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
Bortz, J. (2005). Statistik (6. Aufl.). Berlin: Springer.
Bortz, J., & Lienert, G. (2008). Kurzgefasste Statistik für die klinische Forschung (3. Aufl.). Heidelberg: Springer.
Bortz, J. & Schuster, C. (2010). Statistik für Human–und Sozialwissenschaftler (7. Aufl.). Berlin: Springer.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. MahWah: Erlbaum.
Cohen, J. (1994). The earth is round \((p<0.05)\). American Psychologist, 49(12), 997–1003.
Colegrave, N., & Ruxton, G. D. (2003). Confidence Intervals are a More Useful Complement to Nonsignificant Tests Than are Power Calculations. Behavioral Ecology, 14(3), 446–447.
Cortina, J. M., & Dunlap, W. P. (1997). On the Logic and Purpose of Significance Testing. Psychological Methods, 2(2), 161–172.
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(4), 5323–5574.
Denis, D. J. (2003). Alternatives to Null Hypothesis Significance Testing. Theory & Science, 4(1). Retrieved November 1, 2013, from http://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), 1–11.
Fowler, R. L. (1985). Testing for substantive significance in applied research by specifying nonzero effect null hypotheses. Journal of Applied Psychology, 70(1), 215–218.
Greenwald, A. G. (1975). Consequences of prejudice against the null hypothesis. Psychological Bulletin, 82(1), 1–20.
Harlow, L. L., Mulaik, S. A., & Steiger, J. H. (Eds.). (2009). What if there were no significance tests? New York: Psychology Press, Taylor & Francis.
Hoenig, J. M. & Heisey, D. M. (2001). The abuse of power: The pervasive fallacy of power calculations for data analysis. The American Statistician, 55(1), 19–24.
Johnson, D. H. (1999). The insignificance of statistical significance testing. Journal of Wildlife Management, 63(3), 763–772.
Johnson, D. H. (2005). What hypothesis tests are not: A response to colegrave and ruxton. Behavioral Ecology, 16(1), 323–324.
Johnson, N. L. & Kotz, S. (1970). Continuous univariate distributions. Boston: Houghton Mifflin.
Kendall, M. G. & Stuart, A. (1973). The advanced theory of statistics (2nd ed.). London: Griffin.
Klemmert, H. (2004). Äquivalenz- und Effekttests in der psychologischen Forschung. Frankfurt/Main: Lang.
Kline, R. B. (2004). Beyond significance testing. Washington: American Psychological Association.
Kline, R. B. (2005). Beyond significance testing. Reforming data analysis methods in behavioral research (2nd ed.). Washington, DC: American Psychological Association.
Lipsey, M. W. & Wilson, D. B. (1993). The efficacy of psychological, educational, and behavioral treatment. American Psychologist, 48(12), 1181–1209.
Morrison, D. E. & Henkel, R. E. (Eds.). (2006). The significance test controversy. Chicago: Aldine.
Murphy, K. R. (1990). If the null hypothesis is impossible, why test it? American Psychologist, 45(3), 403–404.
Murphy, K. R. & Myors, B. (1998). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests. Mahwah: Erlbaum.
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(2), 234–248.
Murphy, K. R. & Myors, B. (2004). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests (2nd ed.). Mahwah: Erlbaum.
Murphy, K. R., Myors, B., & Wolach, A. (2009). Statistical power analysis: A simple and general model for traditional and modern hypothesis tests (3rd ed.). New York, NY, US: Routledge/Taylor & Francis Group.
Nachtigall, C., Kroehne, U., Funke, F., & Steyer, R. (2003). (Why) Should we use SEM? Pros and cons of structural equation modelling. Methods of Psychological Research Online, 8(2), 1–22.
Neumann, M. (2005). Entwicklung einer Skala zur Erfassung des Glaubens an Verschwörungstheorien (GVT-Skala). Unveröffentlichte Diplomarbeit, TU Berlin, Berlin.
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.
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.
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(4), 371–386.
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Döring, N., Bortz, J. (2016). Minimum-Effektgrößen-Tests. In: Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. Springer-Lehrbuch. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41089-5_15
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