Synthetic Multiple-Imputation Procedure for Multistage Complex Samples

Zhou Hanzhi (), Elliott Michael R. () and Raghunathan Trivellore E. ()
Additional contact information
Zhou Hanzhi: Mathematica Policy Research, Princeton, NJ 08543, USA.
Elliott Michael R.: Dept. of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI USA 48109; Survey Methodology Program, Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48109, USA.
Raghunathan Trivellore E.: Dept. of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI USA 48109; Survey Methodology Program, Institute for Social Research, University of Michigan, 426 Thompson St., Ann Arbor, MI 48109, USA.

Journal of Official Statistics, 2016, vol. 32, issue 1, 231-256

Abstract: Multiple imputation (MI) is commonly used when item-level missing data are present. However, MI requires that survey design information be built into the imputation models. For multistage stratified clustered designs, this requires dummy variables to represent strata as well as primary sampling units (PSUs) nested within each stratum in the imputation model. Such a modeling strategy is not only operationally burdensome but also inferentially inefficient when there are many strata in the sample design. Complexity only increases when sampling weights need to be modeled. This article develops a generalpurpose analytic strategy for population inference from complex sample designs with item-level missingness. In a simulation study, the proposed procedures demonstrate efficient estimation and good coverage properties. We also consider an application to accommodate missing body mass index (BMI) data in the analysis of BMI percentiles using National Health and Nutrition Examination Survey (NHANES) III data. We argue that the proposed methods offer an easy-to-implement solution to problems that are not well-handled by current MI techniques. Note that, while the proposed method borrows from the MI framework to develop its inferential methods, it is not designed as an alternative strategy to release multiply imputed datasets for complex sample design data, but rather as an analytic strategy in and of itself.

Keywords: Finite population Bayesian bootstrap; Haldane prior; stratified sample; clustered sample; sample weights (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jos-2016-0011 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:32:y:2016:i:1:p:231-256:n:11

DOI: 10.1515/jos-2016-0011

Access Statistics for this article

Journal of Official Statistics is currently edited by Annica Isaksson and Ingegerd Jansson

More articles in Journal of Official Statistics from Sciendo
Bibliographic data for series maintained by Peter Golla ().