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Agree to agree: correcting acquiescence bias in the case of fully unbalanced scales with application to UK measurements of political beliefs

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  • Phil Swatton

    (University of Essex)

Abstract

A methodologically important area in political science is measuring the ideology of voters. This task can be difficult, and researchers often rely on ‘off the shelf’ datasets. Many of these datasets contain unbalanced Likert scales, which risk acquiescence bias. This paper proposes a strategy for dealing with this issue. I first demonstrate using two comparable datasets from the UK how unbalanced scales produce distorted distributions and can affect regression results. Then, building on past research that utilises factor analysis to eliminate the influence of acquiescence bias, I demonstrate how researchers can utilise a person intercept confirmatory factor analysis model to obtain factor scores corrected for acquiescence in the case of fully unbalanced scales. I conclude with practical recommendations for researchers and survey designers moving forward.

Suggested Citation

  • Phil Swatton, 2024. "Agree to agree: correcting acquiescence bias in the case of fully unbalanced scales with application to UK measurements of political beliefs," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5463-5482, December.
    • Handle: RePEc:spr:qualqt:v:58:y:2024:i:6:d:10.1007_s11135-024-01891-0
    DOI: 10.1007/s11135-024-01891-0
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    References listed on IDEAS

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    1. Geoffrey Evans & Anthony Heath, 1995. "The measurement of left-right and libertarian-authoritarian values: A comparison of balanced and unbalanced scales," Quality & Quantity: International Journal of Methodology, Springer, vol. 29(2), pages 191-206, May.
    2. Heath, Anthony & Evans, Geoffrey & Martin, Jean, 1994. "The Measurement of Core Beliefs and Values: The Development of Balanced Socialist/Laissez Faire and Libertarian/Authoritarian Scales," British Journal of Political Science, Cambridge University Press, vol. 24(1), pages 115-132, January.
    3. Rosseel, Yves, 2012. "lavaan: An R Package for Structural Equation Modeling," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i02).
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