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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1957–1976
Number of pages20
JournalQuality and Quantity
Issue number4
Early online date13 Nov 2017
DateAccepted/In press - 1 Sep 2017
DateE-pub ahead of print - 13 Nov 2017
DatePublished (current) - Jul 2018


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.

    Research areas

  • Collinearity, Confounding, Regression analysis, Voting behaviour

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