Estimating psychological networks and their accuracy
A tutorial paper
Bibliographic Data
| ID | 23314014 |
|---|---|
| Authors | Sacha Epskamp (0000-0003-4884-8118, University of Amsterdam), Denny Borsboom (0000-0001-9720-4162, University of Amsterdam), Eiko I Fried (0000-0001-7469-594X, University of Amsterdam) |
| Year | 2018 |
| Volume | 50 |
| Issue | 1 |
| Pages | 195-212 |
| Publication date | 2018-02-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Behavior Research Methods (JOURNAL) |
| Journal identifiers | ISSN: 1554-351X • E-ISSN: 1554-3528 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.3758/s13428-017-0862-1 |
| PMID | 28342071 |
| OpenAlex | W2530237102 |
| Language | EN |
| Citations received | 1.061 |
| References cited | 53 |
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.
Centrality · Complex network · Data mining · Data science · Field (mathematics) · Machine learning · Network science · Popularity · Resampling · Stability (learning theory) · Statistics · Variation (astronomy) · Artificial Intelligence · Complex Network Analysis Techniques · Computer Science · Functional Brain Connectivity Studies · Mathematics · Mental Health Research Topics · Psychology · Social Psychology
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| Unique citing works | 1.061 |
|---|---|
| Citations per year | 117,89 |
| Citation span | 2017 - 2026 (10) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 1.049 |