Structural equation modeling and data mining.
Ross Jacobucci’s research interests are in the areas of structural equation modeling and data mining. Most of his quantitative work is in integrating various types of structural equation models (specifically longitudinal models) with methods commonly used in data mining (regularization and decision trees). His applied work has mostly been in using data mining methods in clinical research, as well as studying trajectories of cognitive change in older adults. rjacobucci.com
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. Psycholgocical 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.