Working With Missing Values.

Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation,...

Descripción completa

Detalles Bibliográficos
Publicado en:Journal of Marriage & Family Vol. 67; no. 4; pp. 1012 - 1029
Autor principal: Acock, Alan C.
Formato: Artículo
Publicado: Wiley-Blackwell November 2005
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=507838168&site=ehost-live
header:
  @attributes:
    shortDbName: ssf
    uiTerm: 507838168
    longDbName: Social Sciences Full Text (H.W. Wilson)
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00222445
        JMA
      jtl: Journal of Marriage & Family
      issn: 00222445
      maglogo: N
    pubinfo:
      dt: November 2005
      vid: 67
      iid: 4
      pid: 480
      pub: Wiley-Blackwell
    artinfo:
      ui:
        507838168
        10.1111/j.1741-3737.2005.00191.x
      ppf: 1012
      ppct: 17
      formats:
        fmt:
          – @attributes:
              type: T
          – @attributes:
              type: P
              size: 151KB
      tig:
        atl: Working With Missing Values.
      aug:
        au: Acock, Alan C.
      su:
        Missing observations (Statistics)
        Families -- Research
      sug:
        subj:
          Missing observations (Statistics)
          Families -- Research
      ab: Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood estimation. The effects of missing values are illustrated for a linear model, and a series of recommendations is provided. When missing values cannot be avoided, multiple imputation and full information methods offer substantial improvements over traditional approaches. Selected results using SPSS, NORM, Stata (mvis/micombine), and Mplus are included as is a table of available software and an appendix with examples of programs for Stata and Mplus. Reprinted by permission of the publisher.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: N
    holdings:
      @attributes:
        islocal: N