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Bestimmung von Teststärke, Effektgröße und optimalem Stichprobenumfang

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Zusammenfassung

Dieses Kapitel vermittelt folgende Lernziele: Die Teststärke definieren und Post-hoc- sowie A-priori-Teststärkeanalysen voneinander abgrenzen können. Wissen, was man unter der Effektgröße versteht und wie man sie berechnet. Verschiedene standardisierte Effektgrößenmaße unterscheiden und hinsichtlich ihrer Ausprägung als kleine, mittlere oder große Effekte einordnen können. Das Konzept des optimalen Stichprobenumfangs erläutern können. Wissen, wie man den optimalen Stichprobenumfang für Studien mit unterschiedlichen Signifikanztests im Zuge der Untersuchungsplanung festlegt.

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Literatur

  • Abelson, R. P., & Prentice, D. A. (1997). Contrast tests of interaction hypothesis. Psychological Methods, 2, 315–328.

    Google Scholar 

  • Aberson, C. L. (2019). Applied power analysis for the behavioral sciences (2nd ed.). New York: Psychology Press.

    Google Scholar 

  • Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An alternative to Cohen’s standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10, 317–328.

    PubMed  Google Scholar 

  • American Psychological Association. (2019). Publication manual of the American Psychological Association (7th ed.). Washington DC: American Psychological Association.

    Google Scholar 

  • Anvari, F., & Lakens, D. (2021). Using anchor-based methods to determine the smallest effect size of interest. Journal of Experimental Social Psychology, 96, 104159.

    Google Scholar 

  • Bausell, B. R., & Li, Y.–F. (2003) Power analysis for experimental research: A practical guide for the biological, medical and social sciences. Cambridge: Cambridge University Press.

    Google Scholar 

  • Bird, K. D. (2002). Confidence intervals for effect sizes in analysis of variance. Educational and Psychological Measurement, 62, 197–226.

    Google Scholar 

  • Bortz, J. (2005). Statistik (6. Aufl.). Berlin: Springer.

    Google Scholar 

  • Bortz, J. & Lienert, G. A. (2008). Kurzgefasste Statistik für die klinische Forschung (3. Aufl.). Heidelberg: Springer.

    Google Scholar 

  • Bortz, J., Lienert, G. A. & Boehnke, K. (2008). Verteilungsfreie Methoden in der Biostatistik (3. Aufl.). Berlin: Springer.

    Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler. Berlin: Springer.

    Google Scholar 

  • Brizendine, L. (2006). The female brain. New York: Broadway Books.

    Google Scholar 

  • Chen, H., Cohen, P., & Chen, S. (2010). How big is a big odds ratio? Interpreting the magnitudes of odds ratios in epidemiological studies. Communications in Statistics – Simulation and Computation, 39, 860–864.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304–1312.

    Google Scholar 

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

    PubMed  Google Scholar 

  • Cohen, J. (1994). The earth is round (\(p<0{.}05\)). American Psychologist, 49, 997–1003.

    Google Scholar 

  • Cooper, H. (2020). Reporting quantitative research in psychology. How to meet APA style journal article reporting standards. Washington: APA.

    Google Scholar 

  • Cumming, G. (2012). Understanding the new statistics: Effect sizes, confidence intervals, and meta-analysis. New York: Routledge.

    Google Scholar 

  • Cumming, G., & Finch, S. (2001). A primer on the understanding, use, and calculation of confidence intervals that are based on central and noncentral distribution. Educational Psychological Measurement, 61, 532–574.

    Google Scholar 

  • Dunlop, W. P., Cortina, J. M., Vaslow, J. B., & Burke, M. J. (1996). Meta-analysis of experiments with matched groups or repeated measure designs. Psychological Methods, 1, 170–177.

    Google Scholar 

  • Ellis, P. D. (2010). The essential guide to effect sizes: An introduction to statistical power, meta-analysis and the interpretation of research results. Cambridge: Cambridge University Press.

    Google Scholar 

  • Erdfelder, E. (1984). Zur Bedeutung und Kontrolle des \(\upbeta\)-Fehlers bei der inferenzstatistischen Prüfung log-linearer Modelle. Zeitschrift für Sozialpsychologie, 15, 18–32.

    Google Scholar 

  • Erdfelder, E., Faul, F., Buchner, A. & Cüpper, L. (2010). Effektgröße und Teststärke. In H. Holling & B. Schmitz (Hrsg.), Handbuch der Psychologischen Methoden und Evaluation (S. 358–369). Göttingen: Hogrefe.

    Google Scholar 

  • 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, 1149–1160.

    PubMed  Google Scholar 

  • Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191.

    PubMed  Google Scholar 

  • Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40, 532–538.

    Google Scholar 

  • Fleiss, J. L. (1994). Measures of effect size for categorical data. In H. Cooper, & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 246–259). New York: Sage.

    Google Scholar 

  • Fowler, R. L. (1987). A general method for comparing effect-magnitudes in ANOVA designs. Educational and Psychological Measurement, 47, 361–367.

    Google Scholar 

  • Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology: General, 141, 2–18.

    PubMed  Google Scholar 

  • Gatsonis, C., & Sampson, A. R. (1989). Multiple correlation: Exact power and sample size calculations. Psychological Bulletin, 106, 519–524.

    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: Erlbaum.

    Google Scholar 

  • Gillett, R. (1994). An average power criterion for sample size estimation. Journal of the Royal Statistical Society. Series D (The Statistician), 43, 389–394.

    Google Scholar 

  • Gillett, R. (2003). The metric comparability of meta-analytic effect-size estimators from factorial designs. Psychological Methods, 8, 419–433.

    PubMed  Google Scholar 

  • Glass, G. V. (1976). Primary, secondary, and meta-analysis of research. Educational Researcher, 5, 3–8.

    Google Scholar 

  • Grissom, R. J., & Kim, J. J. (2012). Effect sizes for research: Univariate and multivariate applications (2nd ed.). New York, NY: Routledge.

    Google Scholar 

  • Haddock, C. K., Rindskopf, D., & Shadish, W. R. (1998). Using odds ratios as effect sizes for meta-analysis of dichotomous data: A primer on methods and issues. Psychological Methods, 3, 339–353.

    Google Scholar 

  • Hager, W. (2004). Testplanung zur statistischen Prüfung psychologischer Hypothesen. Göttingen: Hogrefe.

    Google Scholar 

  • Halpern, S. D., Karlawish, J. H. T., & Berlin, J. A. (2002). The continuing unethical conduct of underpowered clinical trials. Journal of the American Medical Association, 288, 358–362.

    PubMed  Google Scholar 

  • Hays, W. L. (1994). Statistics (5th ed.). New York: Harcourt College Publishers.

    Google Scholar 

  • Hedges, L. V. (1982). Estimation of effect size from a series of independent experiments. Psychological Bulletin, 92, 490–499.

    Google Scholar 

  • Held, U. (2010). Was ist eine „Odds Ratio“ – Und wann wird sie verwendet? Schweiz Med Forum, 10, 634–635.

    Google Scholar 

  • Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power: The pervasive fallacy of power calculations for data analysis. The American Statistician, 55, 19–24.

    Google Scholar 

  • Hsu, L. M. (2004). Biases of success rate differences shown in binomial effect size displays. Psychological Methods, 9, 183–197.

    PubMed  Google Scholar 

  • James, D., & Drakich, J. (1993). Understanding gender differences in amount of talk. In D. Tannen (Ed.), Gender and conversational interaction (pp. 281–312). Oxford: Oxford University Press.

    Google Scholar 

  • Janosky, J. E. (2002). The ethics of underpowered clinical trials. Journal of the American Medical Association, 288, 2118–2122.

    PubMed  Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • Keren, G., & Lewis, C. (1979). Partial omega squared for ANOVA designs. Educational and Psychological Measurement, 39, 119–128.

    Google Scholar 

  • Kirk, R. E. (1996). Practical significance: A concept whose time has come. Educational and Psychological Measurement, 56, 746–759.

    Google Scholar 

  • Kline, R. B. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington: American Psychological Association.

    Google Scholar 

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

    Google Scholar 

  • Kshirsagar, A. M. (1972). Multivariate analysis. New York: Dekker.

    Google Scholar 

  • Kuechler, M. (1980). The analysis of nonmetric data. The relation of dummy dependent variable regression using an additive-saturated Grizzle-Starmer-Koch model. Sociological Methods & Research, 8, 369–388.

    Google Scholar 

  • Lakens, D., Scheel, A. M., & Isager, P. M. (2018). Equivalence testing for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 1, 259–269.

    Google Scholar 

  • Lenhard, W. & Lenhard, A. (2016). Berechnung von Effektstärken. Abgerufen am 13. August 2021, unter https://www.psychometrica.de/effektstaerke.html Dettelbach: Psychometrica. https://doi.org/10.13140/RG.2.2.17823.92329

  • Leonhart, R. (2009). Statistik – Einstieg und Vertiefung (2. Aufl.). Bern: Huber.

    Google Scholar 

  • Levine, M., & Ensom, M. H. H. (2001). Post hoc power analysis: An idea whose time has passed? Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy, 21, 405–409.

    Google Scholar 

  • Lilford, R., & Stevens, A. J. (2002). Underpowered studies. British Journal of Surgery, 89, 129–131.

    PubMed  Google Scholar 

  • Lipsey, M. W. (1997). Design sensitivity: Statistical power for applied experimental research. In L. Bickman, & D. Rog (Eds.), Handbook of applied social research methods (pp. 39–68). Thousand Oaks: Sage.

    Google Scholar 

  • Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks: Sage.

    Google Scholar 

  • Maxwell, S. E. (2000). Sample size and multiple regression analysis. Psychological Methods, 5, 434–458.

    PubMed  Google Scholar 

  • Maxwell, S. E. (2004). The persistence of underpowered studies in psychological research: Causes, consequences, and remedies. Psychological Methods, 9, 147–163.

    PubMed  Google Scholar 

  • Mehl, M. R., Vazire, S., Ramirez–Esparza, N., Slatcher, R. B., & Pennebaker, J. W. (2007). Are women really more talkative than men? Science, 317, 82.

    PubMed  Google Scholar 

  • Mendoza, J. L., & Stafford, K. L. (2001). Confidence intervals, power calculation, and sample size estimation for the squared multiple correlation coefficient under the fixed and random regression models: A computer program and useful standard tables. Educational and Psychological Measurement, 61, 650–667.

    Google Scholar 

  • Murphy, K. R., Myors, B., & Wolach, A. (2014). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests (4th ed.). Milton Park: Routledge.

    Google Scholar 

  • Nickerson, R. S. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5, 241–301.

    PubMed  Google Scholar 

  • Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. Psychological Methods, 8, 434–477.

    PubMed  Google Scholar 

  • Peng, C.-Y. J., Long, H., & Abaci, S. (2012). Power analysis software for educational researchers. The Journal of Experimental Education, 80, 113–136.

    Google Scholar 

  • Reedera, H. M. (1996). A critical look at gender difference in communication research. Communication Studies, 47, 318–330.

    Google Scholar 

  • Rosenthal, M. C. (1994). The fugitive literature. In H. Cooper, & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 85–94). Thousand Oaks: Sage.

    Google Scholar 

  • Rosenthal, R., & Rubin, D. B. (1982). A simple, general purpose display of magnitudes of experimental effect. Journal of Educational Psychology, 74, 166–169.

    Google Scholar 

  • Sachs, L. (2002). Statistische Auswertungsmethoden (10. Aufl.). Berlin: Springer.

    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.

    Google Scholar 

  • Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin.

    Google Scholar 

  • Smithson, M. J. (2003). Confidence intervals. Thousand Oaks: Sage.

    Google Scholar 

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

    PubMed  Google Scholar 

  • Thompson, B. (2002). „Statistical“, „practical“, and „clinical“: How many kinds of significance do counselors need to consider? Journal of Counseling & Development, 80, 64–71.

    Google Scholar 

  • Weber, R., & Popova, L. (2012). Testing equivalence in communication research: Theory and application. Communication Methods and Measures, 6, 190–213.

    Google Scholar 

  • Westermann, R. (2000). Wissenschaftstheorie und Experimentalmethodik. Ein Lehrbuch zur Psychologischen Methodenlehre. Göttingen: Hogrefe.

    Google Scholar 

  • Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychological journals. Guidelines and explanations. American Psychologist, 54, 594–604.

    Google Scholar 

  • Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical principles in experimental design. New York: McGraw-Hill.

    Google Scholar 

  • Zhang, Y., Hedo, R., Rivera, A., Rull, R., Richardson, S., & Tu, X. M. (2019). Post hoc power analysis: Is it an informative and meaningful analysis? General Psychiatry, 32, e100069. https://doi.org/10.1136/gpsych-2019-100069

    Article  PubMed  PubMed Central  Google Scholar 

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  1. Technische Universität Ilmenau, Ilmenau, Deutschland

    Nicola Döring

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Döring, N. (2023). Bestimmung von Teststärke, Effektgröße und optimalem Stichprobenumfang. In: Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64762-2_14

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