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Activity Number:
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414
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2003 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #301017 |
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Title:
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Semiparametric Approach for Multiple Imputation of Unobserved Values in Longitudinal Studies
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Author(s):
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Yulei He*+ and Trivellore E. Raghunathan
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Companies:
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University of Michigan and Institute for Social Research
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Address:
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1586 Murfin Ave. Apt 37, Ann Arbor, MI, 48105,
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Keywords:
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Bayesian framework ; Gibbs sampling ; missing data ; PSID ; spline models ; unobserved data
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Abstract:
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Unbalanced data, where not all the individuals are observed at the same time points, is a common feature in many longitudinal studies. The primary reason is due to nonparticipation even though the original design may have called for measuring all the individuals at the same time. Alternatively, by design the data may not be collected from all the individuals at every wave of data collection. A case study that motivated this research involved relating the wealth of the parents during the critical developmental age on their children's development. Similar problems occur when the longitudinal data are used to characterize the exposure information over a life course or during a certain critical period. Multiple imputation approach provides a framework for handling missing data in such instances. This paper discusses a semiparametric approach using spline models, within a Bayesian framework, to create imputations. Gibbs sampling is used to obtain the draws. We illustrate the proposed method by applying it to the data set of Panel Study of Income Dynamics (PSID). A simulation study evaluates the repeated sampling properties of the inferences obtained using this approach.
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