Estimating psychological networks and their accuracy: A tutorial paper

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 bee...

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Veröffentlicht in:Behavior research methods Jg. 50; H. 1; S. 195 - 212
Hauptverfasser: Epskamp, Sacha, Borsboom, Denny, Fried, Eiko I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.02.2018
Springer Nature B.V
Schlagworte:
Accuracy
Behavior
Behavioral Science and Psychology
Bootstrap method
Cognitive Psychology
Dimensional Measurement Accuracy
Female
Humans
Indexes
Network centrality
Networks
Neural Networks, Computer
Popularity
Post traumatic stress disorder
Psychology
Psychophysiology - methods
Sampling
Simulation
Statistical methods
Stress Disorders, Post-Traumatic - psychology
Syntactic analysis
Syntax
Women
Tutorial
Bootstrap
Psychological networks
Network psychometrics
Replicability
ISSN:1554-3528, 1554-351X, 1554-3528
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:1554-3528
1554-351X
1554-3528
DOI:10.3758/s13428-017-0862-1