Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour

Ron Johnston, Kelvyn Jones and David Manley
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Kelvyn Jones: University of Bristol
David Manley: University of Bristol

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 4, No 26, 1957-1976

Abstract: 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.

Keywords: Regression analysis; Confounding; Collinearity; Voting behaviour (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (51)

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DOI: 10.1007/s11135-017-0584-6

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