Date of Graduation
Master of Science in Statistics and Analytics (MS)
Statistics and Analytics
Second Committee Member
Third Committee Member
Bayesian, Evaluation, Persistence, Rating of Teachers, Teacher Effectiveness
Longitudinal measures for students have become increasingly popular to estimate the effects of individual teachers and schools. Value-added models are one of the approaches using longitudinal data to evaluate teachers and schools. In the value-added model (VAM) literature, many statistical approaches have been developed and used to estimate teacher or school effects on student learning. This study opted to use a Bayesian multivariate model for evaluating teacher effects. The generalized persistence models can handle longitudinal data, not vertically scaled, allowing for a below-par teacher’s effects correlation across test administrations. This study first generated longitudinal students’ test score data and used this model to fit the different data sets. This study uses frequentist and Bayesian frameworks to investigate the generalized persistence models. Then, this paper compared the differences and similarities among the models and between the two approaches using simulated and actual data. The found results are in marge with what is in the present value-added model literature.
Kamgue, M. J. (2023). Comparative Analysis of Teacher Effects Parameters in Models Used for Assessing School Effectiveness: Value-Added Models & Persistence. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/5151