Stephanie T. Lanza, Ph.D. | The Methodology Center

Stephanie T. Lanza, Ph.D.

Stephanie Lanza, Ph.D.Scientific Director and Senior Research Associate, The Methodology Center

Research Associate Professor, College of Health and Human Development


The Methodology Center

The Pennsylvania State University

204 E. Calder Way, Suite 400

State College, PA 16801





Ph.D., Penn State, 2003 (Human Development and Family Studies)

M.A.S., Penn State, 2002 (Applied Statistics)

M.S., Penn State, 1998 (Human Development and Family Studies)

B.S., University of North Carolina at Chapel Hill, 1992 (Mathematical Science)

B.A., University of North Carolina at Chapel Hill, 1992 (Psychology)



Research Interests & Collaborators

I work primarily in two methodological areas. Much of my work seeks to advance finite mixture models, particularly latent class analysis. My primary collaborators on this project include Bethany Bray, Brittany Rhoades Cooper, and John Dziak. I also work to advance and apply new statistical models in order to reveal dynamic processes. Much of this work, done in collaboration with Runze Li, Sara Vasilenko, and Megan Piper, addresses new research questions using intensive longitudinal data related to drug abuse.



Recent Honors

2014: Society for Prevention Research Friend of ECPN Mentoring Award

2012-present: Associate Editor, Prevention Science



Recent Grants

Center for Prevention and Treatment Methodology

National Institute on Drug Abuse: P50-DA-10075

2010-2015; Project Director, Advances in Finite Mixture Modeling for Substance Use and HIV Research; Core Director, Software Development and Technology Transfer Core (PI: Linda M. Collins)


Advancing Tobacco Research by Integrating Systems Science and Mixture Models

National Cancer Institute: R01 CA168676

2012-2015; Role: Principal Investigator


Drug Abuse and HIV Prevention Research Methodology Conferences

National Institute on Drug Abuse: R13 DA020334

2011-2016; Role: Principal Investigator


Recent Publications

Lanza, S.T., Schuler, M.S., & Bray, B.C. (in press). Latent class analysis with causal inference: The effect of adolescent depression on young adult substance abuse profiles. In Causality and Statistics.

Bray, B. C., Lanza, S. T., & Tan, X. (2015). Eliminating bias in classify-analyze approaches for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22, 1-11.

Evans-Polce, R. J., Vasilenko, S. A., & Lanza, S. T. (2015). Changes in gender and racial/ethnic disparities in rates of cigarette use, regular heavy episodic drinking, and marijuana use: Ages 14 to 32. Addictive Behaviors, 41, 218-222

Fairlie, A. M., Maggs, J. L., & Lanza, S. T. (2015). Prepartying, drinking games, and extreme drinking among college students: A daily-level investigation. Addictive Behaviors, 42, 91-95.

Butera, N. M., Lanza, S. T., & Coffman, D. L. (2014). A framework for estimating causal effects in latent class analysis: Is there a causal link between early sex and subsequent profiles of delinquency? Prevention Science, 15, 397-407. PMCID: PMC3888479

Cooper, B. R., & Lanza, S. T. (2014). Who benefits most from Head Start? Using latent class moderation to examine differential treatment effects. Child Development, 85, 2317-38.

Dziak, J. J., Lanza, S. T., & Tan, X. (2014). Effect size, statistical power and sample size requirements for the bootstrap likelihood ratio test in latent class analysis. Structural Equation Modeling, 21, 534-552. 

Lagoa, C., Bekiroglu, K., Lanza, S. T., & Murphy, S. (2014). Designing adaptive intensive interventions using methods from engineering. Journal of Consulting and Clinical Psychology, 82, 868-878.

Lanza, S. T., Cooper, B. R., & Bray, B. C. (2014). Population heterogeneity in the salience of multiple risk factors for adolescent delinquency. Journal of Adolescent Health, 54, 319-325. doi: 10.1016/j.jadohealth.2013.09.007 PMCID: PMC3943167 (abstract)

Lanza, S. T., Piper, M. E., & Shiffman, S. (2014). New methods for advancing research on tobacco dependence using ecological momentary assessments. Nicotine and Tobacco Research, 16 Suppl 2, S71-S72. PMCID: PMC4085886

Vasilenko, S. A., Kugler, K. C., Butera, N. M. & Lanza, S. T. (2014). Patterns of adolescent sexual behavior predicting young adult sexually transmitted infections: A latent class analysis approach. Archives of Sexual Behavior. doi: 10.1007/s10508-014-0258-6 PMICD: PMC4107199

Vasilenko, S. A., & Lanza, S. T. (2014). Predictors of multiple sexual partners from adolescence through young adulthood. Journal of Adolescent Health, 56, 491-497. doi: 10.1016/j.jadohealth.2013.12.025. PMCID: PMC4139487

Vasilenko, S. A., Piper, M. E., Lanza, S. T. Liu, X., Yang, J. & Li, R. (2014). Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies. Nicotine and Tobacco Research, 16, (Suppl. 2), 135-143. PMCID: PMC4056442

Deiches, J., Baker, T., Lanza, S. T., & Piper, M. (2013). Early lapses in a cessation attempt: Lapse contexts, cessation success and predictors of early lapse. Nicotine and Tobacco Research, 15, 1883-91. PMCID: PMC23780705

Lanza, S. T., Coffman, D. L., & Xu, S. (2013). Causal inference in latent class analysis. Structural Equation Modeling, 20(3), 361-383. PMCID: PMC4240500 

Lanza, S. T., Moore, J. E., & Butera, N. M. (2013). Drawing causal inference using propensity scores: A practical guide for community psychologists. American Journal of Community Psychology, 52, 380-392. doi: 10.1007/s10464-013-9604-4 PMCID: PMC4098642

Lanza, S. T. & Rhoades, B. L. (2013). Latent class analysis: An alternative perspective on subgroup analysis in prevention and treatment. Prevention Science, 14, 157-168. PMCID: PMC3173585

Lanza, S. T., Tan, X., & Bray, B. C. (2013). Latent class analysis with distal outcomes: A flexible model-based approach. Structural Equation Modeling: A Multidisciplinary Journal, 20, 1-20. PMC Journal- In Process

Lanza, S. T., Vasilenko, S., Liu, X., Li, R., & Piper, M. (2014). Advancing the understanding of craving during smoking cessation attempts: A demonstration of the time-varying effect model. Nicotine and Tobacco Research, 16, S127-134. 

Like Us On Facebook or Tweet This Page