Jeff is a Professor in the Measurement, Statistics and Evaluation Program within the Department of Human Development and Quantitative Methodology at the University of Maryland, College Park. There, he teaches introductory and intermediate graduate level statistics courses and advanced quantitative methods seminars in longitudinal data analysis, mixture modeling, simulation design and statistical computing. Jeff has taught several multi-day workshops on the application of longitudinal methods using R and SAS statistical software most recently at the National Center of Educational Statistics (NCES) in Washington D.C.  Prior to joining the program faculty in the fall of 2006, Jeff received an M.S. degree in Statistics and completed his Ph.D. in the Quantitative Methods in Education from the University of Minnesota. Before that, Jeff taught high school mathematics for 12 years. He has published nearly 100 articles and book chapters, co-edited three volumes and co-authored a book. His research focuses on linear and nonlinear models for repeated measures data, structural equation models, finite mixtures of both linear and nonlinear growth models and extensions of these methods to multilevel data structures.

Contact Jeff via harring@umd.edu

Tessa is a Ph.D. candidate in the Measurement, Statistics and Evaluation Program within the Department of Human Development and Quantitative Methodology at the University of Maryland, College Park. She received her Master of Science in Educational Research from Georgia State University. Tessa currently serves as a project coordinator for the Synthetic Data Project (SDP) of the Maryland Longitudinal Data System Center, an Institute of Education Sciences (IES) funded project aimed at assessing the feasibility of and implementing a system for synthesizing statewide longitudinal data in order to increase data access for researchers and policy analysts while minimizing risk of data disclosure. For the SDP, Tessa conducts research on the feasibility of reproducing nested data structures in longitudinal synthetization models. Outside her work with the SDP, Tessa’s research has centered around creating and improving statistical methods for analyzing complex data structures in longitudinal contexts. This includes modeling time as an outcome in latent growth models, accounting for similarities among schools when modeling student mobility in longitudinal studies, and exploring the development of ensembles of social networks in the classroom over time.

Contact Tessa via johnsont@umd.edu