GEN_ETA2: A SAS ® Macro for Computing the Generalized Eta-Squared Effect Size Associated with Analysis of Variance Models
ABSTRACT Measures of effect size are recommended to communicate information on the strength of relationships between variables. Such information supplements the reject / fail-to-reject decision obtained in statistical hypothesis testing. The choice of an effect size for ANOVA models can be confusing because indices may differ depending on the research design as well as the magnitude of the effect. Olejnik and Algina (2003) proposed the generalized eta-squared effect size which is comparable across a wide variety of research designs. This paper provides a SAS macro for computing the generalized eta-squared effect size associated with analysis of variance models by utilizing data from PROC GLM ODS tables. The paper provides the macro programming language, as well as results from an executed example of the macro.
Full-textDOI: · Available from: Patrice S. Rasmussen, May 13, 2015
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ABSTRACT: Analyzes and clarifies the differences between eta-squared and partial eta-squared in fixed factor analysis of variance (ANOVA) designs. The formulas are presented and discussed, and an example is presented along with the appropriate use and meaning of the 2 coefficients. Finally, a general discussion of the use of eta-squared and partial eta-squared is provided. (PsycINFO Database Record (c) 2012 APA, all rights reserved)Educational and Psychological Measurement 04/1973; DOI:10.1177/001316447303300111 · 1.17 Impact Factor