Publications

My research spans machine learning, clinical psychology, and statistical methodology, with a focus on (1) digital phenotyping and smartphone-based assessment of suicide risk and (2) regularization methods for structural equation modeling. For the complete and most current publication list, see my Google Scholar profile.

Selected First/Senior-Author Publications

These are highlighted by venue (top general-interest and methodology journals) and authorship position.

Digital phenotyping and suicide risk

• Jacobucci, R., Shao, W., Kobrinsky, V., & Ammerman, B. (2026). Predicting momentary suicidal ideation from smartphone screenshots using vision-language models: Prospective machine learning study. JMIR Mental Health.
• Jacobucci, R., Jones, S. G., Blacutt, M., & Ammerman, B. A. (2025). Passive vs active nighttime smartphone use as markers of next-day suicide risk. JAMA Network Open.
• Jacobucci, R., Blacutt, M., Ram, N., & Ammerman, B. A. (2025). Smartphone screen time and suicide risk in daily life captured through high-resolution screenshot data. npj Digital Medicine.
• Jacobucci, R., Ammerman, B., & Ram, N. (2024). Examining passively collected smartphone-based data in the days prior to psychiatric hospitalization for a suicidal crisis: Comparative case analysis. JMIR Formative Research. doi:10.2196/55999
• Ammerman, B. A., Kleiman, E. M., O’Brien, C., Knorr, A. C., Bell, K. A., Ram, N., …, & Jacobucci, R. (2025). Smartphone-based text obtained via passive sensing as it relates to direct suicide risk assessment. Psychological Medicine.

Machine learning and methodology

• 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. & Grimm, K. J. (2020). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science.
• Jacobucci, R. (2022). A critique of using the labels confirmatory and exploratory in modern psychological research. Frontiers in Psychology. doi:10.3389/fpsyg.2022.1020770
• Jacobucci, R., Ammerman, B., & McClure, K. (2024). Understanding momentary missingness during ecological momentary assessment in clinical research: Implications for suicide research. Journal of Clinical Psychology.

Regularized structural equation modeling

• Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling. doi:10.1080/10705511.2016.1154793 — foundational paper introducing the regsem R package.
• 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.
• Jacobucci, R. & Grimm, K. J. (2018). Comparison of frequentist and Bayesian regularization in structural equation modeling. Structural Equation Modeling.
• Li, X. & Jacobucci, R. (2022). Regularized structural equation modeling with stability selection. Psychological Methods. doi:10.1037/met0000389

Book

• Jacobucci, R., Grimm, K. J., & Zhang, Z. (2023). Machine Learning for Social and Behavioral Research. Guilford Press. (Methodology in the Social Sciences series.)

Recent Publications by Year

2026

• Jacobucci, R., Shao, W., Kobrinsky, V., & Ammerman, B. Predicting momentary suicidal ideation from smartphone screenshots using vision-language models. JMIR Mental Health.
• Muehlenkamp, J. J., O’Brien, C. M., Jacobucci, R., & Ammerman, B. A. Sleep problems alter proximal risk of negative self-perceptions on suicide risk. Suicide and Life-Threatening Behavior.
• Blacutt, M., Jacobucci, R., & Ammerman, B. A. The dynamic relationship between alcohol and suicidal ideation. Journal of Affective Disorders (in press).

2025

• Jacobucci, R., Jones, S. G., Blacutt, M., & Ammerman, B. A. Passive vs active nighttime smartphone use as markers of next-day suicide risk. JAMA Network Open.
• Jacobucci, R., Blacutt, M., Ram, N., & Ammerman, B. A. Smartphone screen time and suicide risk in daily life captured through high-resolution screenshot data. npj Digital Medicine.
• Ammerman, B. A., Kleiman, E. M., O’Brien, C., Knorr, A. C., Bell, K. A., Ram, N., …, & Jacobucci, R. Smartphone-based text obtained via passive sensing as it relates to direct suicide risk assessment. Psychological Medicine.
• Shao, S. S., Xu, Z., Liu, Q., McClure, K., Jacobucci, R., Maxwell, S. E., & Zhang, Z. Zero inflation in intensive longitudinal data: Why is it important and how should we deal with it? Psychological Methods.
• Blacutt, M., Jacobucci, R., & Ammerman, B. A. Modeling the dynamics of perceived burdensomeness, thwarted belongingness, and suicidal ideation in continuous time. Journal of Psychopathology and Clinical Science.
• McClure, K., Ammerman, B. A., Liu, C., Blacutt, M., O’Brien, C., & Jacobucci, R. Initial development of a multidimensional computerized adaptive test for intensive longitudinal assessment of suicide risk. JMIR Formative Research.
• Xu, I., Jacobucci, R., & Ammerman, B. A. Before the attempt: How people think and plan in suicide crises. Journal of Affective Disorders.
• Ammerman, B. A., McClure, K., Law, K. C., O’Loughlin, C. M., & Jacobucci, R. Online disclosure of suicide method: What can online posts tell us about suicidal planning? Journal of Psychiatric Research.
• Abber, S. R., Billman Miller, M. G., Hamilton, A., Ortiz, S. N., Jacobucci, R. C., et al. Bulimia nervosa severity levels based on shape/weight overvaluation. Psychological Medicine.
• Billman Miller, M., Abber, S., Hamilton, A., Ortiz, S., Jacobucci, R., Essayli, J., Smith, A., & Forrest, L. Data mining identifies meaningful severity specifiers for anorexia nervosa. Journal of Psychopathology and Clinical Science.
• Swartz, J., Zhao, P., Watson, D., Jacobucci, R., et al. Associations among drug acquisition and use behaviors, demographics, psychosocial risk factors, and opioid-involved overdoses: A SEM analysis. Drug and Alcohol Dependence.
• Shao, S., Xu, Z., Qu, W., & Jacobucci, R. Sample size planning and power analysis for detecting cross-lagged effects in longitudinal studies with ordinal outcomes. Fudan Journal of the Humanities and Social Sciences.

2024

• Jacobucci, R., Ammerman, B., & Ram, N. Examining passively collected smartphone-based data in the days prior to psychiatric hospitalization for a suicidal crisis. JMIR Formative Research.
• Jacobucci, R., Ammerman, B., & McClure, K. Understanding momentary missingness during ecological momentary assessment in clinical research. Journal of Clinical Psychology.
• McClure, K., Ammerman, B., & Jacobucci, R. On the selection of item scores or composites for clinical prediction. Multivariate Behavioral Research.
• Wilcox, K. T., Jacobucci, R., Waite, E., Dixon-Gordon, K. L., McCloskey, M. S., & Ammerman, B. A. A text mining approach to characterizing interpersonal stress among individuals with a nonsuicidal self-injury history. Current Psychology.
• Muehlenkamp, J. J., Jacobucci, R., & Ammerman, B. Body appreciation protects against proximal self-harm urges in a clinical sample of adults. Journal of Psychopathology and Behavioral Assessment.
• Ammerman, B. A., O’Brien, C., Park, Y., & Jacobucci, R. Momentary associations between positive coping and nonsuicidal self-injury risk among individuals with problematic alcohol use. Crisis.
• Ammerman, B. & Jacobucci, R. Proximal risk pathways of momentary alcohol urges and use: Differences based on suicidal ideation history. Current Psychology.

2023

• Jacobucci, R., Grimm, K. J., & Zhang, Z. Machine Learning for Social and Behavioral Research. Guilford Press (book).
• Jacobucci, R. & Ammerman, B. Examining the dynamic relationship between nonsuicidal self-injury and alcohol use experiences. Suicide and Life-Threatening Behaviors.
• Jacobucci, R., McClure, K., & Ammerman, B. Comparing the role of perceived burdensomeness and thwarted belongingness in prospectively predicting active suicidal ideation. Suicide and Life-Threatening Behaviors.
• Wilcox, K. T., Jacobucci, R., Zhang, Z., & Ammerman, B. A. Supervised latent Dirichlet allocation with covariates: A Bayesian structural and measurement model of text and covariates. Psychological Methods. doi:10.1037/met0000541
• McClure, K., Bell, K., Jacobucci, R., & Ammerman, B. A. Measurement invariance and response consistency of single-item assessments of suicidal thoughts and behaviors. Psychological Assessment.
• Ammerman, B. & Jacobucci, R. The impact of social connection on near-term suicidal ideation. Psychiatry Research.

2022

• Jacobucci, R. A critique of using the labels confirmatory and exploratory in modern psychological research. Frontiers in Psychology.
• Jacobucci, R. & Li, X. Does minority case sampling improve performance with imbalanced outcomes in psychological research? Journal of Behavioral Data Science.
• Li, X. & Jacobucci, R. Regularized structural equation modeling with stability selection. Psychological Methods.
• Grimm, K. J., Jacobucci, R., Stegmann, G., & Serang, S. Explorations of individual change processes and their determinants. Multivariate Behavioral Research.
• Burke, T. A., Shao, S., Jacobucci, R., Kautz, M., Alloy, L. B., & Ammerman, B. A. Examining momentary associations between behavioral approach system indices and nonsuicidal self-injury urges. Journal of Affective Disorders.
• Case, J. A. C., Sullivan-Toole, H., Mattoni, M., Jacobucci, R., Forbes, E. E., & Olino, T. M. Evaluating the item-level factor structure of anhedonia. Journal of Affective Disorders.

2021

• Jacobucci, R., Littlefield, A., Millner, A. J., Kleiman, E. M., & Steinley, D. Evidence of inflated prediction performance: A commentary on machine learning and suicide research. Clinical Psychological Science.
• Jacobucci, R., Ammerman, B. A., & Wilcox, K. 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. Using ordinal regression for advancing the understanding of distinct suicide outcomes. Suicide and Life-Threatening Behavior.
• Littlefield, A. K., Cooke, J. T., Bagge, C., Glenn, C., Kleiman, E. M., Jacobucci, R., Millner, A. J., & Steinley, D. Machine learning to classify suicidal thoughts and behaviors. Clinical Psychological Science.
• Ammerman, B. A., Burke, T. A., Jacobucci, R., & McClure, K. How we ask matters: The impact of question wording in single-item measurement of suicidal thoughts and behaviors. Preventive Medicine.
• Ammerman, B. A., Burke, T. A., Jacobucci, R., & McClure, K. Preliminary investigation of the association between COVID-19 and suicidal thoughts and behaviors in the U.S. Journal of Psychiatric Research.
• Grimm, K. J., & Jacobucci, R. Reliable trees: Reliability-informed recursive partitioning for psychological data. Multivariate Behavioral Research.

2020

• Jacobucci, R. & Grimm, K. J. Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science.
• Jiang, M., Ammerman, B. A., Zeng, Q., Jacobucci, R., & Brodersen, A. Phrase-level pairwise topic modeling to uncover helpful peer responses to online suicidal crises. Humanities & Social Sciences Communications.
• Forrest, L., Jacobucci, R., & Grilo, C. M. Empirically-determined severity levels for binge-eating disorder outperform existing severity classification schemes. Psychological Medicine.
• Hong, M., Jacobucci, R., & Lubke, G. Deductive data mining. Psychological Methods.
• Burke, T. A., Jacobucci, R., Ammerman, B. A., & Diamond, G. Using machine learning to classify suicide attempt history among youth in medical care settings. Journal of Affective Disorders.
• Serang, S. & Jacobucci, R. Exploratory mediation analysis of dichotomous outcomes via regularization. Multivariate Behavioral Research.
• Liang, X., & Jacobucci, R. Regularized structural equation modeling to detect measurement bias. Structural Equation Modeling.

Earlier highlights (2014–2019)

• 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.
• Jacobucci, R., Serang, S., & Grimm, K. J. (2019). A short note on complications in interpretation with the dual change score model. Structural Equation Modeling.
• 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. & Grimm, K. J. (2018). Comparison of frequentist and Bayesian regularization in structural equation modeling. Structural Equation Modeling.
• Ammerman, B. A., Jacobucci, R., Kleiman, E. M., Uyeji, L., & McCloskey, M. S. (2018). 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.
• Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling.
• 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. Psychology and Aging.

Complete Publication List

For the full list of peer-reviewed publications (60+), book chapters, and ongoing work, please see my Google Scholar profile or CV.