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Abstract:
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Advancements in survey administration methodology and multiple imputation software now make it possible for planned missing data designs to be implemented for improving data quality through the reduction in survey length. Many papers have discussed implementing a cross sectional study with planned missing data using a split-questionnaire design, but the research in applying these methods to a longitudinal study has been limited. Using simulations and data from the Health and Retirement Study, we compared the performance of several methods for administering a split-questionnaire design in the longitudinal setting. Our findings suggest that the optimal design depends on the data structure, specifically on both the within-wave and between-wave variable correlations, and there exists a trade-off between the complexity and robustness of the designs. These factors should be taken into account when designing a longitudinal study with planned missing data.
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