4 85 REPLICATION AND COMPARISON NETWORKS 4.1 INTRODUCTION One of the core objectives of forensic practitioners working with adult males with a history of sexual offenses is to help them desist from future crimes (Gannon et al., 2019, Tyler et al., 2021). To reduce the risk of reoffending, forensic workers focus mostly on dynamic risk factors (Bonta & Andrews, 2017; Hanson et al., 2009, Heffernan & Ward, 2020; van den Berg et al., 2018). Dynamic risk factors involve behavioral and psychological characteristics considered modifiable through treatment and risk management interventions, whose change is believed to affect the likelihood of perpetrating a new sexual offense (Babchishin & Hanson, 2020; Bonta & Andrews, 2017; Mann et al., 2010; Olver & Stockdale, 2020). Based on meta-analyses and two large-scale studies on the association of dynamic risk factors with sexual reoffending in adult men convicted for sexual offenses (Hanson & Bussière, 1998; Hanson et al., 2007; Hanson & MortonBourgon, 2005; Helmus et al., 2013; Knight & Thornton, 2007; Thornton, 2002, 2013) developed a framework for dynamic risk assessment, the structured risk assessment (SRA) need framework (see Table 1.1 in Chapter 1). This framework includes four domains - sexual interests, distorted attitudes, relational style, and self-management, which are further divided into subdomains of dynamic risk factors. For example, the domain of sexual interests consists of sexual preoccupation and offense-related sexual interests. The most widely used dynamic risk assessment instruments for adult males with a history of sexual offenses all contain factors from at least three of the four domains of the SRA model (McGrath et al., 2010; van den Berg et al., 2018, 2020). Empirical support for the predictive properties of dynamic risk assessment instruments developed for use in adult men adjudicated for sexual offenses is growing (e.g., van den Berg et al., 2018). However, the interrelationships among dynamic risk factors are still not well understood and not addressed by the SRA model (Heffernan& Ward, 2017, 2020; Ward & Fortune, 2016b). In a previous study, we used network analysis to study the interrelationships among dynamic risk factors in adult men charged with or convicted of sexual offenses (van den Berg et al., 2020). We applied network analysis based on the assumption that risk of sexual reoffending can be understood as involving a network of causally connected dynamic risk factors which arises when causal connections between such dynamic risk factors are sufficiently strong (van den Berg et al., 2023). External factors (e.g., life events such as losing a partner or a friend) can influence dynamic risk factors and their causal connections, resulting in changes in the risk of sexual reoffending (van den Berg et al., 2020, 2022). Although the interrelationships among dynamic risk factors could also be examined using other analytical approaches (e.g., structural equation modeling [SEM], confirmatory factor analysis, or multidimensional scaling), network analysis was selected because of its ability to produce a model in a purely exploratory fashion (i.e., in a bottom-up empirical approach) without prior assumptions regarding the interrelations of the included factors (van den Berg et al., 2020). In addition, network
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