A tutorial on regularized partial correlation networks
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Abstract
Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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An L1-penalized regression was used to avoid estimating spurious relations among variables.
“To avoid estimating spurious relations among variables, we needed some form of control that accounts for these false associations; to this end, we used an L1-penalized regression (Epskamp & Fried, 2018).”
From Exploring the psychology of suicidal ideation: a theory driven network analysis. (2019) · cites this paper as Epskamp & Fried, 2018
The network was regulated using the graphic least absolute shrinkage and selection operator (LASSO) to avoid overfitting and to elevate interpretability of the network model structure.
“Using the graphic least absolute shrinkage and selection operator (LASSO) [ 51 ], the network was regulated to avoid overfitting and to elevate the interpretability of a network model structure.”
From Anxiety and Depression Symptoms among Youth Survivors of Childhood Sexual Abuse: A Network Analysis (2023) · cites this paper as [51]
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WITHDRAWNPreprint
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If intelligence is a cause, it is a within-subjects cause
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The Gaussian Graphical Model in Cross-Sectional and Time-Series Data
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Self-citationDiscovering Psychological Dynamics in Time-Series DataPreprint
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Self-citationA Note on the Quadratic Exponential Binary Distribution
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The Gaussian Graphical Model in Cross-sectional and Time-series Data
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Research works printed but not linked: 3
- Epskamp, S. (Ed.). (2017). Discussion: The road ahead. Network Psycho- metrics (Doctoral Dissertation). Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands.
- Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D., & Rubel, J. A. (2017). Exploring the idiographic dynamics of mood and anxiety via network analysis. Journal of Abnormal Psychology, 126, 1044 -1056.
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Network Analysis and Precision Rehabilitation for the Post-concussion Syndrome
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Psychological networks in clinical populations: investigating the consequences of Berkson's bias
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What Is Central to Political Belief System Networks?
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A network analysis investigation of the cognitive-behavioral theory of eating disorders
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Toward an Integrative Psychometric Model of Emotions
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A network approach to parental burnout
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Quantifying the Reliability and Replicability of Psychopathology Network Characteristics
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Longitudinal network structure of depression symptoms and self-efficacy in low-income mothers
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“The EBICglasso estimator was used to fit a Graphical Gaussian model to the partial polychoric correlation matrix of the data (Foygel and Drton 2010; Epskamp and Fried 2018).”
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Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults
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Self-citationA Cross-Country Network Analysis of Adolescent Resilience
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Self-Boundary Dissolution in Meditation: A Phenomenological Investigation
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How to Compare Psychometric Factor and Network Models
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Self-citationZavlis, O., Butter, S., Bennett, K. M., Hartman, T. K., Hyland, P., Mason, L., McBride, O., Murphy, J., Miller, J. G., Levita, L., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721000635 · Cited by 100 · see citing works
Liang, S., Liu, C., Rotaru, K., Li, K., Wei, X., Yuan, S., Yang, Q., Ren, L., & Liu, X. (2022) · Psychiatry Research · doi:10.1016/j.psychres.2022.114863 · Cited by 100 · see citing works
Burger, J., van der Veen, D. C., Robinaugh, D. J., Quax, R., Riese, H., Schoevers, R. A., & Epskamp, S. (2020) · BMC Medicine · doi:10.1186/s12916-020-01558-1 · Cited by 99 · see citing works
Self-citationKonaç, D., Young, K. S., Lau, J. Y. F., & Barker, E. D. (2021) · Journal of Psychopathology and Behavioral Assessment · doi:10.1007/s10862-021-09880-5 · Cited by 97 · see citing works
Rouquette, A., Pingault, J., Fried, E. I., Orri, M., Falissard, B., Kossakowski, J. J., Vitaro, F., Tremblay, R. E., Côté, S. M., & Borsboom, D. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.2119 · Cited by 96 · see citing works
Self-citationFritz, J., Fried, E. I., Goodyer, I. M., Wilkinson, P. O., & van Harmelen, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-34130-2 · Cited by 95 · see citing works
Self-citationCervin, M., Miguel, E. C., Güler, A. S., Ferrão, Y. A., Yıldırım, A. B. E., Lázaro, L., Gökçe, S., GELLER, D. A., Yulaf, Y., Başgül, Ş. S., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291720005437 · Cited by 95 · see citing works
Schellekens, M. P. J., Wolvers, M. D. J., Schroevers, M. J., Bootsma, T. I., Cramer, A. O. J., & van der Lee, M. L. (2020) · Journal of Behavioral Medicine · doi:10.1007/s10865-019-00084-7 · Cited by 93 · see citing works
Symptom clusters in 1330 survivors of 7 cancer types from the PROFILES registry: A network analysis
de Rooij, B. H., Oerlemans, S., Deun, K. V., Mols, F., de Ligt, K. M., Husson, O., Ezendam, N. P. M., Hoedjes, M., van de Poll-Franse, L. V., & Schoormans, D. (2021) · Cancer · doi:10.1002/cncr.33852 · Cited by 93 · see citing works
Levinson, C. A., Vanzhula, I. A., Brosof, L. C., & Forbush, K. T. (2018) · Current Psychiatry Reports · doi:10.1007/s11920-018-0930-y · Cited by 91 · see citing works
Sheridan, M. A., Shi, F., Miller, A. B., Salhi, C., & McLaughlin, K. A. (2019) · Developmental Science · doi:10.1111/desc.12934 · Cited by 91 · see citing works
Utility and validity of the Brief Psychiatric Rating Scale (BPRS) as a transdiagnostic scale
Hofmann, A. B., Schmid, H. M., Jabat, M., Brackmann, N., Noboa, V., Bobes, J., García‐Portilla, M. P., Seifritz, E., Vetter, S., & Egger, S. (2022) · Psychiatry Research · doi:10.1016/j.psychres.2022.114659 · Cited by 91 · see citing works
Wichers, M., Riese, H., Hodges, T. M., Snippe, E., & Bos, F. M. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.719490 · Cited by 90 · see citing works
Network models of posttraumatic stress disorder: A meta-analysis.
Isvoranu, A., Epskamp, S., & Cheung, M. W. ‐. (2021) · Journal of Abnormal Psychology · doi:10.1037/abn0000704 · Cited by 90 · see citing works
Self-citationMonteleone, A. M., Mereu, A., Cascino, G., Criscuolo, M., Castiglioni, M. C., Pellegrino, F., Patriciello, G., Ruzzi, V., Monteleone, P., Vicari, S., et al. (2019) · International Journal of Eating Disorders · doi:10.1002/eat.23137 · Cited by 89 · see citing works
A network approach to climate change anxiety and its key related features
Heeren, A., Mouguiama-Daouda, C., & McNally, R. J. (2023) · Journal of Anxiety Disorders · doi:10.1016/j.janxdis.2022.102625 · Cited by 88 · see citing works
- Authors
- Epskamp, S., & Fried, E. I.
- Affiliations
- University of Amsterdam (NL)
- Venue
- Psychological Methods
- Published
- 2018-12
- Volume
- 23
- Issue
- 4
- Pages
- 617-634
- ISSN
- 1082-989X (ISSN-L)
- 1939-1463
- OpenAlex
- W2462099493
- Cited by
- 2,918
- References
- 125
- Citations by year
- 2026: 591 · 2025: 566 · 2024: 471 · 2023: 373 · 2022: 283 · 2021: 264 · 2020: 187 · 2019: 109 · 2018: 46 · 2017: 17 · 2016: 7
- FWCI
- 198.77
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Psychological Methods (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- network modeling, psychological factors, regularization, hyperparameter optimization, non-normal data, sample size determination, post-traumatic stress disorder, network structure estimation, R implementation
- MeSH terms
- Data Interpretation, Statistical, Humans, Models, Psychological, Psychology, Stress Disorders, Post-Traumatic, Models, Statistical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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