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 fall and spring tests taken by the same child. One method uses those correlations to multiply impute missing 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.