This article examines the multilevel linear crossed random effects growth model for estimating teacher and school effects from repeated measurements of student achievement. Results suggest that even a small degree of unmodeled nonlinearity can result in a substantial upward bias in the magnitude of the teacher effect, which raises concerns about its appropriateness for estimating teacher effects. To address this issue, a piecewise linear crossed random effect growth model is proposed. A comparison with the linear growth form shows that the piecewise specification provides more accurate estimates of teacher effects when achievement growth departs from linear growth across grade levels or over summer, which are prevalent conditions. Fitted examples using nationally representative data and Bayesian estimation methods are provided. (Contains 3 tables and 3 figures.)