Computation of Effect Size for Moderating Effects of Categorical Variables in Multiple Regression

Autor/inn/en	Aguinis, Herman; Pierce, Charles A.
Titel	Computation of Effect Size for Moderating Effects of Categorical Variables in Multiple Regression
Quelle	In: Applied Psychological Measurement, 30 (2006) 5, S.440-442 (3 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621606286314
Schlagwörter	Effect Size; Multiple Regression Analysis; Predictor Variables; Error of Measurement; Computation; Computer Software + Suchen Sie Ihr Suchwort? Prädiktor; Messfehler
Abstract	The computation and reporting of effect size estimates is becoming the norm in many journals in psychology and related disciplines. Despite the increased importance of effect sizes, researchers may not report them or may report inaccurate values because of a lack of appropriate computational tools. For instance, Pierce, Block, and Aguinis (2004) provided examples of articles published in prestigious journals such as "Psychological Science", "Developmental Psychology", "Journal of Educational Psychology", and "Journal of Abnormal Psychology", in which researchers erroneously reported partial eta-squared values as representing classical eta-squared values. Here, the authors discuss the reasons behind the inaccuracy of values. One likely reason is that most commercially available statistical software packages provide only a limited number of effect-size estimates. Among other things, the authors discuss the effect-size metric f[squared], which is used in moderated multiple regression (MMR) to describe the strength of a moderating effect. (ERIC).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10