title: The Normal-Theory and Asymptotic Distribution-Free (ADF) Covariance Matrix of Standardized Regression Coefficients: Theoretical Extensions and Finite Sample Behavior.
abstract: Yuan and Chan (Psychometrika, 76, 670-690, 2011) recently showed how to compute the covariance matrix of standardized regression coefficients from covariances. In this paper, we describe a method for computing this covariance matrix from correlations. Next, we describe an asymptotic distribution-free (ADF; Browne in British Journal of Mathematical and Statistical Psychology, 37, 62-83, 1984) method for computing the covariance matrix of standardized regression coefficients. We show that the ADF method works well with nonnormal data in moderate-to-large samples using both simulated and real-data examples. R code (R Development Core Team, 2012) is available from the authors or through the Psychometrika online repository for supplementary materials.
authors.affiliation: Department of Psychology, University of Minnesota, 75 East River Road, Minneapolis, MN, 55455, USA.
authors.forename: Jeff A
authors.initials: JA
authors.lastname: Jones
authors.forename: Niels G
authors.initials: NG
authors.lastname: Waller
mesh_terms: D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D011594:Psychometrics; D012044:Regression Analysis; D013223:Statistics as Topic