Tom Smith

Tom Smith is a Teaching Professor in the Department of Educational Technology, Research and Assessment at Northern Illinois University’s College of Education. He is actively involved in teaching and research, focusing on quantitative methodologies and statistical analysis in educational contexts.

• Ph.D. in Quantitative Psychology, University of Illinois at Urbana-Champaign, 2000
• M.S. in Statistics, University of Illinois at Urbana-Champaign, 1997
• B.A. in Psychology, University of St. Thomas (St. Paul, MN), 1988

Dr. Smith's research centers on advanced statistical methods in education, including multilevel modeling, latent variable analysis, categorical data techniques, and robust estimation under non-normality. He conducts workshops on hierarchical linear modeling, survey construction, and factorial invariance, contributing significantly to methodological training in education.

His recent publications reflect a strong trend in applying sophisticated statistical models to educational data, particularly in assessing model fit, improving logistic regression techniques, and enhancing computer-assisted learning environments. These works span journals in applied statistics, educational technology, and geography education, indicating interdisciplinary engagement.

While no specific scientific awards are listed, his title as Presidential Teaching Professor suggests recognition for excellence in pedagogy. He teaches core courses such as ETR 520 (Research Methods), ETR 521/522 (Educational Statistics), and advanced seminars in multivariate analysis and latent variable modeling.

Dr. Smith advises students through graduate workshops and field experiences, though no named advisees are listed. He has been involved in projects related to cognitive tutoring systems and technology-enhanced learning environments, often collaborating with researchers across disciplines. He contributes to outreach through the Learn-IT Conference and Project SEED partnership with School District 203.

He is affiliated with the User Experience (UX) Lab and other resource labs within the department, supporting innovative research in learning analytics and educational assessment.