Dakota W. Cintron, an Assistant Professor of Psychology in the Division of Behavioral and Organizational Sciences at Claremont Graduate University, studies how well-being and health change over time in diverse populations, including at-risk groups. His research uses advanced quantitative methods like growth mixture modeling and natural language processing to explore the interplay between psychosocial factors, health outcomes, and individual development. Cintron focuses on measuring well-being, emotional and affective dynamics, and social health disparities. His scholarship has applied intersectional approaches to measure invariance testing, structural equation modeling, and graphical models to uncover factors influencing everyday and long-term outcomes. He’s also co-authored Introduction to Modern Modeling Methods and contributed to health policy, intersectionality, and psychological resilience studies.
Cintron, D. W., & Ong, A. D. (2024). Trajectories of affective well-being and survival in middle-aged and older adults. Emotion.
Cintron, D. W., Matthay, E. C., & McCoach, D. B. (2023). Testing for intersectional measurement invariance with the alignment method: evaluation of the 8‐item patient health questionnaire. Health Services Research, 58, 248-261
Cintron, D. W., Gottlieb, L. M., Hagan, E., Tan, M. L., Vlahov, D., Glymour, M. M., & Matthay, E. C. (2023). A quantitative assessment of the frequency and magnitude of heterogeneous treatment effects in studies of the health effects of social policies. SSM-Population Health, 22, 101352
Cintron, D. W., & Montrosse-Moorhead, B. (2022). Integrating big data into evaluation: R code for topic identification and modeling. American Journal of Evaluation, 43(3), 412-436.
Cintron, D. W., Loken, E., & McCoach, D. B. (2023). A cautionary note about having the right mixture model but classifying the wrong people. Multivariate Behavioral Research, 58(4), 675-686.
McCoach, D. B., & Cintron, D. (2022). Introduction to modern modelling methods. Sage.
Psych 315E Multilevel Modeling
Psych 315NN Bayesian Statistics
Psych 302 Research Methods (PhD)