معرفی
Benjamin Shear is an Assistant Professor in the Research and Evaluation Methodology program at the University of Colorado Boulder's School of Education. His work focuses on psychometrics, applied statistics, and validity theory, particularly in mathematics education contexts. He collaborates on projects involving differential item functioning (DIF), diagnostic classification models, and improving quantitative methods for education researchers. He is affiliated with the Center for Assessment, Design, Research & Evaluation (CADRE) and contributes to the Stanford Education Data Archive (SEDA), a comprehensive dataset on U.S. education outcomes. His methodological innovations include hierarchical logistic regression approaches for multilevel DIF analysis and pooled heteroskedastic ordered probit models for latent variable estimation. He maintains an active GitHub profile with code for statistical methods published in peer-reviewed journals.
Education background includes prior experience as a high school mathematics teacher, informing his applied research focus on real-world educational measurement challenges. His technical contributions include developing Stata modules for HETOP modeling (heteroskedastic ordered probit) and open-source tools for analyzing coarsened test score data. He has authored over 30 peer-reviewed articles and serves as a methodological expert for large-scale education assessments.
Key collaborations include work with Sean Reardon and others on educational inequality measurement via SEDA, as well as methodological advancements in working with sparse or aggregated testing data. His research bridges theoretical statistical development with practical applications in educational policy evaluation and accountability systems.
