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Working with missing data in large-scale assessments
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AI
DOI：10.1186/s40536-025-00248-9.png)
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Abstract
En 中文
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.
Keywords:
Missing data
Multiple imputation
Large scale assessments
Blimp
Journal
L
IF:
3
Papers:
82
Citations:
745