Jared Boyce, Ph.D., is a quantitative education researcher specializing in using adult and student data to better understand instructional practices, education leadership, and data use in schools. He has firsthand experience building researcher-practitioner partnerships that empower educators in collecting, interpreting, and taking action on their own data through sustainable processes and improvement science. Boyce’s expertise encompasses mixture modeling, analyzing state and national data sets, evaluation methods, and learning analytics.

Boyce received the 2016 Advanced Studies of National Databases Outstanding Dissertation Award from the American Educational Research Association and the 2016 Emerald/EFMD Outstanding Doctoral Research Award in Education and Leadership Strategy for his research on education leadership.

Boyce received his Ph.D. in education leadership from Teachers College, Columbia University and his B.S. in symbolic systems, M.A. in philosophy, and M.A. in education from Stanford University.

Key Projects

• Montana Continuous Improvement in Education Research to Improve Secondary School Literacy Outcomes
• Quasi-experimental Studies of Online Learning in Higher Education
• Evaluation of the New Leaders Emerging Leaders Program
• Profiles of Selected Charter Schools, Charter Management Organizations, and Charter School Authorizers
• Next Generation Courseware Challenge

Selected Publications

• Boyce, J., & Bowers, A. J. (2018). Toward an evolving conceptualization of instructional leadership as leadership for learning: Meta-narrative review of 109 quantitative studies across 25 years. Journal of Educational Administration, 56(2).
• Boyce, J., & Bowers, A. J. (2018). Different levels of leadership for learning: Investigating differences between teachers individually and collectively using multilevel factor analysis of the 2011–2012 Schools and Staffing Survey. International Journal of Leadership in Education, 21(2), 197–225.
• Boyce, J., & Bowers, A. J. (2016). Principal turnover: Are there different types of principals who move from or leave their schools? A latent class analysis of the 2007–2008 Schools and Staffing Survey and the 2008–2009 Principal Follow-Up Survey. Leadership and Policy in Schools, 15(3), 237–272.
• Urick, A., Bowers, A. J., & Boyce, J. (2017). The application of multilevel models with latent variables to K-12 educational leadership and policy research: Multilevel factor analysis, growth models and structural equation models. A symposium accepted for presentation at the Modern Modeling Methods Conference, Storrs, CT.
• Bowers, A. J., Urick, A., & Boyce, J. (2016). Identifying informative subgroup typologies in K-12 education leadership data through the application of latent class analysis (LCA) and multilevel LCA. A symposium Presented at the annual Modern Modeling Methods (MMM) conference, University of Connecticut, Storrs, CT.