Professor Jacobucci is open to mentoring graduate students in the fall
My main line of interest is in integrating methods from both machine learning and latent variable modeling. Additionally, I am researching the use of machine learning for clinical psychology research, specifically suicide and non-suicidal self-injury.
Serang, S., Jacobucci, R., Brimhall, K. C., & Grimm, K. J. (in press). Exploratory mediation analysis via regularization. Structural Equation Modeling.
Ammerman, B. A., Jacobucci, R., Kleiman, E. M., Uyeji, L., & McCloskey, M. S. (in press). The relationship between nonsuicidal self-injury age of onset and severity of self-harm. Suicide and Life Threatening Behavior.
Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2017). A comparison of methods for uncovering sample heterogeneity: Structural equation model trees and finite mixture models. Structural Equation Modeling, 24. 270-282.
Grimm, K. J., Jacobucci, R., McArdle, J. J. (January, 2017). Big data methods and psychological science. Psychological Science Agenda.
Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling, Structural Equation Modeling, 23, 555-566.
Ammerman, B. A., Jacobucci, R., Kleiman, E. M., Muehlenkamp, J. J., & McCloskey, M. S. (2016). Development and validation of empirically derived frequency criteria for NSSI disorder using exploratory data mining, Psychological Assessment.