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

Jeff A. Jones, Niels G. Waller

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)365-378
Number of pages14
JournalPsychometrika
Volume80
Issue number2
DOIs
StatePublished - Jun 9 2015

Keywords

  • ADF, confidence intervals
  • multiple regression
  • standardized regression coefficients

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