3 71 NETWORK ANALYSIS DYNAMIC RISK FACTORS 3.5.3.3 Shortest paths To analyze patterns of connectivity for particular dynamic risk factors, we used graphical representations that indicate the shortest paths from all dynamic risk factors to sexual and violent (including sexual contact) recidivism (Isvoranu et al., 2017). In our study, which involves weighted networks, shortest paths are paths with the maximum product of weights. These representations can be seen as road maps that show the shortest route between nodes. The shortest path in a partial correlation network from Node A to Node C via Node B may in addition suggest that Node B mediates the predictive relation between Nodes A and C (Langley et al., 2015). However, it should be noted that mediation was not explicitly tested in our study. Furthermore, to avoid misconceptions, we note that the term shortest path - which is commonly used within the network approach - should not be interpreted as different path, which implies an incompatible route to sexual offending. By using the term shortest path, we do not refer to mutually exclusive routes to sexual offending but to the sequence of nodes through which a particular dynamic risk factor is connected with recidivism via a series of regression equations. 3.5.3.4 Network stability To check the robustness of the estimated networks (i.e., the degree to which they are affected by sampling variation) and to examine the reliability of our inferences regarding strength centrality, we conducted a bootstrap procedure using 1,000 bootstraps for each network. To quantify stability of the estimated networks, we calculated the correlation stability coefficient (CS-coefficient). According to Epskamp et al. (2018), the CS-coefficient should be not below 0.25 and preferably above 0.5 to interpret centrality differences. Networks were constructed and investigated using the R software environment (Version 3.5.2; R Core Team, 2018) with the R packages qgraph (Version 1.6.1; Epskamp et al., 2012), mgm (Version 1.2-5; Haslbeck & Waldorp, 2016), igraph (Version 1.2.2; Csárdi, 2018), and bootnet (Version 1.2; Epskamp, Borsboom et al., 2018). Section C in the Supplemental Material available online contains the full R code for the network construction, node centrality, clustering, shortest paths, and network stability. 3.6 RESULTS 3.6.1 NETWORK CONSTRUCTION AND CENTRALITY OF DYNAMIC RISK FACTORS Figure 3.1 presents the network structure (Fig. 3.1a) and the centrality plot (Fig. 3.11b) for the dynamic risk factors (n = 788). All associations among risk factors were positive, which suggests that our set of risk factors featured no inhibitory connections. This is an expected effect of the way the items are formulated and scored in the STABLE-2007: Higher scores on any of the items refer to higher risk. We visually identified a cluster of the dynamic risk factors sexual preoccupation, sex as coping, deviant sexual interests, and emotional identification
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