Category

Date Issued:

25.08.2023

DOI:

10.17185/duepublico/78932

Language:

English

Type of Resource:

Software, multimedia

Extent:

5 KB

Institution (Creator):

Clinic for Psychosomatic Medicine and Psychotherapy, LVR-University Hospital Essen, University of Duisburg-Essen, Essen, Germany

Keywords:

1. R script

   Typ

   Schlagwort

2. Network analysis

   Typ

   Schlagwort

Subject categories of the Deutsche Nationalbibliografie:

610 Medical sciences Medicine

Hosting institution:

Faculty of Medicine, Essen University Hospital, LVR-Klinikum Essen

Hosting institution:

Faculty of Medicine, Essen University Hospital, Center for Translational Neuro- and Behavioral Science (C-TNBS)

Hosting institution:

Faculty of Educational Sciences, Institut für Sport- und Bewegungswissenschaften

creation/production credits:

Network Analysis was performed using the packages qgraph, igraph, bootnet, and EGAnet (Csardi & Nepusz, 2006; Epskamp et al., 2012; Golino & Epskamp, 2017). Centrality indices were computed and assessment of the network's stability and accuracy was conducted via bootnet. Missing data was addressed using listwise deletion, with the minimum sample size set to 250-350 participants to ensure sufficient power for the analysis of networks with 20 nodes or fewer (Constantin et al., 2021). The study estimated and visualized the network using a gaussian graphical model (Epskamp & Fried, 2018). Depressive symptoms, somatic symptom disorder, generalized anxiety, distress, mild to moderate injuries, severe injuries, years in elite sports, substance use, financial situation and training units per week were selected as nodes, resulting in a total of 11 nodes in the network. The dependencies among the variables were represented as edges in the network based on partial correlations (Epskamp & Fried, 2018). According to Epskamp and Fried (2018), gLASSO and EBIC (Chen & Chen, 2008; Friedman et al., 2008) methods were applied, with a tuning parameter of 0.5. The tuning parameter of 0.5 was chosen to create a parsimonious network with a higher specificity, as suggested by Epskamp and Fried (Epskamp and Fried, 2018). The centrality indices were then calculated to determine the importance of each node in the network. These indices included degree centrality, strength, closeness, and betweenness (Hevey, 2018). Degree centrality is the sum of all edges of a node, strength is the sum of the edge weights of all edges of a node, closeness measures the average distance of a node to other nodes, and betweenness identifies the role of a node in connecting other nodes (Hevey, 2018). The centrality indices are intended to provide clues as to which constructs are particularly relevant in the context of various mental health and sport variables (Epskamp and Fried, 2018). The stability and accuracy of the network were evaluated through different bootstrap procedures, including an edge weight variation analysis (Isvoranu et al., 2021) and a correlation stability analysis. It is recommended that, in order to interpret centrality with confidence, stability coefficients should exceed at least .25 and ideally surpass .50 (Epskamp et al., 2018a). The interpretability of the edge weight, node strength, and centrality indices was also assessed.

publications:

Geiger S, Jahre LM, Aufderlandwehr J, Krakowczyk JB, Esser AJ, Mühlbauer T, Skoda E-M, Teufel M and Bäuerle A (2023) Mental health symptoms in German elite athletes: a network analysis. Front. Psychol. 14:1243804. doi: 10.3389/fpsyg.2023.1243804

citation/reference:

Chen, J., and Chen, Z. (2008). Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95(3), 759-771. doi: 10.1093/biomet/asn034.
    Constantin, M.A., Schuurman, N.K., and Vermunt, J. (2021). A General Monte Carlo Method for Sample Size Analysis in the Context of Network Models. doi: 10.31234/osf.io/j5v7u.
    Csardi, G., and Nepusz, T. (2006). "The Igraph Software Package for Complex Network Research", in: InterJournal
    Epskamp, S., Borsboom, D., and Fried, E.I. (2018a). Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods 50(1), 195-212. doi: 10.3758/s13428-017-0862-1.
    Epskamp, S., Borsboom, D., and Fried, E.I. (2018b). Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol 14, 299-326.
    Epskamp, S., Cramer, A.O.J., Waldorp, L.J., Schmittmann, V.D., and Borsboom, D. (2012). qgraph: Network Visualizations of Relationships in Psychometric Data. J. Stat. Softw. 48(4), 1-18. doi: 10.18637/jss.v048.i04.
    Epskamp, S., and Fried, E.I. (2018). A tutorial on regularized partial correlation networks. Psychol Methods 23(4), 617-634. doi: 10.1037/met0000167.
    Friedman, J., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3), 432-441. doi: 10.1093/biostatistics/kxm045.
    Golino, H.F., and Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS One 12(6), e0174035. doi: 10.1371/journal.pone.0174035.
    Hevey, D. (2018). Network analysis: a brief overview and tutorial. Health Psychol Behav Med 6(1), 301-328. doi: 10.1080/21642850.2018.1521283.
    Isvoranu, A.M., Abdin, E., Chong, S.A., Vaingankar, J., Borsboom, D., and Subramaniam, M. (2021). Extended network analysis: from psychopathology to chronic illness. BMC Psychiatry 21(1), 119. doi: 10.1186/s12888-021-03128-y.