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<p>Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and the freely available Blimp program to impute and analyze multiply imputed datasets. </p>

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Working with missing data in large-scale assessments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月23日

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  • Francis Huang,
  • Brian Keller

摘要

Missing data are common with large scale assessments (LSAs). A typical approach to handling missing data with LSAs is the use of listwise deletion, despite decades of research showing that approach can be a suboptimal strategy resulting in biased estimates. In order to help researchers account for missing data, we provide a tutorial using R and the freely available Blimp program to impute and analyze multiply imputed datasets.