Research

Current research projects include:

a randomized control trial (RCT) to test a quality improvement process (PDSA) to implement community supervision guidelines in partnership with Massachusetts Probation Service, funded by Arnold Ventures, which includes:
- management and analysis of large, complex administrative data
- developing a natural language processing (NLP) computer algorithm to extract data from case notes
social network analysis (SNA) of the social support networks of young adults;
- older teens in foster care
- undergraduate and graduate students
agent-based modeling (ABM) to explore the accumulation of risk and protective factors among adolescents.

If you are interested in getting involved with any of these projects, or learning about my other projects, please reach out: joannlar@buffalo.edu.

My research focuses on facilitating the transition to adulthood for all youth by generating knowledge that will help us improve the public systems that operate in their lives. The transition to adulthood has become longer, more heterogeneous, and overall more difficult in comparison to past generations. Yet, our systematic understanding of how to support the transition to adulthood for all youth, particularly youth and their families that are struggling, is surprisingly lacking. My research contributes to our systematic understanding of how to support all youth and their families in three related areas: a) the transition to adulthood; b) public systems, mainly the child welfare and juvenile justice systems; and c) positive youth development.

In recent years, I have been inspired to move beyond examining limitations (i.e., social exclusion lens) to building knowledge that can improve our ability to intervene to cultivate healthy development (i.e., positive youth development lens). I use traditional statistical methods (including logistic regression and group based trajectory modeling), and in recent years have expanded my approach and skills. In an effort to move beyond a siloed, deficit-oriented approach and toward using a holistic, positive youth development approach, I have begun delving into computational approaches to study youth development and interventions. Theoretically, computational approaches are often grounded in complexity theory (or complex adaptive systems, which are dynamic, nonlinear systems). Methodologically, computational approaches include a range of methods -- I have been learning social network analysis and agent-based modeling (ABM), a type of computer simulation, as two new methodological approaches.

Recent Research Publications

An * indicates a student author.

For a full list of publications, you can find my profiles on Research Gate or Google Scholar.

Positive Youth Development

Mikytuck, A. & Lee, J.S. (2026). Exploring positive developmental outcomes for youth adjudicated in the juvenile justice system: Patterns of gainful expectations and behavior. American Journal of Orthopsychiatry. https://doi.org/10.1037/ort0000836
Lee, J.S., Taxman, F.S., Mulvey, E.P., & Schubert, C.A. (2022). Who will become productive adults? Longitudinal patterns of gainful activities among serious adolescent offenders. Youth & Society, 54(7), 1150-1177. https://doi.org/10.1177/0044118X21996386

Transitions to Adulthood & Public Systems

Applegarth, D.M. & Lee, J.S. (2026). Recidivism, service characteristics, and changes in risk and protective scores in juvenile probation. Journal of Developmental and Life-Course Criminology, 12(1), 10.1007/s40865-026-00297-w.
Villodas, M.L., Lee, J.S., Gimm, G. & Pilkerton, C.* (2026). At the intersection of disability and transitioning to adulthood: Service receipt by disability status among foster youth. Children and Youth Services Review, 183. https://doi.org/10.1016/j.childyouth.2026.108805

Lee, J.S., Appleton, C. E.*, & Stuart, O.K.* (2023). Changes in risk profiles: Latent transition analysis of youth on probation. Criminal Justice and Behavior, 50(12), 1783-1804. https://doi.org/10.1177/00938548231206537

Complexity and Computational Approaches

Rodriguez, M.Y. & Lee, J.S. (2026). Social work science and advanced computational methods. Research on Social Work Practice. https://doi.org/10.1177/10497315261422584
Pycroft, A., Lee, J.S., & Wolf-Branigin, M. (2026). Exploring accumulative risk and protective factors for young people: An agent-based model. Research on Social Work Practice. https://doi.org/10.1177/10497315251371016
Lee, J.S. & Wolf-Branigin, M. (2023). Generating inclusive services for children, youth, and families: A shift to using complex systems theory. Child & Family Social Work, 28(4), 897-907. http://doi.org/10.1111/cfs.13010

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