Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS)

Multiple imputation (MI) is a widely used analytic approach to address missing data problems. SAS(®) (SAS Institute Inc, Cary, N.C.) has established MI procedures including PROC MI and PROC MIANALYZE. We illustrate the use of these procedures for conducting MI analysis of complex survey data by an e...

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Detalles Bibliográficos
Autores principales: He, Yulei, Zhang, Guangyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422982/
https://www.ncbi.nlm.nih.gov/pubmed/37576783
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author He, Yulei
Zhang, Guangyu
author_facet He, Yulei
Zhang, Guangyu
author_sort He, Yulei
collection PubMed
description Multiple imputation (MI) is a widely used analytic approach to address missing data problems. SAS(®) (SAS Institute Inc, Cary, N.C.) has established MI procedures including PROC MI and PROC MIANALYZE. We illustrate the use of these procedures for conducting MI analysis of complex survey data by an example from RANDS. Section 1 contains the introduction. Section 2 includes some necessary methodological background. Section 3 shows a MI example with an arbitrary missing data pattern. Section 4 concludes the paper with a discussion.
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spelling pubmed-104229822023-08-12 Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS) He, Yulei Zhang, Guangyu Surv Stat Article Multiple imputation (MI) is a widely used analytic approach to address missing data problems. SAS(®) (SAS Institute Inc, Cary, N.C.) has established MI procedures including PROC MI and PROC MIANALYZE. We illustrate the use of these procedures for conducting MI analysis of complex survey data by an example from RANDS. Section 1 contains the introduction. Section 2 includes some necessary methodological background. Section 3 shows a MI example with an arbitrary missing data pattern. Section 4 concludes the paper with a discussion. 2023-01 /pmc/articles/PMC10422982/ /pubmed/37576783 Text en https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Article
He, Yulei
Zhang, Guangyu
Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS)
title Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS)
title_full Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS)
title_fullStr Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS)
title_full_unstemmed Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS)
title_short Multiple Imputation of Missing Complex Survey Data using SAS(®): A Brief Overview and An Example Based on the Research and Development Survey (RANDS)
title_sort multiple imputation of missing complex survey data using sas(®): a brief overview and an example based on the research and development survey (rands)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422982/
https://www.ncbi.nlm.nih.gov/pubmed/37576783
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