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

Author

Listed:
  • Ron Johnston

    (University of Bristol)

  • Kelvyn Jones

    (University of Bristol)

  • David Manley

    (University of Bristol)

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.

Suggested Citation

  • Ron Johnston & Kelvyn Jones & David Manley, 2018. "Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(4), pages 1957-1976, July.
    • Handle: RePEc:spr:qualqt:v:52:y:2018:i:4:d:10.1007_s11135-017-0584-6
    DOI: 10.1007/s11135-017-0584-6
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    References listed on IDEAS

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    1. Roberto Franzosi, 1994. "Outside and inside the regression “black box” from exploratory to interior data analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 28(1), pages 21-53, February.
    2. Imai, Kosuke & Keele, Luke & Tingley, Dustin & Yamamoto, Teppei, 2011. "Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies," American Political Science Review, Cambridge University Press, vol. 105(4), pages 765-789, November.
    3. Evans, Geoffrey & Chzhen, Kat, 2016. "Re-evaluating the Valence Model of Political Choice," Political Science Research and Methods, Cambridge University Press, vol. 4(1), pages 199-220, January.
    4. Sanint, Luis R., 1982. "Applying Principal Components Regression Analysis to Time Series Demand Estimation," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 34(3), pages 1-7, July.
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