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).
Keywords
HyperparameterPartial correlationLatent variableComputer scienceCorrelationStructural equation modelingRegularization (linguistics)Machine learningArtificial intelligenceData miningEconometricsPsychologyMathematics
MeSH Terms
Data InterpretationStatisticalHumansModelsPsychologicalModelsStatisticalPsychologyStress DisordersPost-Traumatic
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Publication Info
- Year
- 2018
- Type
- article
- Volume
- 23
- Issue
- 4
- Pages
- 617-634
- Citations
- 2347
- Access
- Closed
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Cite This
Sacha Epskamp,
Eiko I. Fried
(2018).
A tutorial on regularized partial correlation networks..
Psychological Methods
, 23
(4)
, 617-634.
https://doi.org/10.1037/met0000167
Identifiers
- DOI
- 10.1037/met0000167
- PMID
- 29595293
- arXiv
- 1607.01367