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Michael Seltzer is a Research Professor at the UCLA School of Education & Information Studies, specializing in advanced quantitative methods in education research. He obtained his Ph.D. in Education from the University of Chicago in 1991. His scholarly work focuses on multilevel modeling and statistical analysis in educational settings, including treatment effect variation and measurement error in predictors. He has contributed significantly to the field through various publications, addressing complex methodologies and providing insights for improving educational assessments and programs. His notable works include discussions on multilevel analysis, propensity score matching strategies, and the use of latent variable regression models in educational research. Seltzer's collaborative projects often explore heterogeneity in treatment responses and the application of hierarchical modeling techniques to educational data, contributing to a deeper understanding of educational effectiveness and policy implications.
Department of Economics admits primarily for the PhD program.