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Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data

Author

Listed:
  • Joachim Büschken

    (Catholic University of Eichstätt-Ingolstadt)

  • Ulf Böckenholt

    (Northwestern University)

  • Thomas Otter

    (Goethe University)

  • Daniel Stengel

    (GfK)

Abstract

Ideally, survey respondents read and understand survey instructions, questions, and response scales, and provide answers that carefully reflect their beliefs, attitudes, or knowledge. However, respondents may also arrive at their responses using cues or heuristics that facilitate the production of a response, but diminish the targeted information content. We use eye-tracking data as covariates in a Bayesian switching-mixture model to identify different response behaviors at the item–respondent level. The model distinguishes response behaviors that are predominantly influenced either positively or negatively by the previous response, and responses that reflect respondents’ preexisting knowledge and experiences of interest. We find that controlling for multiple types of adaptive response behaviors allows for a more informative analysis of survey data and respondents.

Suggested Citation

  • Joachim Büschken & Ulf Böckenholt & Thomas Otter & Daniel Stengel, 2022. "Better Information From Survey Data: Filtering Out State Dependence Using Eye-Tracking Data," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 620-665, June.
    • Handle: RePEc:spr:psycho:v:87:y:2022:i:2:d:10.1007_s11336-021-09814-w
    DOI: 10.1007/s11336-021-09814-w
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    References listed on IDEAS

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    1. Joachim Büschken & Thomas Otter & Greg M. Allenby, 2013. "The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis," Marketing Science, INFORMS, vol. 32(4), pages 533-553, July.
    2. Pasquale Valentini & Tonio Battista & Stefano Antonio Gattone, 2011. "Heterogeneity Measures in Customer Satisfaction Analysis," Journal of Classification, Springer;The Classification Society, vol. 28(1), pages 38-52, April.
    3. Eric Bradlow & Howard Wainer & Xiaohui Wang, 1999. "A Bayesian random effects model for testlets," Psychometrika, Springer;The Psychometric Society, vol. 64(2), pages 153-168, June.
    4. Kaminska, Olena & Foulsham, Tom, 2013. "Understanding sources of social desirability bias in different modes: evidence from eye-tracking," ISER Working Paper Series 2013-04, Institute for Social and Economic Research.
    5. Savannah Wei Shi & Michel Wedel & F. G. M. (Rik) Pieters, 2013. "Information Acquisition During Online Decision Making: A Model-Based Exploration Using Eye-Tracking Data," Management Science, INFORMS, vol. 59(5), pages 1009-1026, May.
    6. Fonseca, Jaime R.S., 2009. "Customer satisfaction study via a latent segment model," Journal of Retailing and Consumer Services, Elsevier, vol. 16(5), pages 352-359.
    7. Martijn G. de Jong & Donald R. Lehmann & Oded Netzer, 2012. "State-Dependence Effects in Surveys," Marketing Science, INFORMS, vol. 31(5), pages 838-854, September.
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