3 78 CHAPTER 3 Alarie et al., 2016). Such analyses have revealed factors referred to as a sexual factor, an impulsive/antisocial factor, and sometimes a third factor, described as “detachment” or “immaturity.” Our findings are also compatible with theoretical models distinguishing between different pathways to sexual offending. For instance, Malamuth (1986, 2003; see also Malamuth & Hald, 2016; Malamuth et al., 1995) described two relatively independent pathways to sexual coercion against women, one relevant to sexual processes (impersonal sexual promiscuity) and one relevant to callousness and hostility toward women (hostile masculinity). 3.7.1 LIMITATIONS Several limitations of our study should be acknowledged. First, although the STABLE-2007 is empirically and theoretically driven and its predictive validity has been supported in studies in North America and several European countries (Brankley et al., 2021; Eher et al., 2012; Eher et al., 2013; Smeth, 2013; Sowden, 2013), it captures a limited number of relevant dynamic risk factors. Network analysis can calculate only the interconnections of the factors measured, and adding an additional dynamic risk factor to a network analysis could affect both the networks and centrality measures. Second, our analyses are based on data obtained from a (North American) group of mostly untreated men with a history of sexual offenses who differ with respect to their offenses. Risk factors that are predictive for one group of men who offended (e.g., emotional identification for man with sexual offenses towards children) may be less important in another groups (e.g., men who raped). Because network structures and metrics tend to differ across different samples (Borsboom et al., 2017), replications with different risk assessment instruments and in separate samples of, for example, men who offended towards children and men who raped or in treated as opposed to untreated men with a history of sexual offenses, are indicated. Replication attempts should preferably also include the collection of additional demographic information given that the study from which the current data were derived lacked information on ethnicity, education, socioeconomic status, and income. A third limitation concerns the fact that because of variations in prison sentences, the time between assessments and offenses differed among individuals. Although we were not able to control for this variable, other studies have found that STABLE-2007 scores were highly consistent for the first and 6-month follow-up during community supervision (e.g., Hanson et al., 2007). Thus, we have little reason to expect meaningful change on stable dynamic risk factors during sentencing periods. Nevertheless, future research could take sentence duration into account and describe the percentage of subjects who received sentences of long enough duration to affect the levels and number of stable dynamic risk factors. A fourth limitation of this study is that it is based on cross-sectional data. This type of data allows for the generation of undirected networks regarding relationships between dynamic risk factors and the pathways to recidivism. Using a longitudinal design, future studies could provide information on the direction of observed relationships and offer
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