multiple regression/correlation: A computer program.Educational & Psychological Measurement,50, 819–830.

    Article  Google Scholar 

  • Sedlmeier, P., &Gigerenzer, G. (1989). Do studies of statistical power have an effect on the power of studies?Psychological Bulletin,105, 309–316.

    Article  Google Scholar 

  • Singer, B., Lovie, A. D., &Lovie, P. (1986). Sample size and power. In A. D. Lovie (Ed.),New developments in statistics for psychology and the social sciences (pp. 129–142). London: British Psychological Society and Methuen.

    Google Scholar 

  • Tversky, A., &Kahneman, D. (1971). Belief in the law of small numbers.Psychological Bulletin,76, 105–110.

    Article  Google Scholar 

  • Westermann, R., &Hager, W. (1986). Error probabilities in educational and psychological research.Journal of Educational Statistics,11, 117–146.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Psychologisches Institut der Universität Bonn, Römerstraße 164, D-53117, Bonn, Germany

    Edgar Erdfelder

  2. Institut für Psychologie an der Universität Kiel, Olshausenstraße 40, D-24098, Kiel, Germany

    Franz Faul

  3. FB I-Psychologie, Universität Trier, Universitätsring 15, D-54286, Trier, Germany

    Axel Buchner

Authors
  1. Edgar Erdfelder
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  2. Franz Faul
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  3. Axel Buchner
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Corresponding authors

Correspondence to Edgar Erdfelder, Franz Faul or Axel Buchner.

Additional information

The authors would like to thank S. Dilger for performing the evaluation of the GPOWER accuracy mode calculations and J. Bredenkamp as well as several anonymous reviewers for helpful comments on earlier drafts of this paper. We also are grateful to V. Fischer, P. Frensch, J. Funke, P. Onghena, R. Pohl, E. Stirner, and I. Wegener, who served as beta testers of GPOWER.

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Erdfelder, E., Faul, F. & Buchner, A. GPOWER: A general power analysis program. Behavior Research Methods, Instruments, & Computers 28, 1–11 (1996). https://doi.org/10.3758/BF03203630

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  • Received: 12 October 1993

  • Accepted: 27 October 1994

  • Issue Date: March 1996

  • DOI: https://doi.org/10.3758/BF03203630

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Keywords

  • Power Analysis
  • Speed Mode
  • Statistical Power Analysis
  • Effect Size Measure
  • Noncentrality Parameter
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