Publications

Books

Jacobucci, R., Grimm, K. J., & Zhang, Z. (under contract). Machine learning for social and behavioral research. New York, NY: Guilford

Selected papers

Full CV: https://github.com/Rjacobucci/CV/blob/master/rj_cv.pdf

Hong, M., Jacobucci, R., & Lubke, G. (in press). Deductive Data Mining. Psychological Methods. paper

Serang, S., Jacobucci, R., Stegmann, G., Brandmaier, A. M., Culianos, D, & Grimm, K. J. (in press). Mplus Trees: Structural Equation Model Trees Using Mplus. Structural Equation Modeling. paper

Liang, X., & Jacobucci, R. (in press). Regularized Structural Equation Modeling to Detect Measurement Bias: Evaluation of Lasso, Adaptive Lasso and Elastic Net. Structural Equation Modeling.

Jacobucci, R., & Grimm, K. J. (in press). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science. paper and code

Serang, S. & Jacobucci, R. (2020). Exploratory mediation analysis of dichotomous outcomes via regularization. Multivariate Behavioral Research.

Jacobucci, R., Serang, S., & Grimm, K. J. (2019). A short note on complications in interpretation with the dual change score model . Structural Equation Modeling, 26, 924-930.

Burke, T.A., Ammerman, B.A., & Jacobucci, R. (2019). The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behavior: A systematic review. Journal of Affective Disorders.

Jacobucci, R., Brandmaier, A., & Kievit, R. (2019). A practical guide to variable selection in structural equation models with regularized MIMIC models. Advances in Methods and Practices in Psychological Science, 2, 55-76. paper

Stegmann, G., Jacobucci, R., Serang, S., & Grimm, K. J. (2018). Recursive partitioning with nonlinear change trajectories. Multivariate Behavioral Research, 53, 559-570.

Jacobucci, R., Grimm, K. J. (2018). Comparison of frequentist and Bayesian regularization in structural equation modeling. Structural Equation Modeling, 25, 639-649.

Serang, S., Jacobucci, R., Brimhall, K. C., & Grimm, K. J. (2017). Exploratory mediation analysis via regularization. Structural Equation Modeling, 24. 733-744.

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. (2017). Big data methods and psychological science. Psychological Science Agenda. link

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 BehaviorRcode

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 AssessmentRcode 

Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling, 23, 555-566. paper

Hayes, T., Usami, S., Jacobucci, R., & McArdle, J. J. (2015). Using classification and regression trees (CART) and random forests to analyze attrition in longitudinal data: Results from two simulation studies, 30, 9111-929. Psychology and Aging

Please email me if you would like a copy of a manuscript.