Date of Graduation

12-2023

Document Type

Thesis

Degree Name

Master of Science in Statistics and Analytics (MS)

Degree Level

Graduate

Department

Statistics and Analytics

Advisor/Mentor

Samantha Robinson

Committee Member

Boykin, Allison

Second Committee Member

Petris, Giovanni

Third Committee Member

Liang, Xinya

Keywords

Bayesian, Evaluation, Persistence, Rating of Teachers, Teacher Effectiveness

Abstract

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.

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