Loss of Power in Logistic, Ordinal Logistic, and Probit Regression When an Outcome Variable Is Coarsely Categorized

Autor/inn/en	Taylor, Aaron B.; West, Stephen G.; Aiken, Leona S.
Titel	Loss of Power in Logistic, Ordinal Logistic, and Probit Regression When an Outcome Variable Is Coarsely Categorized
Quelle	In: Educational and Psychological Measurement, 66 (2006) 2, S.228-239 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164405278580
Schlagwörter	Regression (Statistics); Classification; Monte Carlo Methods; Sample Size; Computation; Psychological Studies; Correlation + Suchen Sie Ihr Suchwort? Regression; Regressionsanalyse; Classification system; Klassifikation; Klassifikationssystem; Monte-Carlo-Methode; Korrelation
Abstract	Variables that have been coarsely categorized into a small number of ordered categories are often modeled as outcome variables in psychological research. The authors employ a Monte Carlo study to investigate the effects of this coarse categorization of dependent variables on power to detect true effects using three classes of regression models: ordinary least squares (OLS) regression, ordinal logistic regression, and ordinal probit regression. Both the loss of power and the increase in required sample size to regain the lost power are estimated. The loss of power and required sample size increase were substantial under conditions in which the coarsely categorized variable is highly skewed, has few categories (e.g., 2, 3), or both. Ordinal logistic and ordinal probit regression protect marginally better against power loss than does OLS regression. (Contains 1 figure and 2 tables.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10