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Three-Form Split Questionnaire Design for Panel Surveys

Author

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  • Imbriano Paul M.

    (Department of Biostatistics, School of Public Health, University of Michigan, 1415 Washington Heights Ann Arbor, MI 48109-2029, U.S.A. Emails: pimbri@umich.edu)

  • Raghunathan Trivellore E.

    (Survey Research Center, Institute for Social Research, University of Michigan, 426 Thompson St, Ann Arbor, MI 48104, U.S.A.)

Abstract

Longitudinal or panel surveys are effective tools for measuring individual level changes in the outcome variables and their correlates. One drawback of these studies is dropout or nonresponse, potentially leading to biased results. One of the main reasons for dropout is the burden of repeatedly responding to long questionnaires. Advancements in survey administration methodology and multiple imputation software now make it possible for planned missing data designs to be implemented for improving the data quality through a reduction in survey length. Many papers have discussed implementing a planned missing data study using a split questionnaire design in the cross-sectional setting, but development of these designs in a longitudinal study has been limited. Using simulations and data from the Health and Retirement Study (HRS), we compare the performance of several methods for administering a split questionnaire design in the longitudinal setting. The results suggest that the optimal design depends on the data structure and estimand of interest. These factors must be taken into account when designing a longitudinal study with planned missing data.

Suggested Citation

  • Imbriano Paul M. & Raghunathan Trivellore E., 2020. "Three-Form Split Questionnaire Design for Panel Surveys," Journal of Official Statistics, Sciendo, vol. 36(4), pages 827-854, December.
    • Handle: RePEc:vrs:offsta:v:36:y:2020:i:4:p:827-854:n:5
    DOI: 10.2478/jos-2020-0040
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    References listed on IDEAS

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    1. Jeffrey E. Zabel, 1998. "An Analysis of Attrition in the Panel Study of Income Dynamics and the Survey of Income and Program Participation with an Application to a Model of Labor Market Behavior," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 479-506.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. James O. Chipperfield & Margo L. Barr & David. G. Steel, 2018. "Split Questionnaire Designs: collecting only the data that you need through MCAR and MAR designs," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(8), pages 1465-1475, June.
    4. Elisabeth Deutskens & Ad Jong & Ko Ruyter & Martin Wetzels, 2006. "Comparing the generalizability of online and mail surveys in cross-national service quality research," Marketing Letters, Springer, vol. 17(2), pages 119-136, April.
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