Estimating psychological networks and their accuracy: A tutorial paper

By Sacha Epskamp, Denny Borsboom, Eiko I. Fried. Behavior Research Methods, 2016

4,610 citations recorded by the source corpus.

The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/8566907e3c1b575fbe915bb85a6c009989a43287

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Citations in the corpus

Moving Forward: Challenges and Directions for Psychopathological Network Theory and Methodology (2017, methodology): “Although erring on the side of including rather too many than too few variables may seem an easy solution, current network studies are often underpowered (Epskamp et al., 2017)— with too few persons for the number of parameters we model.”
Network analysis: a brief overview and tutorial (2018, methodology): “However, as Epskamp et al. (2018a) note, relatively little attention has been paid towards examining the accuracy of the edge and centrality estimates.”
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data (2015, methodology): “The R-package bootnet (Epskamp, Borsboom, and Fried 2018) also implements a bootstrap scheme for mgm objects and provides similar plotting options.”
A Tutorial on Regularized Partial Correlation Networks (2016, methodology): “We refer the reader to Epskamp et al. (2017d) to a discussion of extending this framework to such temporally ordered datasets.”
Stability and variability of personality networks. A tutorial on recent developments in network psychometrics (2017, methodology): “Here we present how to implement in R amethod to infer such “within-subject” networks that was proposed by Epskamp et al. (2017).”
What do centrality measures measure in psychological networks? (2019, background): “, 2013), low stability in cross-sectional data (Epskamp et al., 2017), or inconsistency in findings regarding the most central node across datasets of similar psychological variables (Bringmann et al.”