3 65 NETWORK ANALYSIS DYNAMIC RISK FACTORS 3.3 NETWORK ANALYSIS Within the network approach of psychopathology, constructs such as “mental disorders” are considered to arise not from a latent syndromal structure but from the direct interaction among symptoms (Borsboom, 2017; Borsboom et al., 2019). When causal connections between symptoms - which can involve a myriad of biological, social, and psychological mechanisms - are sufficiently strong, a self-sustaining equilibrium will occur within the network. External factors (e.g., life events such as losing a partner) can influence symptoms and thus the activity within the network, which results in a new equilibrium and - when sufficient symptoms become active - cause a mental disorder. Estimated (sexual) offending risk arises from the accumulation of its various (risk) factors. After all, risk assessment instruments are, fundamentally, prognostic tools, given that they serve to predict a continuous construct (i.e., the risk of recidivism; Helmus & Babchishin, 2017). From a network-analytic point of view, recidivism risk increases if more (interrelated) dynamic risk factors become activated for a sufficiently long duration to engage in feedback loops that sustain network activation. Conversely, a reduction in recidivism risk may occur when dynamic risk factors become deactivated or if the connection between them weakens or dissolves (McNally, 2016). However, interrelated risk factors consisting of observed patterns of behavior, thought, and emotion could also (in part) be manifestations of latent variables (Brouillette-Alarie et al., 2016; Epskamp et al., 2017), in which case indicators that depend on the same latent variable would be expected to form fully connected networks. Note that the statistical models used in this article identify networks of conditional dependencies between variables but cannot by themselves distinguish between various explanations for why these dependencies arise; this requires additional experimental research or, if such research is unfeasible, the assessment of plausibility on the basis of theoretical arguments. Statistical network analysis has proven to be a useful tool to detect and visualize the interrelationships among factors or subfactors of a construct (Jones et al., 2018). These analyses also provide information on which factors in a network of factors may play a central role. If the pathways in the network structure indeed represent causal and directed interactions between the network components, then dynamic risk factors causing a relatively large number of other dynamic risk factors are considered more central and should have a greater influence on other dynamic risk factors, in turn contributing to changes in the risk of recidivism. This information can be visualized graphically using nodes and edges; dynamic risk factors are represented as nodes, and edges are a function of statistical associations among risk factors. In addition, centrality metrics can be computed, which allow for the generation of hypotheses on the relative importance of nodes in the network. Other analytical approaches (e.g., structural equation modeling, or SEM) can also accommodate reciprocal effects and provide exploratory models, although they are not
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