{"title":"利用SAS®进行复杂调查数据缺失的多重补全:综述与以研发调查(rand)为例","authors":"Yulei He, Guangyu Zhang","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Multiple imputation (MI) is a widely used analytic approach to address missing data problems. SAS<sup>®</sup> (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.</p>","PeriodicalId":74894,"journal":{"name":"Survey statistician","volume":"87 ","pages":"37-47"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422982/pdf/","citationCount":"0","resultStr":"{\"title\":\"Multiple Imputation of Missing Complex Survey Data using SAS<sup>®</sup>: A Brief Overview and An Example Based on the Research and Development Survey (RANDS).\",\"authors\":\"Yulei He, Guangyu Zhang\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multiple imputation (MI) is a widely used analytic approach to address missing data problems. SAS<sup>®</sup> (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.</p>\",\"PeriodicalId\":74894,\"journal\":{\"name\":\"Survey statistician\",\"volume\":\"87 \",\"pages\":\"37-47\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422982/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Survey statistician\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Survey statistician","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
多重插值(Multiple imputation, MI)是一种广泛应用于解决数据缺失问题的分析方法。SAS®(SAS Institute Inc, Cary, N.C.)已经建立了MI程序，包括PROC MI和PROC MIANALYZE。我们通过rand的一个例子来说明这些程序对复杂调查数据进行MI分析的使用。第1节包含引言。第2节包括一些必要的方法背景。第3节展示了一个具有任意缺失数据模式的MI示例。第四部分对全文进行了总结和讨论。
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 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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