Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by
Ron Johnston1, Kelvyn Jones1, David Manley1
1School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS UK.
Abstract:
Many ecological- and individual-level analyses of voting behaviour use multiple regressions with a considerable number of independent variables but few discussions of their results pay any attention to the potential impact of inter-relationships among those independent variables-do they confound the regression parameters and hence their interpretation? Three empirical examples are deployed to address that question, with results which suggest considerable problems. Inter-relationships between variables, even if not approaching high collinearity, can have a substantial impact on regression model results and how they are interpreted in the light of prior expectations. Confounded relationships could be the norm and interpretations open to doubt, unless considerable care is applied in the analyses and an extended principal components method for doing that is introduced and exemplified.
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