# Ross Jacobucci

Assistant Professor

Ph.D., University of Southern California

- Quantitative

574-631-2589

Corbett Family Hall

Notre Dame, IN 46556

Structural equation modeling and data mining.

## Profile

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.

## Recent Publications

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.