Suppression Situations in Multiple Linear Regression

Autor/in	Shieh, Gwowen
Titel	Suppression Situations in Multiple Linear Regression
Quelle	In: Educational and Psychological Measurement, 66 (2006) 3, S.435-447 (13 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164405278584
Schlagwörter	Multiple Regression Analysis; Modeling (Psychology); Predictor Variables; Correlation; Models; Statistical Analysis + Suchen Sie Ihr Suchwort? Modeling; Modelling; Modellierung; Prädiktor; Korrelation; Analogiemodell; Statistische Analyse
Abstract	This article proposes alternative expressions for the two most prevailing definitions of suppression without resorting to the standardized regression modeling. The formulation provides a simple basis for the examination of their relationship. For the two-predictor regression, the author demonstrates that the previous results in the literature are incomplete and oversimplified. The proposed approach also allows a natural extension for multiple regression with more than two predictor variables. It is shown that the conditions under which both types of suppression can occur are not fully congruent with the significance of the partial "F" test. This implies that all the standard variable selection techniques--backward elimination, forward selection, and stepwise regression procedures--can fail to detect suppression situations. This also explains the controversial findings in the redundancy or importance of correlated variables in applied settings. Furthermore, informative visual representations of various aspects of these phenomena are provided. (Contains 1 table and 3 figures.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10