Cited by 1,371Mental Health Research TopicsPsychometric Methodologies and TestingBayesian Modeling and Causal Inference

Network analysis of multivariate data in psychological science

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Borsboom, Denny, Deserno, Marie K., Rhemtulla, Mijke, Epskamp, Sacha, Fried, Eiko I., McNally, Richard J., Robinaugh, Donald J., Perugini, Marco +7 more·2021·Nature Reviews Methods Primers

Abstract

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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Research works printed but not linked: 5

  1. Newman, M. E. J. Networks: An Introduction (Oxford University Press, 2010). This text is an ideal introduction to network science and the associated mathematical modelling techniques.
  2. Newman, M. E. J., Barabási, A. L. E. & Watts, D. J. The Structure and Dynamics of Networks (Princeton University Press, 2006).
  3. Pearl, J. Causality: Models, Reasoning, and Inference (Cambridge University Press, 2000). This crucial book makes the connection between conditional independence patterns and causal structures.
  4. Chen, B., Pearl, J. & Kline, R. Graphical tools for linear path models. Psychometrika 4, R432 (2018).
  5. Barabasi, A. L. Network Science (Cambridge University Press, 2018). This text is an authoritative overview of network science.

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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
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
Open access
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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