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Zusammenfassung

Dieses Kapitel vermittelt folgende Lernziele: Wissen, was das Good-Enough-Prinzip besagt. Untersuchungen unter Berücksichtigung von Minimum-Effekt-Nullhypothesen planen können. Minimum-Effekt-Nullhypothesen prüfen können. Prinzipien von Nullhypothesen als „Wunschhypothesen“ verstehen. Nullhypothesen als Wunschhypothesen testen können.

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Literatur

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Bortz, J. & Schuster, C. (2010). Statistik für Human–und Sozialwissenschaftler (7. Aufl.). Berlin: Springer.

    Book  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah: Erlbaum.

    Google Scholar 

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

    Google Scholar 

  • Colegrave, N., & Ruxton, G. D. (2003). Confidence intervals are a more useful complement to nonsignificant tests than are power calculations. Behavioral Ecology, 14, 446–447.

    Article  Google Scholar 

  • Cortina, J. M., & Dunlap, W. P. (1997). On the logic and purpose of significance testing. Psychological Methods, 2, 161–172.

    Article  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.

    Article  Google Scholar 

  • Denis, D. J. (2003). Alternatives to null hypothesis significance testing. Theory & Science, 4. Retrieved 2021, August 13, from https://theoryandscience.icaap.org/content/vol4.1/02_denis.html

  • Erdfelder, E., Faul, F., & Buchner, A. (1996). G*Power: A general power analysis program. Behaviour Research Methods, Instruments and Computers, 28, 1–11.

    Article  Google Scholar 

  • Fowler, R. L. (1985). Testing for substantive significance in applied research by specifying nonzero effect null hypotheses. Journal of Applied Psychology, 70, 215–218.

    Article  Google Scholar 

  • Greenwald, A. G. (1975). Consequences of prejudice against the null hypothesis. Psychological Bulletin, 82, 1–20.

    Article  Google Scholar 

  • Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.). (2009). What if there were no significance tests? New York: Psychology Press, Taylor & Francis.

    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 

  • Hyde, J. S., Lindberg, S. M., Linn, M. C., Ellis, A. B., & Williams, C. C. (2008). Gender similarities characterize math performance. Science, 321, 494–495.

    Article  PubMed  Google Scholar 

  • Johnson, D. H. (1999). The insignificance of statistical significance testing. The Journal of Wildlife Management, 63, 763–772.

    Article  Google Scholar 

  • Johnson, D. H. (2005). What hypothesis tests are not: A response to Colegrave and Ruxton. Behavioral Ecology, 16, 323–324.

    Article  Google Scholar 

  • Johnson, N. L., & Kotz, S. (1970). Continuous univariate distributions. Boston: Houghton Mifflin.

    Google Scholar 

  • Kendall, M. G., & Stuart, A. (1973). The advanced theory of statistics (2nd ed.). London: Griffin.

    Google Scholar 

  • Kim, J. H., & Robinson, A. P. (2019). Interval-based hypothesis testing and its applications to economics and finance. Econometrics, 7, 21. https://doi.org/10.3390/econometrics7020021

    Article  Google Scholar 

  • Klemmert, H. (2004). Äquivalenz- und Effekttests in der psychologischen Forschung. Frankfurt/Main: Lang.

    Google Scholar 

  • Kline, R. B. (2004). Beyond significance testing. Reforming data analysis methods in behavioral research (2nd ed.). Washington, DC: American Psychological Association.

    Book  Google Scholar 

  • Lakens, D. (2017). Equivalence tests: A practical primer for \(t\) tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8, 355–362.

    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.

    Article  Google Scholar 

  • Lipsey, M. W., & Wilson, D. B. (1993). The efficacy of psychological, educational, and behavioral treatment. Confirmation from meta-analysis. American Psychologist, 48, 1181–1209.

    Article  PubMed  Google Scholar 

  • Morrison, D. E., & Henkel, R. E. (Eds.). (2006). The significance test controversy. Chicago: Aldine.

    Google Scholar 

  • Murphy, K. R. (1990). If the null hypothesis is impossible, why test it? American Psychologist, 45, 403–404.

    Article  Google Scholar 

  • Murphy, K. R., & Myors, B. (1999). Testing the hypothesis that treatments have negligible effects: Minimum-effect tests in the general linear model. Journal of Applied Psychology, 84, 234–248.

    Article  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.). New York: Routledge/Taylor & Francis Group.

    Book  Google Scholar 

  • Nachtigall, C., Kroehne, U., Funke, F., & Steyer, R. (2003). (Why) should we use SEM? Pros and cons of structural equation modeling. Methods of Psychological Research Online, 8, 1–22.

    Google Scholar 

  • Neumann, M. (2005). Entwicklung einer Skala zur Erfassung des Glaubens an Verschwörungstheorien (GVT-Skala) (Unveröffentlichte Diplomarbeit). TU Berlin.

    Google Scholar 

  • Rindskopf, D. M. (2009). Testing „small“, not null, hypotheses: Classical and Bayesian approaches. In L. L. Harlow, S. A. Mulaik, & J. H. Steiger (Eds.), What if there were no significance tests? (pp. 319–332). New York: Psychology Press, Taylor & Francis.

    Google Scholar 

  • Serlin, R. C., & Lapsley, D. K. (1993). Rational appraisal of psychological research and the good-enough principle. In G. Keren, & C. Lewis (Eds.), A handbook for data analysis in the behavioral sciences. Methodological issues (pp. 199–228). Hillsdale: Erlbaum.

    Google Scholar 

  • Shieh, G. (2018). On detecting a minimal important difference among standardized means. Current Psychology 37, 640–647.

    Article  Google Scholar 

  • Shieh, G. (2020). Appraising minimum effect of standardized contrasts in ANCOVA designs: Statistical power, sample size, and covariate imbalance considerations. Statistics in Biopharmaceutical Research, 13, 468–475. https://doi.org/10.1080/19466315.2020.1788982

    Article  Google Scholar 

  • Tryon, W. W. (2001). Evaluating statistical difference, equivalence, and indeterminacy using inferential confidence intervals. An integrated alternative method of conducting null hypothesis statistical tests. Psychological Methods, 6, 371–386.

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

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Correspondence to Nicola Döring .

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© 2023 Der/die Autor(en), exklusiv lizenziert an Springer-Verlag GmbH, DE, ein Teil von Springer Nature

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Döring, N. (2023). Minimum-Effektgrößen-Tests. In: Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-64762-2_15

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  • DOI: https://doi.org/10.1007/978-3-662-64762-2_15

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