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Principled missing data methods for researchers

Dong, Yiran ; Peng, Chao-Ying Joanne

SpringerPlus, 2013-05, Vol.2 (1), p.222-222, Article 222 [Periódico revisado por pares]

Cham: Springer International Publishing

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  • Título:
    Principled missing data methods for researchers
  • Autor: Dong, Yiran ; Peng, Chao-Ying Joanne
  • Assuntos: Humanities and Social Sciences ; Methodology ; multidisciplinary ; Science ; Science (multidisciplinary) ; Social Sciences
  • É parte de: SpringerPlus, 2013-05, Vol.2 (1), p.222-222, Article 222
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.
  • Editor: Cham: Springer International Publishing
  • Idioma: Inglês
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