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Exhaustive or Exhausting? Evidence on Respondent Fatigue in Long Surveys

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  • Dahyeon Jeong
  • Shilpa Aggarwal
  • Jonathan Robinson
  • Naresh Kumar
  • Alan Spearot
  • David Sungho Park

Abstract

Living standards measurement surveys require sustained attention for several hours. We quantify survey fatigue by randomizing the order of questions in 2-3 hour-long in-person surveys. An additional hour of survey time increases the probability that a respondent skips a question by 10-64%. Because skips are more common, the total monetary value of aggregated categories such as assets or expenditures declines as the survey goes on, and this effect is sizeable for some categories: for example, an extra hour of survey time lowers food expenditures by 25%. We find similar effect sizes within phone surveys in which respondents were already familiar with questions, suggesting that cognitive burden may be a key driver of survey fatigue.

Suggested Citation

  • Dahyeon Jeong & Shilpa Aggarwal & Jonathan Robinson & Naresh Kumar & Alan Spearot & David Sungho Park, 2022. "Exhaustive or Exhausting? Evidence on Respondent Fatigue in Long Surveys," NBER Working Papers 30439, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30439
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    References listed on IDEAS

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    1. Anderson, Michael L, 2008. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt15n8j26f, Department of Agricultural & Resource Economics, UC Berkeley.
    2. Talip Kilic & Thomas Pave Sohnesen, 2019. "Same Question But Different Answer: Experimental Evidence on Questionnaire Design's Impact on Poverty Measured by Proxies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 65(1), pages 144-165, March.
    3. Brzozowski, Matthew & Crossley, Thomas F. & Winter, Joachim K., 2017. "A comparison of recall and diary food expenditure data," Food Policy, Elsevier, vol. 72(C), pages 53-61.
    4. Anderson, Michael L., 2008. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1481-1495.
    5. Abay,Kibrom A. & Berhane,Guush & Hoddinott,John & Hirfrfot,Kibrom Tafere, 2021. "Assessing Response Fatigue in Phone Surveys : Experimental Evidence on Dietary Diversity in Ethiopia," Policy Research Working Paper Series 9636, The World Bank.
    6. David Sungho Park & Shilpa Aggarwal & Dahyeon Jeong & Naresh Kumar & Jonathan Robinson & Alan Spearot, 2021. "Private but Misunderstood? Evidence on Measuring Intimate Partner Violence via Self-Interviewing in Rural Liberia and Malawi," NBER Working Papers 29584, National Bureau of Economic Research, Inc.
    7. Ambler, Kate & Herskowitz, Sylvan & Maredia, Mywish K., 2021. "Are we done yet? Response fatigue and rural livelihoods," Journal of Development Economics, Elsevier, vol. 153(C).
    8. Rachid Laaja & Karen Macours, 2021. "Measuring Skills in Developing Countries," Journal of Human Resources, University of Wisconsin Press, vol. 56(4), pages 1254-1295.
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    Cited by:

    1. Galiani, Sebastian & Gertler, Paul & Navajas-Ahumada, Camila, 2022. "Trust and saving in financial institutions by the poor," Journal of Development Economics, Elsevier, vol. 159(C).

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    More about this item

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

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