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Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses

  • Published: November 2009
  • Volume 41, pages 1149–1160, (2009)
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Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses
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  • Franz Faul1,
  • Edgar Erdfelder2,
  • Axel Buchner3 &
  • …
  • Albert-Georg Lang3 
  • 124k Accesses

  • 19k Citations

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Abstract

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

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Author information

Authors and Affiliations

  1. Institut für Psychologie, Christian-Albrechts-Universität, Olshausenstr. 40, D-24098, Kiel, Germany

    Franz Faul

  2. Lehrstuhl für Psychologie III, Universität Mannheim, Schloss Ehrenhof Ost 255, D-68131, Mannheim, Germany

    Edgar Erdfelder

  3. Heinrich-Heine-Universität, Düsseldorf, Germany

    Axel Buchner & Albert-Georg Lang

Authors
  1. Franz Faul
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  2. Edgar Erdfelder
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  3. Axel Buchner
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  4. Albert-Georg Lang
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Corresponding authors

Correspondence to Franz Faul or Edgar Erdfelder.

Additional information

Manuscript preparation was supported by Grant SFB 504 (Project A12) from the Deutsche Forschungsgemeinschaft. We thank two anonymous reviewers for valuable comments on a previous version of the manuscript.

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Faul, F., Erdfelder, E., Buchner, A. et al. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods 41, 1149–1160 (2009). https://doi.org/10.3758/BRM.41.4.1149

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  • Received: 22 December 2008

  • Accepted: 18 June 2009

  • Issue Date: November 2009

  • DOI: https://doi.org/10.3758/BRM.41.4.1149

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Keywords

  • Implicit Association Test
  • Multiple Correlation Coefficient
  • Linear Multiple Regression
  • Effect Size Measure
  • Noncentrality Parameter
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