The measurement of student academic growth is one of the most important statistical tasks in an educational accountability system. The current methods of measuring student growth adopted in most states have various drawbacks in terms of sensitivity, accuracy, and interpretability. In this thesis, we apply the conditional growth chart method, a well-developed diagnostic tool in pediatrics, to student longitudinal test data to produce descriptive and diagnostic statistics about students' academic growth trajectory. We also introduce an innovative simulation-extrapolation (SIMEX) method which corrects for measurement error-induced bias in the estimation of the conditional growth model. Our simulation study shows that the proposed method has an advantage in terms of mean squared error of the estimators, when compared with the growth model that ignores measurement error. Our data analysis demonstrates that the conditional growth chart method, when combined with the SIMEX method, can be a powerful tool in the educational accountability system. It produces more sensitive and accurate measures of student growth than the other currently available methods; it provides diagnostic information that is easily understandable to teachers, parents and students themselves; the individual level growth measures can also be aggregated to school level as an indicator of school growth.