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Machine Learning for Mental Health | Clinical Data Science | Research Innovation |
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Published in Structural Equation Modeling, 2016
Foundational paper introducing RegSEM methodology, extending regularization techniques to structural equation models.
Recommended citation: Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). "Regularized Structural Equation Modeling." Structural Equation Modeling, 23(4), 555-566.
Published in Perspectives on Psychological Science, 2020
Demonstrates that measurement error prevents machine learning algorithms from accurately modeling nonlinear relationships in psychological research.
Recommended citation: Jacobucci, R., & Grimm, K. J. (2020). "Machine Learning and Psychological Research: The Unexplored Effect of Measurement." Perspectives on Psychological Science, 15(3), 809-816.
Published in Frontiers in Psychology, 2022
This paper critiques the use of confirmatory and exploratory labels in modern psychological research.
Recommended citation: Jacobucci, R. (2022). "A Critique of Using the Labels Confirmatory and Exploratory in Modern Psychological Research." Frontiers in Psychology.
Published in Psychological Methods, 2022
This paper presents methods for regularized structural equation modeling using stability selection.
Recommended citation: Li, X., & Jacobucci, R. (2022). "Regularized structural equation modeling with stability selection." Psychological Methods, 27, 497-518.
Published in Guilford Press, 2023
A comprehensive guide to machine learning techniques for social and behavioral research applications.
Recommended citation: Jacobucci, R., Grimm, K. J., & Zhang, Z. (2023). "Machine learning for social and behavioral research." New York, NY: Guilford.
Published in Psychological Methods, 2023
This paper presents methods for supervised Latent Dirichlet Allocation incorporating covariates.
Recommended citation: Wilcox, K. T., et al. (2023). "Supervised Latent Dirichlet Allocation with Covariates." Psychological Methods.
Published in Handbook of Structural Equation Modeling, 2023
A handbook chapter covering machine learning approaches to structural equation modeling.
Recommended citation: Brandmaier, A. M., & Jacobucci, R. (2023). "Machine-learning approaches to structural equation modeling." Handbook of Structural Equation Modeling.
Published in Journal of Clinical Psychology, 2024
This paper examines patterns of missingness in ecological momentary assessment data within clinical research contexts.
Recommended citation: Jacobucci, R., Ammerman, B., & McClure, K. (2024). "Understanding momentary missingness during ecological momentary assessment in clinical research." Journal of Clinical Psychology.
Published in JMIR Formative Research, 2024
Groundbreaking study introducing “screenomics” - digital phenotyping through smartphone screenshots to predict psychiatric hospitalization for suicidal crises.
Recommended citation: 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, 8:e55999.
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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