Work

Working with missing data in large-scale assessments

Year: 2025

Type: article

Abstract: 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.

Source: Large-scale Assessments in Education

DOI: 10.1186/s40536-025-00248-9

Authors: Francis Huang, Brian Keller

Institution: University of Missouri

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: diamond

Funder: Institute of Education Sciences

Awards: Multiple Imputation Procedures for Multilevel Data, Model-based Multiple Imputation for Multilevel Data: Methodological Extensions and Software Enhancements