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Predicting Teacher Value-Added Results In Non-Tested Subjects Based On Confounding Variables: A Multinomial Logistic Regression, Nathan Street
Predicting Teacher Value-Added Results In Non-Tested Subjects Based On Confounding Variables: A Multinomial Logistic Regression, Nathan Street
Doctoral Dissertations and Projects
Teacher value-added measures (VAM) are designed to provide information regarding teachers’ causal impact on the academic growth of students while controlling for exogenous variables. While some researchers contend VAMs successfully and authentically measure teacher causality on learning, others suggest VAMs cannot adequately control for exogenous influences on the classroom. Furthermore, because VAMs are primarily connected to student performance on standardized, high-stakes exams and those exams are resoundingly considered to be inadequate measures of true student learning, educators and educational leaders assert VAM results are moot. The purpose of this study was to consider the potential for student background, teacher preparation, …