71
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Estimating psychological networks and their accuracy: A tutorial paper

      research-article

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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.

          Electronic supplementary material

          The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          Sparse inverse covariance estimation with the graphical lasso.

          We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Extended Bayesian information criteria for model selection with large model spaces

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Reliability and validity of a brief instrument for assessing post-traumatic stress disorder

                Bookmark

                Author and article information

                Contributors
                sacha.epskamp@gmail.com
                Journal
                Behav Res Methods
                Behav Res Methods
                Behavior Research Methods
                Springer US (New York )
                1554-351X
                1554-3528
                24 March 2017
                24 March 2017
                2018
                : 50
                : 1
                : 195-212
                Affiliations
                ISNI 0000000084992262, GRID grid.7177.6, Department of Psychology, , University of Amsterdam, ; Amsterdam, The Netherlands
                Article
                862
                10.3758/s13428-017-0862-1
                5809547
                28342071
                50c607b9-3a81-4f2a-ae0d-b4be5bd8d9bd
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Funding
                Funded by: NWO "research talent"
                Award ID: 406-11-066
                Categories
                Article
                Custom metadata
                © Psychonomic Society, Inc. 2018

                Clinical Psychology & Psychiatry
                network psychometrics,psychological networks,replicability,bootstrap,tutorial

                Comments

                Comment on this article