Author Information:
Walter L. Leite, Professor of the Research and Evaluation Methodology Program
College of Education, University of Florida
Dr. Leite's research consists of developing and evaluating statistical methods to strengthen causal inference and understanding of causal mechanisms using quasi-experimental and non-experimental data. His specific methodological interests are in structural equation modeling, multilevel modeling, and propensity score methods, as well as the integration between these three methods. He investigates innovative applications of these methods to educational research performed with large datasets from state departments of education, nationally-representative educational surveys, and massive datasets from virtual learning environments. The methods that he investigate take advantage of large scale longitudinal data to answer causal questions about treatment effects, mediation and moderation. He addresses obstacles to effective program evaluation with quasi-experimental and non-experimental data such as selection bias, measurement error, and attrition bias. He is currently the principal investigator of the Virtual Learning Lab, a research center funded by the Institute of Education Sciences of the US Department of Education. He has also served as program evaluator for the Lastinger Center for Learning at University of Florida since 2007, where he has evaluated large-scale student, teacher and school improvement programs implemented in all districts of Florida.
List of publications with links to full text.
Walter L. Leite, Professor of the Research and Evaluation Methodology Program
College of Education, University of Florida
Dr. Leite's research consists of developing and evaluating statistical methods to strengthen causal inference and understanding of causal mechanisms using quasi-experimental and non-experimental data. His specific methodological interests are in structural equation modeling, multilevel modeling, and propensity score methods, as well as the integration between these three methods. He investigates innovative applications of these methods to educational research performed with large datasets from state departments of education, nationally-representative educational surveys, and massive datasets from virtual learning environments. The methods that he investigate take advantage of large scale longitudinal data to answer causal questions about treatment effects, mediation and moderation. He addresses obstacles to effective program evaluation with quasi-experimental and non-experimental data such as selection bias, measurement error, and attrition bias. He is currently the principal investigator of the Virtual Learning Lab, a research center funded by the Institute of Education Sciences of the US Department of Education. He has also served as program evaluator for the Lastinger Center for Learning at University of Florida since 2007, where he has evaluated large-scale student, teacher and school improvement programs implemented in all districts of Florida.
List of publications with links to full text.