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 (1) applications of social psychological theories of status, identity, and stereotyping to the intersections of gender and sexuality; and (2) methodological research on categorical data analysis, latent variable modeling, and experimental design. His research has appeared in top social science journals including the American Sociological Review, Sociological Methodology, Social Problems, Social 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.
You can visit his university webpage here.
You can visit his personal webpage here.
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
Data Visualization Using Stata*
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