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文章基本信息

  • 标题:JMASM 32: Multiple Imputation of Missing Multilevel, Longitudinal Data: A Case When Practical Considerations Trump Best Practices?
  • 本地全文:下载
  • 作者:Lloyd, Jennifer E. V. ; Obradović, Jelena ; Carpiano, Richard M.
  • 期刊名称:Journal of Modern Applied Statistical Methods
  • 出版年度:2013
  • 卷号:12
  • 期号:1
  • 页码:29
  • 出版社:Wayne State University
  • 摘要:A pedagogical tool is presented for applied researchers dealing with incomplete multilevel, longitudinal data. It explains why such data pose special challenges regarding missingness. Syntax created to perform a multiply-imputed growth modeling procedure in Stata Version 11 (StataCorp, 2009) is also described.
  • 关键词:Missing data; longitudinal data; multilevel data; multiple imputation; growth modeling; Stata
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