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State-Dependence Effects in Surveys

Author

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  • Martijn G. de Jong

    (Erasmus School of Economics, Erasmus University, 3000 DR Rotterdam, The Netherlands)

  • Donald R. Lehmann

    (Columbia Business School, Columbia University, New York, New York 10027)

  • Oded Netzer

    (Columbia Business School, Columbia University, New York, New York 10027)

Abstract

In recent years academic research has focused on understanding and modeling the survey response process. This paper examines an understudied systematic response tendency in surveys: the extent to which observed responses are subject to state dependence, i.e., response carryover from one item to another independent of specific item content. We develop a statistical model that simultaneously accounts for state dependence, item content, and scale usage heterogeneity. The paper explores how state dependence varies by response category, item characteristics, item sequence, respondent characteristics, and whether it becomes stronger as the survey progresses. Two empirical applications provide evidence of substantial and significant state dependence. We find that the degree of state dependence depends on item characteristics and item sequence, and it varies across individuals and countries. The article demonstrates that ignoring state dependence may affect reliability and predictive validity, and it provides recommendations for survey researchers.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormksc:v:31:y:2012:i:5:p:838-854
    DOI: 10.1287/mksc.1120.0722
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    3. Salim Moussa, 2016. "A two-step item response theory procedure for a better measurement of marketing constructs," Journal of Marketing Analytics, Palgrave Macmillan, vol. 4(1), pages 28-50, March.
    4. Arpan Kumar Kar & Amit Kumar Kushwaha, 2023. "Facilitators and Barriers of Artificial Intelligence Adoption in Business – Insights from Opinions Using Big Data Analytics," Information Systems Frontiers, Springer, vol. 25(4), pages 1351-1374, August.
    5. Xiaoyuan Wang & Yan Liu, 2020. "Explaining Consumer Heterogeneity in Structural State-Dependence," Sustainability, MDPI, vol. 12(7), pages 1-13, March.
    6. 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.
    7. Oscar A. Martínez-Martínez & Araceli Ramírez-López, 2018. "Walkability and the built environment: validation of the Neighborhood Environment Walkability Scale (NEWS) for urban areas in Mexico," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(2), pages 703-718, March.

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