Confidence Intervals for an Effect Size Measure in Multiple Linear Regression

Autor/inn/en	Algina, James; Keselman, H. J.; Penfield, Randall D.
Titel	Confidence Intervals for an Effect Size Measure in Multiple Linear Regression
Quelle	In: Educational and Psychological Measurement, 67 (2007) 2, S.207-218 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164406292030
Schlagwörter	Probability; Intervals; Multiple Regression Analysis; Correlation; Sample Size; Effect Size; Regression (Statistics) + Suchen Sie Ihr Suchwort? Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Korrelation; Regression; Regressionsanalyse
Abstract	The increase in the squared multiple correlation coefficient ([Delta]R[squared]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. The coverage probability that an asymptotic and percentile bootstrap confidence interval includes [Delta][rho][squared] was investigated. As expected, coverage probability for the asymptotic confidence interval was often inadequate (outside the interval 0.925 to 0.975 for a 95% confidence interval), even when sample size was quite large (i.e., 200). However, adequate coverage probability for the confidence interval based on a bootstrap interval could typically be obtained with a sample size of 200 or less, and moreover, this accuracy was obtained with relatively small sample sizes (100 or less) with six or fewer predictors. (Contains 4 tables.) (Author).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10