J.R. Lockwood is a statistician at RAND Specializing in longitudinal modeling of student achievement, value-added models for estimating teacher effects, relationships between teaching practices and student achievement. His methodological areas of expertise include hierarchical, longitudinal and random effects modeling, Bayesian methods, and statistical computing. His extensive work with value-added modeling for estimating teacher effects includes several papers on statistical and computational methods and the development of software for implementing complex models with large datasets, as well papers presenting empirical analyses of the sensitivity of estimated teacher effects to assumptions about teacher effect persistence and the psychometric properties of the assessments. Dr. Lockwood is currently leading a project funded by the Institute of Education Sciences to develop enhanced models for estimating teacher effects that account for student-teacher interactions and potentially missing-not-at-random data. He received the 2001 Leonard J. Savage Award for the outstanding doctoral dissertation in Bayesian Application Methodology and in his six years at RAND, he has worked on a variety of quantitative analyses of education, environmental, health policy problems. Dr. Lockwood received a Ph.D. in statistics in 2001 from Carnegie Mellon University.