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

Details

• Alternative Title:
  Surv Stat
• Personal Author:
  He, Yulei ; Zhang, Guangyu
• 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.
• Subjects:
  Missing Data Multiple Imputation Surveys And Questionnaires
• Source:
  Surv Stat. 87:37-47
• Pubmed ID:
  37576783
• Pubmed Central ID:
  PMC10422982
• Document Type:
  Text
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/
• Genre:
  Journal Article
• Volume:
  87
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:5d7c9fe33bf3174ee4d26bfced61e41a152aced9c7bf5775ea7735aa15c8707702d5b4c2d9ea004d58575ee40cd5f27e1fde44e63334e035e136e11d4094a671
• Download URL:
  https://stacks.cdc.gov/view/cdc/132019/cdc_132019_DS1.pdf
• File Type:
  [PDF - 115.69 KB ]