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Abstract Details
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Activity Number:
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80
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Type:
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Contributed
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #302589 |
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Title:
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IRT Summarized Pattern Mixture Model for Data Not Missing at Random
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Author(s):
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Jian Zhu*+ and Trivellore Raghunathan
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Companies:
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University of Michigan and University of Michigan
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Address:
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Department of Biostatistics, Ann Arbor, MI, 48109, USA
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Keywords:
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multiple imputation ;
item response theory ;
nonignorable missing data
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Abstract:
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Imputation for large scale study when data are missing not at random is generally difficult, especially when there are a large number of items with general missing patterns. This paper is aimed to investigate several pattern mixture models for such data. The patterns are determined by summarized latent information from response indicators assuming item response models. Both Bayesian models and sequential regression imputation methods were considered. Simulation studies based on such pattern mixture models were conducted for multivariate normally distributed data with different missing mechanisms. Performance of the pattern mixture models compared to models assuming data are missing at random was examined.
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