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

Selected papers

Full CV: link

Jacobucci, R. (in press). A Critique of Using the Labels Confirmatory and Exploratory in Modern Psychological Research. Frontiers in Psychology.

Wilcox, K. T., Jacobucci, R., Zhang, Z., & Ammerman, B. A. (in press). Supervised Latent Dirichlet Allocation with Covariates: A Bayesian Structural and Measurement Model of Text and Covariates. Psychological Methods.

Brandmaier, A. M., & Jacobucci, R. (accepted). Machine-learning approaches to structural equation modeling. To appear in  Hoyle, R. H. (Ed.), Handbook of Structural Equation Modeling (2nd ed.). New York, NY: Guilford. pdf

Li, X., & Jacobucci, R. (2022). Regularized structural equation modeling with stability selection. Psychological Methods, 27, 497-518.

Forrest, L., Jacobucci, R., & Grilo, C. M. (2022). Empirically-Determined severity levels for binge-eating disorder outperform existing severity classification schemes. Psychological Medicine.

Littlefield, A. K., Cooke, J. T., Bagge, C., Glenn, C., Kleiman, E. M., Jacobucci, R., Millner, A. J., & Steinley, D. (2021). Machine Learning to Classify Suicidal Thoughts and Behaviors: Implementation within the Common Data Elements used by the Military Suicide Research Consortium. Clinical Psychological Science.

Jacobucci, R., Littlefield, A., Millner, A. J., Kleiman, E. M., & Steinley, D. (2021). Evidence of inflated prediction performance: A commentary on machine learning and suicide research. Clinical Psychological Science.

Jacobucci, R., Ammerman, B. A., & Wilcox, K. (2021). The application of machine learning for text-based responses to improve suicide risk prediction. Suicide and Life Threatening Behavior

Jacobucci, R., Ammerman, B. A., & Li, X. (2021). Using ordinal regression for advancing the understanding of distinct suicide outcomes. Suicide and Life Threatening Behavior

Grimm, K. J., & Jacobucci, R. (2021). Reliable trees: Reliability informed recursive partitioning for psychological data. Multivariate Behavioral Research

Serang, S., Jacobucci, R., Stegmann, G., Brandmaier, A. M., Culianos, D, & Grimm, K. J. (2021). Mplus Trees: Structural Equation Model Trees Using Mplus. Structural Equation Modeling. paper

Hong, M., Jacobucci, R., & Lubke, G. (2020). Deductive Data Mining. Psychological Methods. 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.

Ammerman, B.A., Jacobucci, R., Turner, B. J., Dixon-Gordon, K., & McCloskey, M.S. (2020). Quantifying the importance of lifetime frequency versus number of methods used in the consideration of NSSI severity. Psychology of Violence

Burke, T. A., Jacobucci, R., Ammerman, B. A., & Diamond, G. (2020). Using machine learning to classify suicide attempt history among youth in medical care settings. Journal of Affective Disorders, 268, 206-214

Jacobucci, R., & Grimm, K. J. (2020). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science, 15, 809-816. paper and code

Serang, S. & Jacobucci, R. (2020). Exploratory mediation analysis of dichotomous outcomes via regularization. Multivariate Behavioral Research, 55, 69-86.

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.