Structural equation modeling and data mining.
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.