Network analysis of multivariate data in psychological science
Open accessAbstract
In recent years, network analysis has been applied to identify and analyse patterns of statistical association in multivariate psychological data. In these approaches, network nodes represent variables in a data set, and edges represent pairwise conditional associations between variables in the data, while conditioning on the remaining variables. This Primer provides an anatomy of these techniques, describes the current state of the art and discusses open problems. We identify relevant data structures in which network analysis may be applied: cross-sectional data, repeated measures and intensive longitudinal data. We then discuss the estimation of network structures in each of these cases, as well as assessment techniques to evaluate network robustness and replicability. Successful applications of the technique in different research areas are highlighted. Finally, we discuss limitations and challenges for future research. Network analysis allows the investigation of complex patterns and relationships by examining nodes and the edges connecting them. Borsboom et al. discuss the adoption of network analysis in psychological research.
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Self-citation
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Social Drivers and Algorithmic Mechanisms on Digital Media.
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“…and algorithms, this research needs to incorporate methods from complexity science and computational social science, such as network analysis or agent-based modeling (Borsboom et al., 2021; Jackson et al., 2017; Smith & Conrey, 2007; Vlasceanu et al., 2018) to address these many open questions.”
MethodA Systematic Literature Review of Empirical Research on Epistemic Network Analysis in Education
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- Authors
- Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R., & Waldorp, L. J.
- Affiliations
- University of Amsterdam (NL)
- Max Planck Institute for Human Development (DE)
- University of California, Davis (US)
- Leiden University (NL)
- Harvard University (US)
- University of Milano-Bicocca (IT)
- Santa Fe Institute (US)
- Corresponding author
- Borsboom, Denny
- ORCID
- Venue
- Nature Reviews Methods Primers
- Published
- 2021-08-19
- Volume
- 1
- Issue
- 1
- ISSN
- 2662-8449 (ISSN-L)
- OpenAlex
- W3195599406
- Cited by
- 1,371
- References
- 147
- Citations by year
- 2026: 361 · 2025: 375 · 2024: 306 · 2023: 207 · 2022: 104 · 2021: 14 · 2020: 1
- FWCI
- 25.87
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-02-21)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Reviews Methods Primers (publishedVersion, public-domain)
- UvA-DARE (University of Amsterdam) (submittedVersion, public-domain)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- European Commission (952464, HORIZON2020, 889682)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek (019.191SG.005, 016–195-261)
- National Institute of Mental Health
- H2020 European Research Council (647209, 949059)
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- network analysis, multivariate data, conditional association, network structure estimation, network robustness, cross-sectional data, repeated measures, intensive longitudinal data
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