| 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.
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