Linear regression has gained widespread popularity in the social sciences. However, many applications of linear regression have been in situations in which the model data are collinear or ‘ill-conditioned.’ Collinearity renders regression estimates with inflated standard errors. In this paper, we present a method for precisely identifying coefficient estimates that are ill-conditioned, as well as those that are not involved, or only marginally involved in a linear dependency. Diagnostic tools are presented for a hypothetical regression model with ordinary least squares (OLS). It is hoped that practicing researchers will more readily incorporate these diagnostics into their analyses.Accessed 17,081 times on https://pareonline.net from June 18, 2008 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right.
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Callaghan, Karen J. and Chen, Jie
"Revisiting the Collinear Data Problem: An Assessment of Estimator 'Ill-Conditioning' in Linear Regression,"
Practical Assessment, Research, and Evaluation: Vol. 13
, Article 5.
Available at: https://scholarworks.umass.edu/pare/vol13/iss1/5