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Erscheinungsjahr: 
2024
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[Journal:] Statistical Papers [ISSN:] 1613-9798 [Volume:] 65 [Issue:] 6 [Publisher:] Springer Berlin Heidelberg [Place:] Berlin/Heidelberg [Year:] 2024 [Pages:] 3971-3979
Verlag: 
Springer Berlin Heidelberg, Berlin/Heidelberg
Zusammenfassung: 
Abstract The size of the effect of the difference in two groups with respect to a variable of interest may be estimated by the classical Cohen’s d . A recently proposed generalized estimator allows conditioning on further independent variables within the framework of a linear regression model. In this note, it is demonstrated how unbiased estimation of the effect size parameter together with a corresponding standard error may be obtained based on the non-central t distribution. The portrayed estimator may be considered as a natural generalization of the unbiased Hedges’ g . In addition, confidence interval estimation for the unknown parameter is demonstrated by applying the so-called inversion confidence interval principle. The regarded properties collapse to already known ones in case of absence of any additional independent variables. The stated remarks are illustrated with a publicly available data set.
Schlagwörter: 
Effect size
Cohen’s d
Linear regression
Non-central t distribution
Confidence interval
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