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The Stability of Individual Response Styles

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  • B. WEIJTERS
  • M. GEUENS
  • N. SCHILLEWAERT

Abstract

The current study addresses the stability of individual response styles. In contrast with previous studies, we set up a dedicated data collection, where the same respondents filled out two questionnaires consisting of independent sets of randomly sampled questionnaire items. Between data collections, there was a one year time gap. We simultaneously model four response styles that capture the major directional biases in questionnaire responses: acquiescence, disacquiescence, midpoint and extreme response style. The results provide conclusive evidence that response styles have an important stable component, only a small part of which can be explained by demographics. The meaning and implications of these findings are discussed.

Suggested Citation

  • B. Weijters & M. Geuens & N. Schillewaert, 2008. "The Stability of Individual Response Styles," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/547, Ghent University, Faculty of Economics and Business Administration.
    • Handle: RePEc:rug:rugwps:08/547
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    File URL: http://wps-feb.ugent.be/Papers/wp_08_547.pdf
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    References listed on IDEAS

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    Cited by:

    1. Eva Vlimmeren & Guy B. D. Moors & John P. T. M. Gelissen, 2017. "Clusters of cultures: diversity in meaning of family value and gender role items across Europe," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2737-2760, November.
    2. Anne Thissen-Roe & David Thissen, 2013. "A Two-Decision Model for Responses to Likert-Type Items," Journal of Educational and Behavioral Statistics, , vol. 38(5), pages 522-547, October.
    3. Sonja C. Kassenboehmer & Stefanie Schurer, 2018. "Survey item-response behavior as an imperfect proxy for unobserved ability: Theory and application," Working Papers 2018-035, Human Capital and Economic Opportunity Working Group.
    4. Dirk Tempelaar & Bart Rienties & Quan Nguyen, 2020. "Subjective data, objective data and the role of bias in predictive modelling: Lessons from a dispositional learning analytics application," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-29, June.
    5. Alice Barth & Andreas Schmitz, 2018. "Response quality and ideological dispositions: an integrative approach using geometric and classifying techniques," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(1), pages 175-194, January.
    6. Baumgartner, Hans & Weijters, Bert, 2019. "Measurement in Marketing," Foundations and Trends(R) in Marketing, now publishers, vol. 12(4), pages 278–400-2, December.

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