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Generalized eta squared for multiple comparisons on between-groups designs
Psychological and educational researchers are experiencing many practical difficulties in following the guidelines of the American Psychological Association (APA) for their statistical analyses: one such difficulty is the reporting of an effect-size measure along with each test of statistical signif...
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Published in: | Psicothema 2016-08, Vol.28 (3), p.340-345 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that cite this one |
Online Access: | Get full text |
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Summary: | Psychological and educational researchers are experiencing many practical difficulties in following the guidelines of the American Psychological Association (APA) for their statistical analyses: one such difficulty is the reporting of an effect-size measure along with each test of statistical significance (APA, 2010). The problem is exacerbated when researchers focus on contrast analysis instead of omnibus tests and when the Type-I error rate per comparison has to be adjusted.
Several reasons for this problem are discussed, with emphasis on the facts that researchers may be presented with too many optional effect-size measures with varying degrees of adequacy in several designs, and common statistical packages fail to provide appropriate effect-size measures for contrast analysis.
This study proposes specific procedures (also implemented in spreadsheets) to compute generalized eta squared for various kinds of hypotheses, either general or specific, for one-factor and factorial between-group designs, and with manipulated and/or measured factors.
Finally, conclusions are drawn concerning the need to take into account the kind of design and the kind of hypothesis in order to calculate comparable effect-size indexes across different types of studies and to prevent an overestimation of effect size. |
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ISSN: | 0214-9915 1886-144X |
DOI: | 10.7334/psicothema2015.124 |