Longitudinal studies can produce biased estimates of learning if children miss tests. In an application to summer learning, we illustrate how missing test scores can create an illusion of large summer learning gaps when true gaps are close to zero. We demonstrate two methods that reduce bias by exploiting the correlations between missing and observed scores on tests taken by the same child at different times. One method, multiple imputation, uses those correlations to fill in missing scores with plausible imputed scores. The other method models the correlations implicitly, using child-level random effects. Widespread adoption of these methods would improve the validity of summer learning studies and other longitudinal research in education.
Estimating Learning When Test Scores Are Missing: The Problem and Two Solutions
Keywords
summer learning, missing data
Education level
Topics
Document Object Identifier (DOI)
10.26300/07bv-by90
EdWorkingPaper suggested citation:
von Hippel, Paul T.. (). Estimating Learning When Test Scores Are Missing: The Problem and Two Solutions. (EdWorkingPaper:
-864). Retrieved from
Annenberg Institute at Brown University: https://doi.org/10.26300/07bv-by90