Date of Graduation


Document Type


Degree Name

Doctor of Philosophy in Education Policy (PhD)

Degree Level



Education Reform


Jonathan Wai

Committee Member

Sarah McKenzie

Second Committee Member

Robert Maranto


Arkansas, descriptive statistics, gifted and talented identification, gifted education, high aptitude, logistic regressions, mixed-effects


In the United States, education policies differ from state to state. Local research, therefore, is important to inform educators, policymakers, and researchers on the ground. This dissertation leverages ten years of administrative data to study three questions about gifted and talented (G/T) identification and education in Arkansas: does the current system identify the right students? Are gifted and talented programs beneficial for students? And, how can we improve diversity in gifted and talented education? Leveraging logistic regression, mixed-effects models, and descriptive statistics, I sought to provide answers to these three questions. First, are academically ready students from low-income families being missed in the current gifted and talented education system? Second, do gifted and talented services benefit high aptitude students academically? And finally, does using the local norm approach necessarily improve diversity in the G/T pool of students? This study has important implications for Arkansas’s G/T identification and education policies.