6 139 NETWORK-BASED MODEL OF RISK OF SEXUAL REOFFENDING reoffending. An alternative approach to examining the proposition on the relative influence of dynamic risk factors is to test the hypothesis that treatment and risk management strategies focusing on dynamic risk factors with relatively high influence on a network result in a relatively larger reduction in future sexual offending (van den Berg et al., 2022). Assuming future studies indeed demonstrate the relative stronger influence of specific dynamic risk factors on the network of dynamic risk factors (and thus on the risk of sexual reoffending), this can be expected to have not only theoretical but also clinical relevance. After all, devoting attention to these dynamic risk factors in both risk management and treatment of adult men with a history of sexual offenses could result in a substantial decrease in risk of sexual reoffending. The reverse also applies: If future research shows certain dynamic risk factors to have relatively little impact on the risk of sexual reoffending, treatment providers and probation officers might either eliminate or markedly reduce their focus on such factors (van den Berg et al., 2022). This section presents empirically testable propositions and hypotheses derived from the NBM-RSR. Testing these hypotheses requires a statistical approach that differs from and extends what typically has been done based on the Propensities Model. For this reason, we include a description of how the challenge of statistically detecting interactions among interrelated dynamic risk factors in a network might be addressed. 6.3.2 DETECTION INTERACTIONS IN A NETWORK OF DYNAMIC RISK FACTORS The NBM-RSR is in part inspired by the network approach to psychopathology (Borsboom, 2017; Borsboom et al., 2019; Borsboom et al., 2021; Robinaugh et al., 2019). Empirical research based on this approach has increased exponentially over the past decade (Burger et al., 2022; McNally, 2021). This research typically uses network analysis to statistically detect interactions within a disorder’s symptom network.1 To construct and assess the network structure of interrelated dynamic risk factors, first a pairwise Markov random field is estimated (PMRF; Costantini et al., 2015; van Borkulo et al., 2014). A PMRF can essentially be considered a partial correlation network, that is a network in which an association between two dynamic risk factors is conditioned on, or controlled for, all other dynamic risk factors in the network (Isvoranu et al., 2022). In a PMRF, dynamic risk factors connected by edges indicate conditional dependence (Epskamp, Borsboom, & Fried, 2018; Isvoranu et al., 2022). However, some spurious connections may result from sampling error. To control for such spurious connections a technique can be employed that relies on the extended Bayesian information criteria 1 Statistical networks can be constructed and investigated using the R software environment (R Core Team, 2022). See Borsboom and colleagues (2021) for a more extensive introduction on network analysis in psychological science, Isvoranu and colleagues (2022) for an accessible textbook on network psychometrics for both novices and experienced researchers, and Burger and colleagues (2020) for guidance on reporting network analytic results in a scientific paper. Examples of r-codes to estimate and analyze networks of dynamic risk factors can be found in the supplementary materials of previous studies (regarding cross-sectional data: van den Berg et al., 2020; van den Berg et al., 2022; regarding longitudinal ESM data: van den Berg et al., 2023).
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