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Modeling anchoring effects in sequential Likert scale questions

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  • Marcin Hitczenko

Abstract

Surveys in many different research fields rely on sequences of Likert scale questions to assess individuals' general attitudes toward a set of related topics. Most analyses of responses to such a series do not take into account the potential measurement error introduced by the context effect we dub \"sequential anchoring,\" which occurs when the rating for one question influences the rating given to the following question by favoring similar ratings. The presence of sequential anchoring can cause systematic bias in the study of relative ratings. We develop a latent-variable framework for question responses that capitalizes on different question orderings in the survey to identify the presence of sequential anchoring. We propose a parameter estimation algorithm and run simulations to test its effectiveness for different data-generating processes, sample sizes, and orderings. Finally, the model is applied to data in which eight payment instruments are rated on a five-point scale for each of six payment characteristics in the 2012 Survey of Consumer Payment Choice. We find consistent evidence of sequential anchoring, resulting in sizable differences in properties of relative ratings for certain instruments.

Suggested Citation

  • Marcin Hitczenko, 2013. "Modeling anchoring effects in sequential Likert scale questions," Working Papers 13-15, Federal Reserve Bank of Boston.
    • Handle: RePEc:fip:fedbwp:13-15
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    References listed on IDEAS

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    1. Schuh, Scott & Stavins, Joanna, 2010. "Why are (some) consumers (finally) writing fewer checks? The role of payment characteristics," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1745-1758, August.
    2. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    3. Jianhua Z. Huang & Naiping Liu & Mohsen Pourahmadi & Linxu Liu, 2006. "Covariance matrix selection and estimation via penalised normal likelihood," Biometrika, Biometrika Trust, vol. 93(1), pages 85-98, March.
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    Cited by:

    1. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2013. "The 2010 Survey of Consumer Payment Choice: Technical Appendix," Consumer Payments Research Data Reports 2013-03, Federal Reserve Bank of Atlanta.
    2. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2014. "The 2011 and 2012 Surveys of Consumer Payment Choice: technical appendix," Research Data Report 14-2, Federal Reserve Bank of Boston.
    3. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2016. "The 2014 Survey of Consumer Payment Choice: Technical Appendix," Consumer Payments Research Data Reports 2016-04, Federal Reserve Bank of Atlanta.
    4. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2015. "The 2013 Survey of Consumer Payment Choice: technical appendix," Research Data Report 15-5, Federal Reserve Bank of Boston.
    5. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2017. "The 2015 Survey of Consumer Payment Choice: technical appendix," Research Data Report 17-4, Federal Reserve Bank of Boston.
    6. Marco Angrisani & Kevin Foster & Marcin Hitczenko, 2020. "The 2016 and 2017 Surveys of Consumer Payment Choice: Technical Appendix," Consumer Payments Research Data Reports 2018-4, Federal Reserve Bank of Atlanta.

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    More about this item

    Keywords

    survey bias; latent variable models; EM algorithm; Survey of Consumer Payment Choice;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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