7 154 CHAPTER 7 to affect dynamic risk factors and thus sexual reoffending. Fifth, it remains somewhat unclear how sexual reoffending is best understood from the NBM-RSR. Although the NBM-RSR and other theories of sexual offending have much in common, the NBM-RSR provides a rationale not on the occurrence of but on the likelihood of sexual offending. In the future, a network-based model of sexual offending could be developed that approaches (the persistence of) sexual offenses from a critical transition of a network of dynamic risk factors to a network that includes the actual sexual offending behavior. Finally, although not a limitation of the model itself, this dissertation did not examine the stipulated propositions and hypotheses derived from the NBM-RSR concerning the development and nature of dynamic risk factors and how they impact the risk of sexual reoffending (described in Chapter 6). 7.5 RECOMMENDATIONS FOR FUTURE RESEARCH Guided by the findings, and limitations, of the research presented in this dissertation, it can be recommended that additional research is conducted on the predictive propensities of (changes scores of) dynamic risk assessment instruments developed for adult men with a history of sexual offenses. This will help expand the currently limited literature and allow future meta-analyses to examine whether the results described in Chapter 2 can be replicated. Second, although it is assumed that the core principles of our NBM-RSR apply to humans in general, network structures and metrics might differ for samples with different demographics or different offense types. Future research will have to determine to what extent network construction varies depending on variables relevant to culture, gender (e.g., being a transgender or gender diverse individual), sexual orientation, and the offense type the individual is convicted for. Third, replication of network analyses on the interrelationships of dynamic risk factors including recidivism of adult man with a history of sexual offenses convicted under the inquisitorial system of law is needed as differences in legal systems might impact criminal law and procedures (e.g., probation). Fourth, future network analysis on dynamic risk factors might estimate and investigate directed networks of dynamic risk factors. These networks provide information on the direction of observed relationships and offer more insight in causal processes that result from changes in dynamic risk factors. Directed networks can be estimated using a longitudinal research design. Recently Briganti and colleagues (2022), however, developed an alternative method to identify admissible causal relationships in cross-sectional data. They introduced Bayesian networks to overcome the limited causal interpretation of networks estimated through a PMRF. Bayesians networks, which are derived from principles of causal reasoning (Pearl & Mackenzie, 2018), ascertain both the direction and the magnitude of causal effects (Maathuis et al., 2018). Fifth, the NBM-RSR provides the ability to formulate hypotheses on the causal
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