Research Topics

Applied AI for Human Development & Health

Dynamic & Intensive Longitudinal Measurement

Lifespan Health & Disparities

We study how psychological, cognitive, and physical health evolve across the lifespan, with a particular focus on how structural inequities and social determinants of health shape these trajectories. Our work integrates life course theory with advanced statistical modeling to identify risk and resilience pathways from early adulthood through older age. We explore how factors such as loneliness, affect, discrimination, and access to resources influence well-being and survival, with the goal of informing interventions and policies that promote healthy aging for all.

Cintron et al. (2023). Does discrimination mediate the association between education and later-life cognitive outcomes in a cohort of Black Americans? An example of parametric g-estimation with latent variables in the STAR study. Alzheimer’s & Dementia
Ong et al. (in press). Loneliness trajectories in U.S. military veterans: a 3-year longitudinal study of risk and protective factors. Journals of Gerontology: Series B
Cintron & Ong (2024). Trajectories of affective well-being and survival in middle-aged and older adults. Emotion

Applied AI for Human Development & Health

We leverage artificial intelligence, machine learning, and natural language processing to analyze complex behavioral and health data. This work aims to uncover patterns in psychological functioning, detect treatment heterogeneity, and generate insights from high-dimensional or unstructured data. We are particularly interested in how AI tools can enhance prediction, understanding, and intervention across the lifespan.

Cintron & Ong (in press). What Makes Life Go Well? A Network Topic Modeling Analysis of Well-Being Practices in Adults with Chronic Pain. Pain Medicine
Cintron & Montrosse-Moorhead (2022). Integrating big data into evaluation: R code for topic identification and modeling. American Journal of Evaluation
Cintron, D. W. (2020). A Latent Dirichlet Allocation Model of Action Patterns. [NCME Presentation]

Causal Inference & Program Evaluation

We apply rigorous causal inference methods to evaluate the effects of social policies, interventions, and environmental exposures on health and well-being. Our work examines not just whether programs work, but for whom, under what conditions, and through which mechanisms. Using tools such as G-estimation, propensity score matching, and quasi-experimental designs, we generate actionable evidence to guide the design of effective policies and interventions across diverse populations and settings.

Cintron et al. (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
Cintron et al. (2022). Heterogeneous treatment effects in social policy studies: an assessment of contemporary articles in the health and social sciences. Annals of Epidemiology
Cintron et al. (2023). Does discrimination mediate the association between education and later-life cognitive outcomes in a cohort of Black Americans? An example of parametric g-estimation with latent variables in the STAR study. Alzheimer’s & Dementia

Psychometrics & Measurement Equity

We develop and apply psychometric methods that ensure psychological and health assessments are valid, reliable, and equitable across diverse populations and contexts. This includes testing for intersectional measurement invariance, improving classification accuracy in latent class and factor models, and using computational techniques such as alignment optimization and topic modeling. Our work is grounded in the belief that measurement is not neutral—tools must be critically evaluated for fairness and precision to support just and inclusive science.

Cintron et al. (2023). Testing for intersectional measurement invariance with the alignment method: Evaluation of the 8-item patient health questionnaire. Health Services Research
Cintron (2025). Methods for intersectional measurement invariance testing. [WPA Workshop]
Cintron, D. W. (2021). Methods for measuring speededness: Chronology, classification, and ensuing research and development. ETS Research Report Series
Cintron, D. W. (2020). An Evaluation of Statistical Estimation Procedures for Ordinal Factor Analysis Models. [Modern Modeling Methods]
Briesch et al. (2020). Factorial invariance of the Usage Rating Profile for Supporting Students' Behavioral Needs (URP-NEEDS). School Psychology

Dynamic & Intensive Longitudinal Measurement

We develop and apply cutting-edge longitudinal modeling techniques to capture within-person change and between-person differences over time. Drawing on intensive longitudinal designs such as ecological momentary assessment (EMA) and daily diaries, we use approaches like dynamic structural equation modeling, growth mixture modeling, and multilevel modeling to uncover nuanced patterns of change in affect, behavior, and health. This research reveals the temporal and contextual dynamics that shape psychological and physical well-being in daily life.

Ong et al. (in press). Targeting daily positive evenets to improve emotional and functional well-being in adults with fibromyalgia: Insights from the LARKSPUR randomized controlled trail. Journal of Medical Internet Research
Cintron & Ong (2024). Trajectories of affective well-being and survival in middle-aged and older adults. Emotion
Cintron & Ong (2024). Parallel growth trajectory classes of psychological and subjective well-being. [Society for Affective Science]

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