Stephanie Lanza

• Substance use behavior in daily life among adolescents and young adults
• Finite mixture modeling, including innovations in latent class analysis, so that researchers can discover and understand hidden subgroups in their data
• Time-varying effect models to study substance use behavior developmentally and in real time/daily life
• Leveraging new technologies and methods to understand substance use behaviors in daily life based on intensive longitudinal assessment

Stephanie's career is devoted to advancing research in behavioral health through the development and application of innovative quantitative methods. She focuses this work in two key areas. First, she works on extensions to latent class analysis, including multilevel latent class analysis, so that researchers can discover hidden subgroups or types of moments in their data and understand their antecedents and consequences. Second, she focuses on the application and dissemination of time-varying effect models, which enable scientists to examine regression coefficients as a continuous function of age or time, often using intensive longitudinal data.

Current external funding includes a NIDA grant awarded to Dr. Lanza, titled “Promoting Rapid Uptake of Multilevel Latent Class Modeling via Best Practices: Investigating Heterogeneity in Daily Substance Use Patterns” (R01-DA057588). Additionally, Dr. Lanza Co-directs with Dr. Jennifer Maggs the long-standing Prevention and Methodology Training Program (T32-DA017629), which has trained more than 100 pre- and post-doctoral research fellows in advancing substance use research through innovative methods.

Latent class analysis; time-varying effect modeling; longitudinal methods; intensive longitudinal data; etiology of health risk behaviors.