![]() ![]() The goal is to present a practical primer to power analysis that complements other reviews of power analysis ( Cohen, 1992a, 1992b Faul, Erdfelder, Buchner & Lang, 2009 Faul, Erdfelder, Lang & Buchner, 2007 Maxwell, Kelley & Rausch, 2008) as well as more comprehensive and advanced textbooks ( Cohen, 1988 Liu, 2014). We will also discuss some important issues related to power analysis. We will focus mainly on between-subject designs, and we will limit our discussion of repeated-measures designs to the simplest case of two dependent groups. The focus is on simple experimental designs often encountered in social psychology, and we will provide illustrative examples throughout the article. This article aims to remind readers what power analysis is, why it matters, and when and how it should be used. If these chances are insufficient, they should consider changes that could increase the probability of observing a significant effect. One of the main benefits of power analysis when planning studies is that researchers become aware of their chances of finding an effect of interest. In the presence of publication bias, systematically performing studies that lack the power to detect effect sizes of interest results in a prevalence of false-positive findings in the literature ( Button et al., 2013 Maxwell, 2004). One reason for this is the recent replicability crisis in psychology one of the main culprits for the difficulty in replicating some results was that original studies were often underpowered to start with ( Asendorpf et al., 2013 Bakker, van Dijk & Wicherts, 2012 Swiatkowski & Dompnier, 2017). 1 However, interest in power analysis has increased considerably only during the last few years. Since then, many have recommended its use and suggested it as good research practice ( Wilkinson & TSFI, 1999) within the Null-Hypothesis Significance Testing (NHST) framework. It was pioneered in psychology more than fifty years ago by Jacob Cohen ( 1962, 1990). Power analysis is one of the most fundamental tools that researchers can use when planning studies. “When I finally stumbled onto power analysis, and managed to overcome the handicap of a background with no working math beyond high school algebra (to say nothing of mathematical statistics), it was as if I had died and gone to heaven.”. Applications of power analysis for more complex designs are briefly mentioned, and some important general issues related to power analysis are discussed. #STATISTICS GPOWER CORRELATION SAMPLE CALCULATOR CODE#Annotated code for the examples with R and dedicated computational tools are made freely available at a dedicated web page ( ). Illustrative practical examples based on G*Power and R packages are provided throughout the article. ![]() Special attention is given to the application of power analysis to moderation designs, considering both dichotomous and continuous predictors and moderators. ![]() The focus is on applications of power analysis for experimental designs often encountered in psychology, starting from simple two-group independent and paired groups and moving to one-way analysis of variance, factorial designs, contrast analysis, trend analysis, regression analysis, analysis of covariance, and mediation analysis. ![]() This contribution aims to remind readers what power analysis is, emphasize why it matters, and articulate when and how it should be used. Power analysis is an important tool to use when planning studies. ![]()
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