DATA CLEANING: LONGITUDINAL STUDY CROSS-VISIT CHECKS.

SAS global forum Pub Date : 2014-03-01
Lauren Parlett
{"title":"DATA CLEANING: LONGITUDINAL STUDY CROSS-VISIT CHECKS.","authors":"Lauren Parlett","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Cross-visit checks are a vital part of data cleaning for longitudinal studies. The nature of longitudinal studies encourages repeatedly collecting the same information. Sometimes, these variables are expected to remain static, go away, increase, or decrease over time. This presentation reviews the naïve and the better approaches at handling one-variable and two-variable consistency checks. For a single-variable check, the better approach features the new ALLCOMB function, introduced in SAS® 9.2. For a two-variable check, the better approach uses a BY PROCESSING variable to flag inconsistencies. This paper will provide you the tools to enhance your longitudinal data cleaning process.</p>","PeriodicalId":90722,"journal":{"name":"SAS global forum","volume":"2014 ","pages":"1314"},"PeriodicalIF":0.0000,"publicationDate":"2014-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4224286/pdf/nihms595284.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAS global forum","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

Abstract

Cross-visit checks are a vital part of data cleaning for longitudinal studies. The nature of longitudinal studies encourages repeatedly collecting the same information. Sometimes, these variables are expected to remain static, go away, increase, or decrease over time. This presentation reviews the naïve and the better approaches at handling one-variable and two-variable consistency checks. For a single-variable check, the better approach features the new ALLCOMB function, introduced in SAS® 9.2. For a two-variable check, the better approach uses a BY PROCESSING variable to flag inconsistencies. This paper will provide you the tools to enhance your longitudinal data cleaning process.

数据清理:纵向研究交叉访问检查。
交叉访问检查是纵向研究数据清理的重要组成部分。纵向研究的本质鼓励重复收集相同的信息。有时,这些变量会随着时间的推移保持静态、消失、增加或减少。本演讲回顾了naïve以及处理单变量和双变量一致性检查的更好方法。对于单变量检查,更好的方法是SAS®9.2中引入的新的ALLCOMB功能。对于双变量检查,更好的方法是使用BY PROCESSING变量标记不一致性。本文将为您提供增强纵向数据清理过程的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信