3 79 NETWORK ANALYSIS DYNAMIC RISK FACTORS more insight in processes that result from changes in dynamic risk factors. A fifth limitation concerns the fact that the measurement of each dynamic risk factor was limited to a single item. Although all individual items were related to recidivism, it would be valuable to examine the possible added value of measuring dynamic risk factors using multiple items or scale scores. Finally, although stability coefficients for strength centrality were all above the lower limit, none of the strength centrality coefficients exceeded the cutoff of 0.5 that is required for the metric to be considered stable (Epskamp, Borsboom et al., 2018). As a result, the order of node strength within all three networks should be interpreted with caution. The stability coefficients for strength centrality suggest that it is unfeasible to analyze network structure, centrality, and shortest paths to recidivism separately for men who committed sexual offenses towards adults and children. We recommend further studies, preferably in larger samples, attempt to replicate our findings and differentiate between those two offender groups in their analyses. 3.7.2 IMPLICATIONS AND STRENGTHS Despite the above limitations, we believe our findings - to the extent that the estimated network structure indeed describes a pattern of mutualistic causal interactions - have several theoretical, clinical, and social implications. Because network analyses provide insight in the interrelations among risk factors and possible shortest pathways to (sexual) offending behavior, without the need for a priori assumptions, they can be used to contribute to the further development of theories on sexual offending (e.g., Malamuth & Hald, 2016; Malamuth et al., 1995; Seto, 2019; Smid & Wever, 2019; Toates et al., 2017; Ward & Beech, 2016; Ward et al., 2006). For example, our network analyses provide support for the idea that different clusters of dynamic risk factors and pathways to recidivism exist, as was found in earlier research in adult men with a history of sexual offenses (Malamuth, 1986, 2003; Malamuth & Hald, 2016; Malamuth et al., 1995). Future longitudinal research may be able to produce “directed networks” that assess not only the strength of interrelations but also their direction. Another important move forward would involve the identification of psychobiological and social mechanisms that underlie the connections among risk factors. Our findings also have clinical implications for treatment. Accepting the possibility that conditional dependencies in our network structures indeed reflect mutualistic causal interactions between risk factors, treatment should be focused on highly central dynamic risk factors and dynamic risk factors directly related to recidivism after statistically controlling for the other dynamic risk factors. 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. However, again, we acknowledge that the network estimation techniques we used are primarily hypothesis generating, so these ideas should be tested and validated more systematically in future studies.
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