Skip to content
Back to Instructors

Trenton Mize

Trenton D. Mize, Ph.D., is the Dean’s Professor of the College of Liberal Arts in the Departments of Sociology & Statistics (by courtesy) and Director of The Methodology Center at Purdue University.

Professor Mize’s research and teaching focus on categorical data analysis, latent variable modeling, experimental design, and data visualization.

Mize received a Ph.D. in Sociology and a M.S. in Applied Statistics from Indiana University. His research broadly focuses on three areas: (1) methodological advancements for categorical data analysis, data visualization, latent variable modeling, and experimental design; (2) social psychological examinations of how social categories impact how we view ourselves and how others view and treat us; and (3) longitudinal research on how social factors influence our long-term health and well-being. His research has appeared in top social science journals including the American Sociological ReviewSociological MethodologySocial ProblemsSocial Science & Medicine, and Social Science Research.

Mize teaches a variety of courses and seminars on applied statistics and quantitative methodology. Specific topics include categorical data analysis, latent variable modeling, experimental design, data visualization, data management and workflow, missing data, statistical software programming, and survey design.

He also teaches seminars for AI Horizons, where he offers training in data visualization and LLM-assisted workflows.

You can visit his university webpage here.

You can visit his personal webpage here.

Google Scholar Citation Page

Trenton's Seminars
Livestream

Categorical Data Analysis

Categorical Data Analysis is a seminar in applied statistics that primarily deals with regression models in which the dependent variable is binary, nominal, ordinal, or count.

View Details
Livestream

Data Visualization Using Stata and LLMs*

Understanding data and effectively presenting model results are challenges that data analysts face almost every day. There is seldom a more effective solution than a well thought out visualization. Problems in the data are easily identified; complex effects are quickly...

View Details