3 66 CHAPTER 3 often applied in this way. Most commonly, SEM is considered, used, and interpreted as a confirmatory statistical technique. The advantages of using network analyses over alternative approaches such as SEM include 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. In addition, in contrast to SEM and latent variable models, networks that represent conditional dependencies (i.e., pairwise Markov random fields; Epskamp & Fried, 2017) are statistically speaking determinate; that is, no statistically equivalent network models exist that have a different structure. In contrast, a given SEM model typically has many equivalent or nearly equivalent alternative models (Epskamp et al., 2017). These are important benefits of network analyses because (a) researchers’ current insight into the interplay among dynamic risk factors may be too limited to theoretically single out a particular model from a large class of nearly equivalent models and (b) the occurrence of feedback loops is highly likely in the case of dynamic risk factors. For example, sex can be used to cope with feelings of loneliness, which may be effective in the short term but may further increase loneliness, which results in more sexual activity in an attempt to cope with this negative emotional state in the long term. Finally, network analyses allow for generating hypotheses regarding causal relationships between dynamic risk factors and the pathways to recidivism. Networks based on repeated measurements may also provide information on the direction of observed relationships and give more insight in nonstationary processes that result from changing dynamics (Bringmann et al., 2017). 3.4 CURRENT STUDY The goal of the current study was to assess 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 males with a history of sexual offenses. For this purpose, we conducted three regularized network analyses (Borsboom & Cramer, 2013; Epskamp, van Borkulo et al., 2018; van Borkulo et al., 2014). In regularized networks, edges represent the strength of the connection between two nodes that remains after controlling for all other nodes in the network (Epskamp & Fried, 2017). Our analyses focused on network construction, node centrality, and the shortest paths of dynamic risk factors to recidivism. First, we investigated the relationship among dynamic risk factors as measured by the STABLE-2007 (Fernandez et al., 2012). Second, we determined which dynamic risk factors play a central role in networks of dynamic risk factors. Third, we examined the pathways between individual risk factors and sexual and violent (including sexual contact) recidivism.
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