eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 26 27 article Jean Descôteaux jean.descoteaux@usherbrooke.ca 1 Université de Sherbrooke In 1999, Wilkinson and the Task Force on Statistical Inference published a number of recommendations concerning testing – related issues including, most importantly, statistical power. These recommendations are discussed prior to the presentation of the structure and the various articles of this special issue on statistical power. The contents of these articles will most certainly prove quite useful to those wishing to follow the Task Force’s recommendations. http://www.tqmp.org/Content/vol03-2/p026/p026.pdf Statistics Statistical power Editorial eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 28 34 article Jean Descôteaux jean.descoteaux@usherbrooke.ca 1 Université de Sherbrooke Despite the funding agencies’ growing demands towards power analyses, we believe researchers are still not fully aware of the statistical power concept, of the possible benefits of power analysis in the planning phase and of the ways to increase the chances of significantly detecting a given effect in their study. The following review falls within this area of interest. We discuss the history of the concept of statistical power, the reasons for its ongoing neglect, its potential benefits to researchers, as well as actual ways to improve statistical power. We also touch upon the subject of the impact of power analysis on the scientific literature. http://www.tqmp.org/Content/vol03-2/p028/p028.pdf Statistics statistical power fre TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 35 42 article Louis Laurencelle louis.laurencelle@uqtr.ca 1 Université du Québec à Trois- Rivières La puissance statistique est un concept dont la définition mathématique et l’utilisation par les chercheurs soulèvent encore des difficultés. Nous revisitons différents aspects du problème dans cet article polémique et formulons enfin quatre propositions argumentées dans le but d’éclairer le chercheur : 1. Le recours à une valeur d’effet prescrite, selon un argument clinique ou de portée pratique, produit une estimation de puissance délibérément fausse, et qui contrevient de ce fait aux raisons déontologiques sur lesquelles il repose. 2. Il est illusoire et peut être mensonger de calculer une puissance qui n’est pas basée sur des données du domaine de recherche. 3. Le chercheur qui possède de l’information numérique préalable sur son projet peut s’en servir utilement pour en estimer la puissance. 4. Le calcul de la puissance d’un test d’hypothèses bidirectionnel ne doit pas inclure la contrepartie de probabilité. http://www.tqmp.org/Content/vol03-2/p035/p035.pdf Statistics statistical power eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 43 50 article Carmen R. Wilson Van Voorhis wilson.carm@uwlax.edu 1 Betsy L. Morgan bm@where.ca 1 University of Wisconsin-La Crosse This article addresses the definition of power and its relationship to Type I and Type II errors. We discuss the relationship of sample size and power. Finally, we offer statistical rules of thumb guiding the selection of sample sizes large enough for sufficient power to detecting differences, associations, chi-square, and factor analyses. http://www.tqmp.org/Content/vol03-2/p043/p043.pdf Statistics statistical power eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 51 59 article Susanne Mayr susanne.mayr@uni-duesseldorf.de 1 Edgar Erdfelder erdfelder@psychologie.uni-mannheim.de 2 Axel Buchner axel.buchner@uni-duesseldorf.de 1 Franz Faul ffaul@psychologie.uni-kiel.de 3 Heinrich-Heine-Universität, Düsseldorf, Germany Universität Mannheim, Mannheim, Germany Christian-Albrechts-Universität, Kiel, Germany The purpose of this paper is to promote statistical power analysis in the behavioral sciences by introducing the easy to use GPower software. GPower is a free general power analysis program available in two essentially equivalent versions, one designed for Macintosh OS/OS X and the other for MS-DOS/Windows platforms. Psychological research examples are presented to illustrate the various features of the GPower software. In particular, a priori, post-hoc, and compromise power analyses for t-tests, F-tests, and chi-2-tests will be demonstrated. For all examples, the underlying statistical concepts as well as the implementation in GPower will be described. http://www.tqmp.org/Content/vol03-2/p051/p051.pdf Statistics statistical power eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 60 62 article Denis Cousineau denis.cousineau@umontreal.ca 1 Université de Montréal We show how to compute the power of a 2-group t test using SPSS or Mathematica. To do so, it is necessary to estimate the hypothetical effect size, if an effect is to be found. http://www.tqmp.org/Content/vol03-2/p060/p060.pdf Statistics statistical power t test eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 63 69 article Sébastien Hélie helies@rpi.edu 1 Rensselaer Polytechnic Institute This paper presents a graphical way of interpreting effect sizes when more than two groups are involved in a statistical analysis. This method uses noncentral distributions to specify the alternative hypothesis, and the statistical power can thus be directly computed. This principle is illustrated using the chi-squared distribution and the F distribution. Examples of chi-squared and ANOVA statistical tests are provided to further illustrate the point. It is concluded that power analyses are an essential part of statistical analysis, and that using noncentral distributions provides an argument in favour of using a factorial ANOVA over multiple t tests. http://www.tqmp.org/Content/vol03-2/p063/p063.pdf Statistics statistical power chi-square and anova eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 70 78 article Sylvain Chartier sylvain.chartier@uottawa.ca 1 Jean François Allaire jfallaire@ssss.gouv.qc.ca 2 University of Ottawa Institut Philippe Pinel de Montréal Power is often overlooked in designing multivariate studies for the simple reason that it is believed to be too complicated. In this paper, it is shown that power estimation in multivariate analysis of variance (MANOVA) can be approximated using a F distribution for the three popular statistics (Hotelling-Lawley trace, Pillai-Bartlett trace, Wilk`s likelihood ratio). Consequently, the same procedure, as in any statistical test, can be used: computation of the critical F value, computation of the noncentral parameter (as a function of the effect size) and finally estimation of power using a noncentral F distribution. Various numerical examples are provided which help to understand and to apply the method. Problems related to post hoc power estimation are discussed. http://www.tqmp.org/Content/vol03-2/p070/p070.pdf Statistics statistical power multivariate analysis of variance eng TQMP Tutorials in Quantitative Methods for Psychology 1913-4126 2007-09-01 3 2 79 79 article Jacob Cohen jacob.cohen@nyu.edu 1 New York University One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for 8 standard statistical tests: (1) the difference between independent means, (2) the significance of a product-moment correlation, (3) the difference between independent rs, (4) the sign test, (5) the difference between independent proportions, (6) chi-square tests for goodness of fit and contingency tables, (7) 1-way analysis of variance (ANOVA), and (8) the significance of a multiple or multiple partial correlation. http://www.tqmp.org/Content/vol03-2/p079/p079.pdf Statistics statistical power t-test, correlation and analysis of variance