4 86 CHAPTER 4 analysis as a statistical method fits with the assumption that risk of sexual reoffending results from a network of interacting dynamic risk factors. In contrast, classic SEM would consider this risk a latent variable to be measured by manifest variables in a reflective manner. In network analyses, interrelationships among risk factors are graphically represented using nodes and edges, where dynamic risk factors are represented as nodes and connections among risk factors are visualized by edges. Dynamic risk factors with a relatively large number of (strong) connections can be assumed to have more influence on other dynamic risk factors and, thus, on (future) offending behavior (McNally, 2016; Opsahl et al., 2010). This relative importance is expressed in centrality metrics. Within the graphical representation of networks, based on the strength and number of relationships, dynamic risk factors can cluster together in meaningful related (sub) communities. However, centrality and communities are not the only way to think of the significance of a dynamic risk factor within a network. A dynamic risk factor can also be influential when it forms a connection between two or more communities of dynamic risk factors, also called a bridge. Such analyses have important implications on the theory of sexual reoffending. 4.1.1 PREVIOUS NETWORK ANALYSIS IN ADULT MEN WITH A HISTORY OF SEXUAL OFFENSES In an earlier study, we explored how and to what degree dynamic risk factors are interrelated, and which dynamic risk factors play a central role within dynamic risk factor networks in adult men with a history of sexual offenses (van den Berg et al., 2020). For this purpose, we estimated a series of three network analyses. The most salient findings were the high-strength centrality for the dynamic risk factors general rejection/loneliness (in all networks), poor cognitive problem-solving (in networks containing sexual or violent [including sexual contact] recidivism), and impulsive acts (in the network including sexual recidivism). In other words, these three risk factors might provide relatively large influence on the risk of sexual recidivism. These variables also represented links between communities of dynamic risk factors referred to as sexual self-regulation, emotionally intimate relationships, antisocial traits, and self-management (van den Berg et al., 2020). Assuming the estimated network structure indeed describes a pattern of mutual causal interactions, these findings have theoretical, clinical, and social implications. Insights in the interrelations among dynamic risk factors can be used to contribute to the development of theories on sexual reoffending. Further, our findings suggest that treatment efforts may benefit from a focus on inhibition of impulsive acts, improving cognitive problem-solving, reducing feelings of loneliness, and stimulating reintegration in society. Based on the central role of social rejection/loneliness in all networks, this dynamic risk factor is believed to be particularly important to address and monitor not only during treatment but also during reintegration efforts.
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