- Author
-
D. Borsboom
M.K. Deserno
M. Rhemtulla
S. Epskamp
E.I. Fried
R.J. McNally
D.J. Robinaugh
M. Perugini
J. Dalege
G. Costantini
A.-M. Isvoranu
A.C. Wysocki
C.D. van Borkulo
R. van Bork
L.J. Waldorp - Date
- 19-8-2021
- Title
- Network analysis of multivariate data in psychological science
- Journal
- Nature Reviews Methods Primers
- Volume
- 1
- Article number
- 58
- Number of pages
- 18
- Document type
- Article
- Faculty
- Faculty of Social and Behavioural Sciences (FMG)
- Institute
- Psychology Research Institute (PsyRes)
- 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.
- URL
- go to publisher's site
- Other links
- corrigendum
dataset - Language
- English
- Note
- Correction published in: Nature Reviews Methods Primers, Volume 2, 21 February 2022, Article number: 10
- Persistent Identifier
- https://hdl.handle.net/11245.1/f1fc91c7-791b-4e8e-a1f6-4e2e97d0e1bb
- Downloads
-
s43586-021-00055-w1(Final published version)
- Supplementary materials
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