Publications
Research Impact Overview
With 2,668+ citations and an active research program spanning machine learning, clinical psychology, and statistical methodology, my work focuses on developing computational methods for understanding complex psychological phenomena. My research has been particularly influential in advancing regularized structural equation modeling and examining the intersection of measurement and machine learning in psychology.
Featured Recent Publications (2020-2025)
2024
Examining Passively Collected Smartphone-Based Data in the Days Prior to Psychiatric Hospitalization for a Suicidal Crisis
JMIR Formative Research, 2024
Authors: Jacobucci, R., Ammerman, B., & Ram, N.
Key Impact: This groundbreaking study introduces “screenomics” - digital phenotyping through smartphone screenshots - to predict psychiatric hospitalization. The research demonstrates how passive smartphone data collection can identify suicide risk patterns, representing a major advance in digital mental health assessment.
Understanding Momentary Missingness During Ecological Momentary Assessment in Clinical Research
Journal of Clinical Psychology, 2024
Authors: Jacobucci, R., Ammerman, B., & McClure, K.
Key Impact: Addresses critical methodological challenges in EMA research, providing practical guidance for handling missing data in real-time clinical assessments - essential for advancing mobile health interventions.
2023
Machine Learning for Social and Behavioral Research (Book)
Guilford Press, 2023
Authors: Jacobucci, R., Grimm, K. J., & Zhang, Z.
Key Impact: Comprehensive 416-page guide bridging machine learning and social science research. Featured as a key resource in the Methodology in the Social Sciences series, providing researchers with practical tools for large-scale data analysis.
2022
A Critique of Using the Labels Confirmatory and Exploratory in Modern Psychological Research
Frontiers in Psychology, 2022
Author: Jacobucci, R.
Key Impact: Challenges traditional research categorizations, arguing for more nuanced approaches to psychological research methodology in the age of big data and machine learning.
Regularized Structural Equation Modeling with Stability Selection
Psychological Methods, 2022
Authors: Li, X., & Jacobucci, R.
Key Impact: Extends the RegSEM framework with stability selection methods, improving model selection reliability and advancing the field’s approach to complex structural models.
2021
Evidence of Inflated Prediction Performance: A Commentary on Machine Learning and Suicide Research
Clinical Psychological Science, 2021
Authors: Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E. M., & Steinley, D.
Key Impact: Critical methodological paper demonstrating how bootstrap resampling with nonlinear machine learning produces inflated performance estimates. Essential reading for suicide research methodology.
2020
Machine Learning and Psychological Research: The Unexplored Effect of Measurement
Perspectives on Psychological Science, 2020
Authors: Jacobucci, R. & Grimm, K. J.
Key Impact: Highly Cited - Top 25% of Altmetric scores. Demonstrates that measurement error prevents machine learning algorithms from modeling nonlinear relationships effectively. Foundational paper showing that “garbage in, garbage out” applies critically to ML in psychology.
Foundational Contributions
Regularized Structural Equation Modeling (RegSEM)
Regularized Structural Equation Modeling
Structural Equation Modeling, 2016
Authors: Jacobucci, R., Grimm, K. J., & McArdle, J. J.
Key Impact: Most Cited Work - Introduced RegSEM methodology, extending regularization (lasso/ridge) to structural equation models. The accompanying R package regsem has become a standard tool in the field, addressing the replication crisis by emphasizing model generalizability.
A Practical Guide to Variable Selection in Structural Equation Modeling Using Regularized Multiple-Indicators, Multiple-Causes Models
Advances in Methods and Practices in Psychological Science, 2019
Authors: Jacobucci, R., Brandmaier, A. M., & Kievit, R. A.
Key Impact: Provides practical implementation guidance for regularized SEM, making advanced statistical methods accessible to applied researchers.
Research Program Impact
My research addresses fundamental questions at the intersection of:
- Clinical Psychology & Technology: Developing smartphone-based assessment tools for suicide risk detection
- Statistical Methodology: Advancing structural equation modeling through regularization techniques
- Machine Learning & Psychology: Examining measurement quality’s critical role in ML applications
- Methodological Rigor: Addressing replication concerns through improved statistical practices
| Total Citations: 2,668+ | Focus Areas: Machine Learning, Suicide Research, Structural Equation Modeling |
Complete Publication List
For a comprehensive list of all publications, please visit my Google Scholar profile.