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Trenton D. Mize

Trenton D. Mize

Associate Professor // Sociology

Curriculum vitae

Office and Contact

Room: STON 328


Phone: (765) 496-3286

Ph.D. Sociology, M.S. Applied Statistics, M.A. Sociology - Indiana University

B.A. Sociology, B.S. Psychology - University of Georgia


Applied Statistics, Experimental Design, Computational Social Science, Social Psychology, Gender, Sexuality, Race/Ethnicity


My current research program spans two main areas: (1) applications of social psychological theories of status, identity, and stereotyping to the intersections of gender, sexuality, and race/ethnicity; and (2) applied statistics, quantitative methodology, computational social science, and experimental design. I use multiple quantitative methods in my work, including survey experiments, lab experiments, representative survey data, longitudinal surveys, and agent-based modeling.

My work has appeared in the American Sociological Review, Social Problems, Sociological Methodology, Social Psychology Quarterly, Social Science & Medicine, and other peer-reviewed journals. My research has been supported by the National Science Foundation, Time-Sharing Experiments for the Social Sciences (TESS), the Kinsey Institute, the American Sociological Association’s social psychology section, and others.

In addition to the sociology department, I am also core faculty in the cluster for advanced methodologies for the social, behavioral, and health sciences at Purdue (AMAP). Feel free to email me if you have questions or would like a copy of any of my published articles

Research on Social Psychology, Gender, Sexuality, and Race/Ethnicity

I use social psychological theories of status, identity, and stereotyping to better understand the intersections of gender, sexuality, and race/ethnicity. Much of this work has focused on inequalities in the labor market. For example, I have published on wage inequalities based on sexual orientation and on gender inequalities in leadership positions. Current projects examine how gender and sexual orientation influence hiring decisions and a separate project examining how an organization’s parental leave policies influence perceptions of mothers and fathers in the workplace.

Multiple projects of mine examine stereotypes of the intersections of gender, sexual orientation, and race/ethnicity—and the consequences of these stereotypes. Much of my work focuses on processes of categorization; e.g. one project examines how status processes affect who gets labeled as what sexual orientation category.

I am currently working on multiple projects focused on affect control theory, a computational and mathematical model of social interaction that combines many of my interests around identity, stereotyping, and quantitative methodology. These projects examine both how stereotypes/cultural sentiment are measured and on how to determine cultural consensus, along with ways to incorporate these insights into the theoretical model.

Research on Applied Statistics, Quantitative Methodology, Computational Social Science, and Experimental Design

Much of my current work focuses on categorical data analysis, latent variable modeling, and experimental design. For example, I have published on statistical approaches for examining nonlinear interaction effects and on approaches for examining cross-model comparisons of predictions and effects (across both linear and nonlinear models). A new line of research develops new methods for examining measurement invariance in nonlinear latent variable models (e.g. item response theory).

Another active area of my research focuses on experimental design. Multiple projects examine new and old approaches for measuring interpersonal status in groups. Another set of projects examines tools for measuring cultural stereotypes.

My work on affect control theory uses Bayesian statistical approaches to incorporate aspects of uncertainty into the computational model of behavior.

I also write statistical programs that implement new statistical approaches (primarily using Stata). Available programs/packages/commands implement best practices for data visualization, ways to visualize imbalance across groups, and easy ways to produce publication quality descriptive statistics tables. I am currently working on programs (a) that simplify tests of cross-model comparisons, (b) that provide new approaches for interpreting item response theory models, and (c) that calculate measures of model fit in latent class analysis.


I primarily teach applied statistics and quantitative methods courses and short workshops on advanced quantitative methods. I teach semester-long graduate courses on categorical data analysis, experimental design, and on latent variable modeling. I also teach one and two-day workshops on data visualization, survey design, analysis with missing data, workflow practices for reproducible research, statistical programming in Stata, and on survey experiments. The materials for these courses and workshops are freely available under the Teaching tab of my website.

I am also a core faculty member in the Advanced Methodologies at Purdue (AMAP) cluster.