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Activity Number: 212 - GOVT CSpeed 1
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 1:30 PM to 3:20 PM
Sponsor: Government Statistics Section
Abstract #318306
Title: Multiple Imputation of Missing Data with Skip-Pattern Covariates
Author(s): Guangyu Zhang* and Yulei He and Bill Cai and Chris Moriarity and Hee-Choon Shin and Van Parsons and Katherine Irimata
Companies: CDC and US Centers for Disease Control and Prevention and NCHS and NCHS and National Center for Health Statistics and CDC and NCHS
Keywords: missing data; skip pattern covariates ; survey

Multiple imputation (MI) is a widely used approach to address missing data issues in surveys. Variables included in MI have various distributional forms with different degrees of missingness. In this research we compare two approaches for MI when skip-pattern covariates with missing values exist. One approach imputes missing values in the skip-pattern variables only among applicable subjects. The other approach imputes skip-pattern covariates among all subjects while using different parameterizations on the skip-pattern variables; after imputation, skip-pattern variables for non-applicable cases are recoded so that data used for analysis don’t have erroneous values. A simulation study is conducted to compare these methods. Both approaches are applied to the 2015 and 2016 Research and Development Survey data from the National Center for Health Statistics, where missing data are imputed using income as the key outcome variable along with approximately 60 other covariates including several skip-pattern variables.

Authors who are presenting talks have a * after their name.

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