The Normal-Theory and Asymptotic Distribution-Free (ADF) Covariance Matrix of Standardized Regression Coefficients: Theoretical Extensions and Finite Sample Behavior

Jeff Jones and Niels Waller ()

Psychometrika, 2015, vol. 80, issue 2, 365-378

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. Copyright The Psychometric Society 2015

Keywords: standardized regression coefficients; multiple regression; ADF; confidence intervals (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11336-013-9380-y

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