Replication of a Three-Factor Solution:
Exploring the Underlying Constructs of the Static-99R and Static-2002R
Master’s Thesis Presented to
The Faculty of the Graduate School of Arts and Sciences Brandeis University
Department of Psychology Dr. Raymond A. Knight, Advisor
In Partial Fulfillment
of the Requirements for the Degree Master of Arts
in Psychology
by
Diane M. Rohrer
Copyright by Diane Rohrer
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ACKNOWLEDGEMENTS
I would like to thank Ray Knight, my advisor, for always giving me enough rope to either climb to the top or hang myself; Xiaodong Liu for helping me fall in love with a subject that I have always feared and reminding me to eat the apple; the Knight Lab for their constant support and
uplifting text messages when things are going “fine”; my husband, Brandon Rohrer, for being my better half, my light in the darkness; and Reign of Terror for always offering unconditional
iv ABSTRACT
Replication of a Three-Factor Solution:
Exploring the Underlying Constructs of the Static-99R and Static-2002R
A thesis presented to the Department of Psychology
Graduate School of Arts and Sciences Brandeis University
Waltham, Massachusetts By Diane M. Rohrer
Traditionally actuarials that have been used to predict recidivism have almost exclusively focused on historical or static factors. This limits their utility for measuring change on critical traits and identifying dynamic treatment targets. In 2016 Brouillette-Alarie, Babchishin, Hanson, and Helmus sought to address this limitation by factor analyzing combined items from the Static 99R/2002R. The goal was to identify the underlying factors that could then be looked at via the lens of latent psychological constructs. Three factors emerged: Persistence/Paraphilia, related to pedophilia; Youthful Stranger Aggression, related to youthfulness and serious offenses; and General Criminality. The present study endeavored to replicate the Brouillette-Alarie et al.’s (2016) factor analysis with a sub-sample of 533 sexual offenders (M age = 35, SD = 11) from the Massachusetts Treatment Center (MTC) who had been assessed for civil commitment between 1959 and 1984 and either been committed or released to finish their prison sentences.
Brouillette-Alarie et al.’s (2016) three-factor solution was partially replicated, but the present studies’ results also identified a fourth factor, the Agonistic Continuum Factor, defined by non-contact sexual convictions and stranger victims.
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In order to further understand what each of the latent psychological constructs were measuring, external scales were created by factor analyzing items from diagnostic and
typological measures, mechanical actuarials, dynamic risk assessment tools, structured clinical guidelines, and multiple scales developed as part of the MTC research program. The aim with each scale was to capture a particular psychological risk factor that was then correlated with the Static latent factors. The results of these correlations found that the Persistence/Paraphilia Factor was significantly correlated with the Pedophilia Scale, the Density of Sexual Offenses Scale, and negatively correlated with both the Offense Violence Scale and the Non-Sexual Violence Scale. The Youthful Antisociality Factor was found to significantly correlate with both impulsivity scales (Impulsivity in Offense and Lifestyle), as well as the Antisociality Scale and the Offense Violence Scale. The General Criminality Factor was found to have significant correlations with the Antisociality Scale, the Density of Sexual Offense Scale, and the Lifestyle Impulsivity Scale. Finally, the Agonistic Continuum Factor significantly correlated with the Offense Violence Scale, both impulsivity scales (Impulsivity in Offense and Lifestyle), the Antisociality Scale, and the Stranger Offenses Scale, while negatively correlating with the Pedophilia Scale. These
findings suggest that the latent factor structure found within the Static99R/2002R not only can be used to predict recidivism, but have further credence for assessing not only treatment targets but change within those treatment targets as well.
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Table of Contents
Abstract ... iv
List of Tables ... vii
Introduction ...1
Advantages of Using Latent Construct Models Purpose of the Study Methods ...8
Participants Procedure General Item Preparation Measures Analyses Results ...16
Static Factor Analysis Young Antisociality Factor General Criminality Factor Pedophilia Factor Agonistic Continuum Factor Discussion ...21
Conclusion ...28
Tables ...29
Appendices ...34
Appendix 1 Coding rules for combining the Static-99 and Static 2002 Appendix 2 Items Selected to Measure Each External Validity Domain References ...47
vii List of Tables
Table 1. Brouillette-Alarie et al. (2016) Rotated Factor Loadings with
GEOMIN Rotation for 13 Items from the Static 99R and Static 2002R ...29 Table 2. Rotated Factor Loadings with GEOMIN Rotation for 13 Items
from the Static 99R and Static 2002R. Heywood Case in Factor 4 ...30 Table 3. Rotated Factor Loadings with GEOMIN Rotation for 12 Items
from the Static 99R and Static 2002R. Item 4 Removed ...31 Table 4. Factor Loadings on Principal Axis Factoring with OBLIMIN
Rotation on External Validity Scales Child Preference ...32 Table 5. Correlations of Static Factors and External Validity Scales ...33
1
Replication of a Three-Factor Solution:
Exploring the Underlying Constructs of the Static-99R and Static-2002R
Sexual violence has become an everyday news topic, not only in this country but around the world. In 2017 the Centers for Disease Control and Prevention, published the results from the 2015 National Intimate Partner and Sexual Violence Survey, reporting that one in five women had experienced completed or attempted rape in her lifetime, and that one in fourteen men had been made to penetrate someone (either completed or attempted intercourse) in their lifetime (Smith et al., 2018). Smith et al. (2018) also found that 81.3% of the female victims in the survey reported that the experienced sexual violence took place prior to their twenty-fifth birthdays (Smith et al., 2018). With such statistics it is not surprising that managing the treatment and release of those convicted of sexual coercion is one of the key issues that brings together
societal, clinical, political, and judiciary fields. Because both risk and resilience factors play key roles in determining whether and when sex offenders are released, both clinicians and society at large have focused on these factors. Risk assessment addresses the issue of whether an
individual, who has served time for sex offending, will recidivate upon release. Three primary historical movements in the area of risk assessment have led to the measures that we use today to predict recidivism.
In 1996 Bonta identified the use of unstructured professional opinion as the first generation of risk assessment procedures to address the demands that sexual offender cases posed for court systems (Prentky, Barbaree, & Janus, 2015). This strategy involved assessments that neither specified relevant items nor prescribed a method for combining items to determine
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risk level. A potential problem with this method is the introduction of biases and errors in human judgment into decisions that have the potential to affect not only society at large but also the sex offenders themselves. Meehl (1954) categorized risk assessment as either clinical or actuarial (i.e., statistically derived, mechanical) prediction, which constitute pervasive approaches of predicting human behavior (Dawes, Faust, Meehl, 1989; Grove & Meehl,1996; Prentky et al., 2015). Predictions or distinctions made about an offender’s behavior in the category of clinically based assessment involve an evaluator’s judgment, which has been described as informal,
subjective, and impressionistic (Grove & Meehl, 1996; Prentky et al., 2015). Such unrestricted, unguided clinical assessment has long been recognized as an unreliable, undependable metric for predicting future violence (Hanson & Morton-Bourgon, 2009; Monahan, 2007).
The introduction of empirical evidence to guide assessment demarcates Bonta’s (1996) second generation of risk assessment, ushering the field into mechanical prediction (Meehl, 1954). Empirical actuarials are risk assessment tools that comprise risk items that have been empirically validated. An evaluator follows a set of rules pertaining to each item, assigning a score to the offender for that item (Groove & Meehl, 1996). Items are questions or statements that are answered, usually by a clinician or evaluator working with an offender, that indicate whether or not that offender exhibits a particular behavior or has a particular offense pattern. An example of an item is “Stranger victim offense” (Thornton, 2002). After the scoring of all items the evaluator follows rules generated by the actuarial developer that allow the item scores to be combined into an overall summary score. Optimized statistical formulas, which have been empirically derived, are then applied to the summary score to predict recidivism (Groove & Meehl, 1996; Prentky et al., 2015). Empirical actuarials include tables linking the summary scores to recidivism rates.
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Research comparing the predictability between the first two generations of risk assessment methods in forensic settings have found consistently that actuarial predictions generally demonstrate superior accuracy over clinical predictions (Mossman, 1994; Gardner, Lidz, Mulvey, & Shaw, 1996; Menzies, Webster, McMain, Staley, & Scaglione, 1994). Grove, Zald, Lebow, Snitz, and Nelson (2000) did a meta-analysis of 136 psychological and medical studies and found that mechanical prediction outperformed clinical prediction by 33-47%. Empirically derived actuarials are preferred over the first generation of unstructured professional opinion because of their greater level of objectivity, reliability, and predictive validity.
This second generation primarily focused on static risk items, which are fixed, historical factors that cannot be changed. This focus constituted the major limitation of their utility for identifying dynamic treatment targets and for measuring changes in these critical traits. Dynamic traits are characteristics that are capable of change, and their change is associated with
modifications (up or down) in recidivism risk (Andrews & Bonta, 2014; Mann, Hanson, & Thornton, 2010). This limitation of the second generation of actuarials led to the inception of the less well researched third generation, which focuses with dynamic risk factors. As a group these dynamic actuarials assess changeable factors that can then be used as targets for both treatment and risk assessment. Third generation actuarials have yielded comparable, but not superior, levels of predictive accuracy. Nonetheless, such dynamic risk instruments have been found to add incremental predictive validity to static actuarials (Knight & Thornton, 2007; Beech,
Friendship, Erikson, & Hanson;, 2002; Thornton, 2002, Olver, Wong, Nicholaichuk, & Gordon, 2007; Mann et al., 2010), suggesting that these dynamic instruments capture variance not tapped by the static measures.
4 Advantages of Using Latent Construct Models
The line dividing static and dynamic items in actuarials is at best fuzzy. It can be argued that static items have a dynamic component and can be viewed as historical markers of dynamic traits. Hanson, Harris, Helmus, and Thornton (2014) have argued that as an offender ages earlier historical markers should become less relevant in determining current recidivism risk, and concurrent assessments of the trait should be employed to modify earlier historical indicators.
In 2016 Brouillette-Alarie et al. investigated this perception of static actuarials by using exploratory factor analysis (EFA) to identify the latent factors underlying two of the most commonly used static actuarials, the Static-99R and the Static-2002R. Such latent factors can then be examined as hypothetical psychological constructs that can serve as potential treatment targets and as theoretical guides for the assessment of recidivism.
Although some might question the usefulness of investigating the latent factors that static actuarials tap rather than moving directly to studying dynamic actuarials, this constitutes a research strategy question that is beyond the focus of the current study. Moreover, it is undeniable that demonstrating the core dynamic factors that actuarials assess has the
consequence of expanding the practical utility of a whole generation of actuarials that are widely used today.
The term “latent” means that the construct being assessed is not measured directly (Kline, 2015). For instance, the items of the Static-99R or Static-2002R are not conceived as direct measures of a particular trait, but composites of correlated items in these actuarials might serve as indirect, covarying indicators of a latent dynamic trait. In personality psychology, composites of observed attitudes and behaviors are used to identify such latent constructs as impulsivity, antisocial personality disorder, or narcissistic personality disorder. Analogously, the items in the
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Static-99R and Static-2002R can be viewed as observable, historical behaviors that can be clustered to assess specific latent constructs (Brouillette-Alarie et al. (2016).
As Brouillette-Alarie et al. (2016) pointed out, there are distinct advantages to using latent factor models to inform assessment. As mentioned previously, a latent factor model increases the utility of the Static-99R and Static-2002R, two of the most popular actuarials for predicting recidivism. Factor models can also allow us to identify the underlying constructs that serve as guides for developing changeable measures (Brouillette-Alarie, Proulx, & Hanson, 2018).
This notion, that non-overlapping Static items could be used to identify latent factors that are psychological constructs, was what Brouillette-Alarie et al. (2016) set out to test when they applied exploratory factor analysis to 13 non-overlapping items of the 99R and Static-2002R. The knowledge gained from this would further drive home the idea that both static and dynamic factors not only predict recidivism but, owing to the fact that they are behavioral
markers of latent enduring risk, are also relevant psychological constructs (Beech & Ward, 2004; Mann et al., 2010).
It is important to acknowledge that the idea of utilizing a latent trait model to assess underlying psychological behaviors of static actuarials is by no means new. Over the past
decade, deciphering the underlying factor constructs of sex offender static actuarials has been the focus of many a research endeavor (Allen & Pfugradt, 2014; Barbaree, Langton, Blanchard, & Cantor, 2009; Barbaree, Langton, & Peacock, 2006; Boughner, 2010; Brouillette-Alarie & Proulx, 2013; Knight & Thornton, 2007; Langton, Barbaree, Hansen, Harkins, & Peacock, 2007; Roberts, Boren, & Thornton, 2002; Seto, 2005; Walters, Deming, & Elliott, 2009). What was innovative about Brouillette-Alarie et al.’s (2016) study was that they addressed some of the
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limitations of the earlier studies. The first thing that they addressed was the use of Pearson product-moment correlations, which are suboptimal for handling the mixture of dichotomous and ordinal items that are often found in actuarials (Brown & Beneditti, 1977; Flora & Curran, 2004: Hologado-Tello, Chacón-Moscoso, Barbero-García, & Vila-Abad, 2010; Kubinger, 2003). They next experimented with extraction and rotation techniques, instead of using the SPSS defaults of principal components extraction and orthogonal rotation.
Brouillette-Alarie et al.’s (2016) exploratory factor analysis yielded a 3 factor solution. The first factor consisted of five items: two or more young victims, one unrelated; high rate of sexual offending/prior sex offences; male victim; and noncontact sex conviction. Brouillette-Alarie et al. (2016) identified this factor as a Persistence/Paraphilia, primarily centering around pedophilia. The second factor was made up of five items: never lived with a lover for at least 2 years; younger age at release; unrelated/stranger victim; index nonsexual violence; and juvenile sex arrest. This factor was identified by Brouillette-Alarie et al. (2016) as Youthful Stranger Aggression owing to the youngness of offenders and the severity of their offenses. The final factor that was identified consisted of four items; prior sentencing; prior nonsexual violence; breach of conditional release; and few years free prior to index. This final factor was labeled as General Criminality showing both a magnitude and diversity of criminal careers (Brouillette-Alarie et al, 2016).
Purpose of the Study
The current study attempted to replicate Brouillette-Alarie et al.’s (2016) factor structure for the Static-99R and Static-2002R, using their methods in Mplus, but with a smaller sub-sample of their meta-analysis. This sub-sample comprising of 533 sexual offenders also had substantial additional measures available for validation purposes. Although exploratory factor
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analysis conventionally requires no a priori hypotheses about factor structure, due to the fact that this is a replication study, we hypothesized that we would replicate the three-factor solution that Brouillette-Alarie et al. (2016) found in their multi-sample analysis, (i.e., Persistence/Paraphilia, Youthful Stranger Aggression, and General Criminality).
The second aim of this study involved a validation of the resultant latent psychological factors, using selected psychological constructs derived from several dynamic risk assessment instruments. We hypothesized that (a) the Persistence/Paraphilia Factor would correlate with our Pedophilia and Density of Sexual Offenses Scales and will covary negatively with our Offense Violence and Hebephilia Scales; (b) the General Criminality Factor would correlate with our Antisociality, Non-sexual Violence, and both Impulsivity scales; and (c) the Youthful Stranger Aggression Factor, which we predict is an analogue of the Agonistic Continuum (Knight, Sims-Knight, & Guay, 2013), would correlate positively with our Offense Violence and Impulsivity Scales and negatively with our Pedophilia Scale. The overarching goal of this study was to generate information about each actuarial factor that would deepen and extend our understanding of the latent constructs being measured, thereby furthering their utility in identifying treatment options and enhancing their utility for improving risk assessment.
8 Methods Participants
This study endeavored to replicate the Brouillette-Alarie et al. (2016) factor analysis with a sample of 533 sexual offenders (M age = 35, SD = 11) from the Massachusetts Treatment Center (MTC) who had been assessed for civil commitment between 1959 and 1984 and either been committed or released to finish their prison sentences. The sample was predominantly Caucasian (89.7%). Non-white participants included African American, Native Americans, and Asian Americans. The educational make-up of the sample measure by their last grade completed was 5.4% completed some or all of primary school (grades Kindergarten through 5th), 41.3 % completed some or all of junior high (grades 6th through 8th), 48.5% completed some or all of high school (grades 9 through 12), and 5% completed some college or 4 years of college. Procedures
Institutional review boards (IRBs) at Brandeis University and at MTC, where participants were tested, approved both the participant selection and administration protocols.
Selection and administration procedures. Of the offenders in this study 55.6% of them were declared not sexually dangerous and rather than being committed to the Massachusetts Treatment Center they were simply evaluated and return to prison to serve out their sentences. However, 44.4% of the offenders were declared sexually dangerous and thus committed for treatment. In 2007 Knight and Thornton accessed the archival clinical files for both subsets of offenders and coded these records on a battery of modern empirically-derived, mechanical actuarials for predicting sexual recidivism, two of which were the Static 99R and Static 2002R.
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Static 99R/2002R. These two risk assessment tools are the focus of the current factor analysis. They use only static (unchangeable or historic) items that have been found to correlate with recidivism in adult male offenders (Hanson & Thornton, 2003). They consist of 14 items in 5 categories: age at release, persistence of sex offending, sexual deviance, relationship to
victims, and general criminality with total scores ranging from -2 to 13 (Hanson & Thornton, 2003). The current study used ratings from the 2007 database collected by Knight and Thornton. Each offender was coded on each item of both actuarials by two raters. In order to evaluate interrater reliability, prior to taking the mean of the two raters on each item a Cronbach’s Kappa was ran on all items that would be needed to complete the item preparation according to
Brouillette-Alarie et al. (2016). Of the twenty-three items used from both the Static 99R and Static 2002R (see Appendix 1 for full item preparation) the average Cronbach’s Kappa was 0.737. Two items had Kappas below the .600 threshold: Static 2002R item 11 “Arrest/Sentencing occasions” ( = 0.59) and item 12 “Years free prior to index offense” ( = 0.57). The highest Kappa score was item 2 on the Static 99R, “Ever lived with a lover for at least 2 years” ( = 0.91).
General Item Preparation
Item preparation of the Static-99R and Static-2002R, consistent with Brouillette-Alarie et al.’s (2016) item preparation, ensured item non-redundancy. First, “age at release” from both actuarials was not used. Instead age at release in years, a continuous variable was calculated and used. Second, two items, both measuring stranger victims, “any unrelated victims” and “any stranger victims” were merged into one item with a 3 point scale, where 0 = no unrelated or stranger victims, 1 = at least one unrelated victim and no strangers, and 3 = at least one stranger victim. Third, when items appeared on both static actuarials, they were only counted once (e.g.,
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“any convictions for noncontact sex offenses” [Static-99R] and “any sentencing occasion for noncontact sex offences” [Static- 2002R]. Fourth, when items were extremely similar, but had different coding rules, these items were summed (e.g., “prior sex offenses” [Static-99R], “prior sentencing occasions for sexual offenses” 2002R], and “rate of sexual offending” [Static-2002R]. A summary of the coding rules for each final item is presented in Appendix 1.
Measures
Static 99R/2002R. These are risk assessment tools, which are the focus of the current factor analysis. These two widely used actuarials use only static (unchangeable or historic) items that have been found to correlate with recidivism in adult male offenders (Hanson & Thornton, 2003). They consist of 14 items in 5 categories: age at release, persistence of sex offending, sexual deviance, relationship to victims, and general criminality with total scores ranging from -2 to 13 (Hanson & Thornton, 2003).
External Validation Scales. An extensive database is available for selecting scales and items that can serve to test a priori hypotheses about the core constructs that each Static factor measures. Items were selected from this database for their theoretical relevance to specific domains: pedophilia, hebephilia, antisociality, impulsivity in the offense, impulsivity in lifestyle, stranger offenses, density of sexual offenses, sexual violence, and non-sexual violence. The specific sources from which the items/scales were selected included a number of mechanical actuarials, two structured clinical guidelines (SCG), diagnostic and typological instruments, one dynamic risk tool, and multiple scales developed as part of the MTC research program. Each scale/item was aimed at capturing a particular psychological risk factor that was then correlated with the Static factors. The selected items for each domain are presented in Appendix 2. We identify next each source below from which items/scales were selected.
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Mechanical Actuarials. Items were selected for external validation from the following four mechanical risk instruments: the Minnesota Sex Offender Screening Tool-Revised
(MnSOST-R; Epperson, Kaul, Huot, Hesselton, Alexander, & Goldman, 1998), the Risk Matrix (RM 2000) (Thornton, 2002), the Violent Risk Appraisal Guide (VRAG; Quinsey, Harris, Rice, & Cormier, 1998), and the Sex Offender Appraisal Guide (SORAG; Quinsey et al., 1998).
SCGs. Two SCGs were tapped for validation items. These were: the Sexual Violence Risk-20 (SVR-20; Boer, Hart, Kropp, & Webster, 1997) and the Adult Sex Offender Assessment Protocol (A-SOAP-II; Prentky & Righthand, 2003).
Dynamic Risk Tool. One dynamic risk assessment tool provided validation items: the Structured Risk Assessment (SRA; Thornton & Knight, 2015).
Diagnostic and Typological Sources. Four diagnostic and typological measures provided validation items. These included: Psychopathy Checklist-Revised (PCL-R; Hare, 1991, 2003), the Screening Scale for Pedophilic Interests (SSPI; Seto & Lalumière, 2001), and the two typological systems developed for rapists and child molesters (Knight, 2010, 2012; Knight & Prentky, 1990).
Other Sources of External Validation Items. Additional scales/items were selected from the extensive MTC Coding Questionnaire (Knight, Cerce, Carter, & Martino, 1986).
Analyses
To replicate Brouillette-Alarie et al.’s (2016) findings the software program Mplus was used for the exploratory factor analysis (EFA), as were the exact correlation methods, factor extraction method, and rotational method. The items from the Static 99R and Static 2002R are a combination of dichotomous variables, ordinal variables with more than two levels, and
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polychoric correlations are less sensitive to the restriction of range artifacts commonly observed when Pearson product-moment correlations are used with dichotomous or ordinal data. Because all three types of variables were used as they were in Brouillette-Alarie et al.’s (2016) analysis, tetrachoric and polychoric correlations were used in the factor analysis. Weighted least square means- and variance-adjusted factor extraction method was also employed because it is
recommended for EFA with dichotomous and ordinal data as it was in Brouillette-Alarie et al.’s analysis (2016) (Muthen &Muthen, 2017). Finally, goemin rotation was used due to the
prediction in Brouillette-Alarie et al. (2016) study that the resultant factors would be correlated. Five fit indices were examined to evaluate model fit—the root mean square error of approximation (RMSEA), comparative fit index (CFI), and the Tucker-Lewis index (TLI), chi square (2) and p-value. For the purposes of this analysis, RMSEA should not exceed .07 (Kline, 2016). A CFI greater or equal to .95 indicates a good fit, and a TLI greater or equal to .95 also indicates a good model fit (Kline, 2016). Normed chi square 𝑑𝑓2 in which a value of <3.0
indicates an acceptable model fit and p-value. The criteria for factor inclusion for an item was a factor loading of 0.4 or higher. The reason for this cutoff is twofold. First, it was the same criterion used in the Brouillette-Alarie et al. (2016) study, and second it helped reduce the amount of cross-loading of items on more than two factors.
The first EFA can be viewed in Table 2, yielded confounding results with eigenvalues suggesting a 4 factor solution, but subpar TLI and RMSEA values. This solution also produced a Heywood case with item 11 “unrelated/stranger victim” loading on the fourth factor at 1.09. A Heywood case occurs when a solution includes a negative residual variance. Muthen and Muthen (2017) address such a case as theoretically inadmissible and argue that such a solution is not a good model fit. As such they suggest reconfiguring one’s model. After a close inspection of item
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11’s, “unrelated/stranger victim,” correlation it was hypothesized that the Heywood case was the result of a suppression effect between the item 11 and item 4 “noncontact sex conviction.” As such, the decision was made to remove item 4 from the analysis and re-run the factor analysis, as neither the Heywood case nor the fit indices suggested that this solution fit the data properly.
The reconfiguration and rerunning of a second EFA resulted in mixed results. The eigenvalues suggested a three factor solution, with a fourth factor yielding an eigenvalue of 0.963. Given the subpar fit indices for the three factor solution and accounting for the fourth eigenvalue being right on the cusp of the standard 1.0 threshold, a four factor solution was explored.
The factors were named by means of the Static items significantly contributed the most to each: Factor 1 is Young Antisociality, Factor 2 is General Criminality, Factor 3 is Pedophilia, and Factor 4 is Agonistic Continuum. Within the model Young Antisociality accounted for 31.93% of the variance, while General Criminality accounted for 19.4% of the variance,
Pedophilia accounted for 15.28% of the variance, and finally Agonistic Continuum accounted for 8.02% of the variance.
The final four static factors were correlated with the external validity measures. Static factor scores were created by standardizing the score of each item and multiplying each standardized item by the resultant factor weight from the EFA and then dividing by the total number of items. Standardizing each item allowed us to equalize the contribution of each item, and avoided the problem of items with a large range (e.g., age) overwhelming the contribution of dichotomous variables. Multiplying the standardized score of each item by the factor weight and then dividing by the number of items let each item exert the appropriate influence on the factor. The alternative was to force an arbitrary cutoff which would result in factor loadings being one
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or zero. Our method takes advantage of the subtle nuances in the factor loadings that are a result of the weighted least square means- and variance-adjusted factor extraction method extraction and the goemin rotation methods used in the EFA.
The next step involved generating and combining the external validation measures. As indicated above, to validate each factor we selected theoretically relevant items/scales from the variety of measures that Knight and Thornton (2007) collected. Items/scales (variables) chosen within each domain were factor analyzed using Principle Axis Factoring and Oblimin rotation in SPSS. Factor scores were generated as follows: Variables loading >.4 on each resultant factor were standardized, summed and divided by the number of variables loading >.4.
The first factor analysis focused on victim age-preference scales. Two factors emerged with eigenvalues over one. The first factor accounted for 47.46% of the variance in the model. Given the items that made up Scale 1, the name Pedophilia Scale was given to it. The second scale accounted for 19.34% of the variance and was named Hebephilia. Table 4 shows the results of the factor analysis with the two factor structure.
A second factor analysis was focused on items/scales that were hypothesized to capture antisociality. The initial factor analysis had three couplets (items that are measuring the same items and thus highly correlated with one another) comprising 7 items: SVR Item 10, VRAG Item 5, ASO Item 13, MNS Item 10, ASO Item 9, VRAG Item 6, and SVR Item 11. To resolve this problem items were summed and divided by the number of items in the couplet, creating a single item. The factor analysis then yielded a one factor solution that accounted for 52.22% of the variance in the model and was named the Antisociality Scale.
The third factor analysis was intended to capture impulsivity in the offense. It has been noted that impulsivity in the offense and lifestyle impulsivity are two different constructs
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(Prentky & Knight, 1986). The factor analysis yielded a one factor solution accounting for 68.97% of the variance. The Impulsivity in Lifestyle scale accounted for 58.99% of the model variance in a fourth factor analysis.
The fifth factor analysis comprised scales that were hypothesized to capture stranger offenses. It initially yielded a couplet between RM Item 5 and MNS Item 9, which were
combined using the method described for the Antisociality Scale. A one factor solution emerged that accounted for 53.65%. When we attempted to create a Density of Sex Offense Scale in a sixth factor analysis, the couplet problem was also addressed by combining SOR Item 2, RM Item 2, and ASO Item one. This resulted in a one factor solution that accounted for 70.93% of the variance. The seventh and eighth factor analyses yielded single factors with no couplets and resulted in the Offense Violence Scale and the Nonsexual Violence Scale accounting for 59.77% and 68.56% of the variance in each model, respectively.
Finally, simple correlations were used to correlate the resulting nine scales capturing theoretical psychological constructs with the four factors from the initial replication part of this study. This allowed us not only to corroborate our a priori hypothesis about what each factor was capturing, but also deepen our understanding of the factors for treatment, dynamic treatment change, and help further predict recidivism.
16 Results Static Factor Analysis
While the first EFA’s eigenvalues indicated a four-factor solution, the preselected fit indices yielded a normed chi square 2
𝑑𝑓 = 4.25 (32), p < .001; CFI =0.953, TLI = 0.886, RMSEA
= 0.078 [95% CI = .065, .092]. This solution also included a Heywood case with Item 11 “unrelated/stranger victim” loading on the fourth factor at 1.087 the model to be adjusted by elimination Item 4 and rerunning the analysis. The second EFA also yield a mixed result with eigenvalues indicating a 3 factor solution but included extremely subpar fit indices which included a normed chi square 2𝑑𝑓 = 4.67 (33), p < .001; CFI = 0.941, TLI = 0.883, RMSEA = 0.083 [95% CI = 0.070, 0.096]. The four factor solution yielded a much more cohesive set of fit indices with normed chi square 2𝑑𝑓 = 2.961 (24), p < .001; CFI = 0.977, TLI = 0.937, RMSEA =
0.061 [95% CI = 0.045, 0.077]. Although Kline (2016) recommends a TLI ≥ .95 for a very good model fit, the four factor TLI of 0.937 indicates an acceptable model fit (Byrne, 1994). Likewise, RMSEA is extremely close to the 0.06 cutoff and thus is an acceptable value. Finally, the fourth factor was assessed for interpretability and found to be interpretable. These four factors together account for 74.63% of the variance within the model. Thus, although neither the 3 or 4 factor solution was a perfect fit for these data, taking into account the eigenvalues, fit indices, and the interpretability of the factors, we chose the four factor solution for interpretation and validation. The five items that loaded significantly at the 5% level onto Factor 1 included; (a) age at release in years (reversed); (b) juvenile sex arrest; (c) breach of conditional release; (d) few years
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free prior to index; and (e) never lived with a lover for at least 2 years. This factor accounted for 31.93% of the variance in the model and predominately captured items that address a young chronic offender. It was therefore named the Young Antisociality factor.
To calculate the internal consistency of each factor, congeneric reliability was used as it allows all items and their unique factor loadings to be accounted for when combining the factor loadings. This is in contrast to Cronbach’s alpha, which assumes tau-equivalence, i.e. that all items have equal loadings on a factor (Congeneric Relibility, 2018). The internal consistency of Young Antisociality was low (c = .44). It is important to note that because congeneric
reliabilities take into account each item with all of their unique loadings, this type of reliability can be low. Each Static item measures a different facet of the latent factor, but reliability reflects the extent to which items measure the same latent variable.
The second factor included six items that loaded significantly at the 5% level; (a) juvenile sex arrest; (b) high rate of sexual offending/prior sex offenses; (c) breach of conditional release; (d) few years free prior to index; (e) prior sentencing; and (f) prior nonsexual violence. These items primarily speak to a persistence in offending in both sexual and nonsexual offenses and as such it was named the General Criminality factor. This factor, as noted above, accounted for 19.4% of the model variance. The internal consistency of the factor was fair (c = .62).
Factor three which accounted for 15.28% of the variance, had seven items that that loaded significantly at the 5% level. The four items that loaded positively were: (a) juvenile sex arrest; (b) high rate of sexual offending/prior sex offences; (c) male victim; and (d) two or more young victims, one unrelated. The three items that loaded negatively onto this factor included: (e) age at release in years (reversed); (f) index nonsexual violence; and (g) prior nonsexual violence. This item being composed primarily of young victims, male victims, and a high density
18
of sexual offenses captures pedophilia and thus was named as such. The internal consistency of this factor was low (c = .52).
The fourth factor included three items that loaded significantly at the 5% level; (a) never lived with a lover for at least 2 years; (b) index nonsexual violence; and (c) unrelated stranger victim. This factor accounted for 8.02% of the variance and comprises a combination of items that indicate a young offender who had victims that were strangers and also captures violence. This final factor was named for agonistic continuum (Knight, Sims-Knight, & Guay, 2013). The internal consistency for this factor was low (c = .44).
The first three factors, Young Antisociality, General Criminality, and Pedophilia, were not significantly correlated with one another. Agonistic Continuum was found to be significantly positively correlated (p < 0.05) with Young Antisociality (r = 0.24), General Criminality (r = 0.42), and with Pedophilia (r = 0.19). This intercorrelation between the factors was expected as it was also found between Brouillette-Alarie et al.’s (2016) factors.
Young Antisociality Factor
The full results of the Pearson Correlations between the four factors and the nine external validity scales are presented in Table 5. The Young Antisociality factor was found to positively correlate with the Antisociality Scale (r(508) = .35, p = .01), Offense Violence Scale (r(452) = .15, p = .01), and the Stranger Offenses Scale, r(459) = .14, p = .01. Additionally, Young
Antisociality showed significant positive correlations with both impulsivity scales, Impulsivity in the Offense, r(487) = .27, p = .01, and Impulsivity in Lifestyle, r(442) = .35, p = .01. Conversely Young Antisociality was found to significantly negatively correlate with the Pedophilia Scale (r(518) = -.13, p = .01).
19
As shown in Table 5 General Criminality was found to be highly significantly correlated with the Antisociality Scale (r(508) = .77, p = .01) and the Non-sexual Violence Scale (r(520) = .69, p = .01). The factor was found to be moderately but significantly correlated with the Density of Sexual Offenses Scale (r(519) = .49, p = .01) and Impulsivity in Lifestyle Scale (r(442) = .57, p = .01). General Criminality was also found to have significant but low correlations with the Offense Violence Scale (r(452) = .19, p = .01), Impulsivity in Offense Scale (r(442) = .22, p = .01), and the Stranger Offenses Scale (r(459) = .29, p = .01).
Pedophilia Factor
With regards to the Pedophilia factor Table 5 shows that it was found to be highly significantly correlated with not only the Pedophilia Scale (r(518) = .73, p = .01), but also with the Density of Sexual Offenses Scale (r(519) = .61, p = .01). The Stranger Offense Scale had a significant correlation (r(459) = .26, p = .01) as well, even though it was low. The Pedophilia factor was also found to be significantly negatively correlated with both the Offense Violence Scale (r(452) = -.54, p = .01) and Impulsivity in the Offense Scale (r(487) = -.16, p = .01). Interestingly enough this factor was not found to be significantly correlated with the Hebephilia Scale.
Agonistic Continuum Factor
As Table 5 shows, the Agonistic Continuum factor was significantly correlated with the Antisociality Scale (r(508) = .35, p = .01) and Offense Violence Scale (r(452) = .54, p = .01). It is also notable that Agonistic Continuum was significantly correlated with both impulsivity scales, Impulsivity in the Offense Scale yielding a correlation of (r(487) = .41, p = .01) and Impulsivity in Lifestyle yielding (r(442) = .38, p = .01). There were also two scales that were significantly correlated with Agonistic Continuum but low, Non-sexual Violence Scale (r(520) =
20
.31, p = .01) and Density of Sexual Offenses Scale (r(519) = .15, p = .01). The Pedophilia Scale was significantly negatively correlated with the Agonistic Continuum factor (r(518) = -.25, p = .01).
21 Discussion
The main aim of the current study was to replicate Brouillette-Alarie et al.’s (2016) factor structure for non-repetitive, non-redundant items of the Static-99R and Static-2002R, all while staying true to their methods in Mplus. Our original hypothesis that we would successfully replicate the three factor solution that Brouillette-Alarie et al.’s (2016) EFA had yielded (i.e. Persistent Paraphilia, Youthful Stranger Aggression, and General Criminality) was only partially corroborated. Their three-factor solution was not a good fit for the current study’s data. Instead, given the pre-chosen fit indices, a four factor solution was found to be a better fit for the data. Pedophilia Factor
Although a full replication was not achieved, Table 3 shows that when the arbitrary factor loading cutoff of .40 was used, there were substantial similarities between the current study’s resultant factor structure and that of Brouillette-Alarie et al.’s (2016). Their
Persistence/Paraphilia factor (Table 1), which was the first factor in their analysis, comprised: (a) high rate of sexual offending/prior sex offenses; (b) male victim; (c) two or more young victims, one unrelated; and (d) noncontact sex conviction. Additionally, (e) age at release in years
(reversed) negatively loaded onto this factor, but was later removed to help internal consistency. The Pedophilia factor was the third to emerge in the current study (see Table 3 for the factor loadings). Factors with >.4 loadings on this factor included: (a) high rate of sexual
offending/prior sex offenses; (b) male victim; (c) two or more young victims, one unrelated, as well as a negative loading of (d) index nonsexual violence. The two factor structures are quite similar with the only differences being “noncontact sex conviction” in Brouillette-Alarie et al.’s
22
(2016) compare with “index nonsexual violence” in the current study’s. The primary difference between these two factor structures appears to be that in the Brouillette-Alaire et al. (2016) study the factor is picking up not only pedophilia, but also paraphilia such as voyeurism and
exhibitionism. In contrast, the current study’s factor analysis indicated that this factor is more of a child molester factor versus a child rapist factor (Knight & Prentky, 1990).
The second aim of this study focused on the validation of the latent psychological factors with external scales. Previously it was noted that the Pedophilia factor correlated positively with the Pedophilia Scale, the Density of Sexual Offenses Scale, the Stranger Offense Scale, and negatively with the Offense Violence Scale and the Impulsivity in Offense Scale (Table 5). The original hypothesis that the Pedophilia factor would correlate with our Pedophilia and Density of Sexual Offenses Scales and would covary negatively with our Offense Violence and Hebephilia scale was therefore almost entirely correct, with the caveat that Hebephilia did not correlate at all with the Pedophilia factor. Pedophilia is defined as a persistent sexual interest in prepubescent children (Seto, 2008), which often occurs against male boys outside of the offender’s family and often occurs via grooming versus coercion (Knight & Prentky, 1990). The combination of the items that the Pedophilia factor primarily comprised: (a) high rate of sexual offending/prior sex offenses, (b) male victim, (c) two or more young victims, one unrelated, the negative loading of (d) index nonsexual violence, and the external correlation results all suggest that this is a Pedophilia factor.
Young Antisociality Factor
When considering a .4 factor loading cutoff for the Young Antisociality factor, there is a similar result to what emerged for the Pedophilia factor. Brouillette-Alarie et al.’s (2016) second EFA Youthful Stranger Aggression factor comprised: (a) age at release in years (reversed); (b)
23
juvenile sex arrest; (c) never lived with a lover for at least 2 years; (d) index non-sexual violence; (e) unrelated/stranger victim. See Table 1 for Brouillette-Alarie et al.’s factor analysis results. When we considered a .4 cutoff, our Young Antisociality factor partially captured the
Brouillette-Alarie et al. (2016) structure, which as noted above comprised (a) age at release in years (reversed); (b) juvenile sex arrest; and (e) never lived with a lover for at lese 2 years. Although both studies’ factors captured traits of a young offender, the Young Antisociality captured a more persistent young offender, and the Youthful Stranger Aggression captured an aggression component that included unrelated victims (Table 3).
The initial hypothesis of this study was that the full Youthful Stranger Aggression factor from the Brouillette-Alarie et al. (2016) would emerge in the EFA and would correlate positively with our Offense Violence and Impulsivity scales and negatively with our Pedophilia scale. This turned out to be incorrect because our factor structure was slightly different from the original. The Young Antisociality factor that emerged in our EFA did positively correlate with the Offense Violence Scale, as well as both impulsivity scales (Impulsivity in the Offense and Impulsivity in Lifestyle Anticociality Scale). Also as predicted there was a negative correlation between the factor and the Pedophilia Scale. Additionally, our Young Antisoicality factor was also found to have correlate positively with both the Stranger Offenses and the Antisociality scales. This is interesting, as the Static item 11 “unrelated stranger victim” did not significantly load on to Young Antisociality, but rather it loaded on to the fourth factor, Agonistic Continuum. The full correlations can be view in Table 5.
The resulting correlations appeared to be capturing psychopathic traits that often include impulsiveness, selfishness, and aggression (Hare & Neumann, 2006). Harris and Rice (2006) also noted that psychopathic traits often begin early and are persistent throughout the course of
24
life. This is congruent with the Young Antisociality factor correlating not only with offense violence and the two impulsivity scales, but also with the Antisociality scale. It comprises Static items that capture both a juvenile onset of such behaviors and a young offender. One should note that although the Young Antisociality did correlated with the Offense Violence scale, it was a mild correlation. This could have been the result of a truncation effect, as only extremely violent behaviors load on this scale.
General Criminality Factor
As hypothesized, a General Criminality factor did emerge in our EFA. Of all four factors that emerged from the present EFA, in the >.4 factor loading cutoff comparison the General Criminality most closely mirrored its companion Brouillette-Alarie et al.’s (2016) factor. Thus, it was given the same name. Brouillette-Alarie et al.’s (2016) General Criminality factor was comprised of: (a) breach of conditional release; (b) few years free prior to index; (c) prior sentencing; and (d) non-sexual violence and was the third factor to load in their EFA (Table 1). The current study’s General Criminality factor with >.4 loadings comprised (a) high rate of sexual offending/prior sex offenses, (b) breach of conditional release, (c) few years free prior to index, (d) prior sentencing, and (e) prior nonsexual violence. This almost perfect replication of a factor suggests that such a construct is fairly stable (Table 3).
We hypothesized a priori that the General Criminality factor would correlate with our Antisociality Scale, Non-sexual Violence Scale, and both impulsivity scales (Impulsivity in the Offense and Impulsivity in Lifestyle Anticociality Scale). As can be seen in Table 5, our
hypothesis was corroborated. General Criminality was found to be highly significantly correlated with the Antisociality Scale, Non-sexual Violence Scale, and both impulsivity scales. These results suggest that this factor captured multiple facets of psychopathy. The Antisociality facet
25
encompasses criminal versatility (Hare, Neumann, & Mokros, 2018), both in non-sexual and sexual offending. Another psychopathy facet that the factor could be accounting for is that of the Lifestyle facet, which includes impulsivity (Hare et al., 2018). Thus, the data suggest that this study’s General Criminality factor likely covaries with the two facets that the higher-order Factor 1 of the PCL-R comprises (Hare, 1991).
Agonistic Continuum Factor
Of the three items that loaded onto the Agonistic Continuum factor significantly at the 5% level only two items loaded higher than .4, as can be seen in Table 3: (a) index nonsexual violence and (b) unrelated/stranger victim. As noted in a previous section, Brouillette-Alarie et al.’s (2016) Youthful Stranger Aggression factor appears to have spilt into two separate factors in the current study, Young Antisociality and Agonistic Continuum. Whereas Young
Antisociality appears to capture the young persistent offender who shares traits with psychopathy, the Agonistic Continuum factor comprises items that capture aggression and unrelated or stranger victims.
Because this fourth factor was not hypothesized, there were no a priori hypotheses about its potential covariates. It was found to correlate significantly positively with Antisociality Scale and Offense Violence Scale. As can be seen in Table 5 this purported Agonistic Continuum factor actually had a stronger correlation with the Offense Violence Scale than any other factor in this study. As noted in Knight et al. (2013) this could be due to the fact the extreme forms of sexual violence are captured in the upper end of the Agonistic Continuum, which contains sadistic behaviors. Its correlates with both impulsivity scales (Impulsivity in the Offense Scale and Impulsivity in Lifestyle) and lends further credence to the claim that this factor is capturing the Agonistic Continuum. Although for the most part sadists tend to be organized, planning out
26
their offenses (Healey, Lussier, & Beauregard, 2012), some sadist have been found to be impulsive (Stone, 2010). Thus, it would seem likely that this factor is capturing the continuum that it is named after.
Study Limitations
No study is without limitations, and this holds true for the current study. As previously mentioned the current study’s initial EFA that included all 13 items that were utilized in the Brouillette-Alarie et al. (2016) study resulted in a Heywood case that required item 4 “non-contact sex conviction” to be removed from further analyses. This removal of one of the key items does result in essentially a different analysis of the items in the Statics. Not only will this lead to slightly different factor loading, but also to slightly different factor structures as well. As pointed out by Osborned and Fitzpatrick (2012) the lowest threshold for a replication should be the same basic factor structure. This basic factor structure should include the same number of factors as well as the same items loading on the respective factors. These criteria were not met in this replication study.
Another limitation that should be addressed is the lack of perfect fit indices for the chosen factor structure. In the Analysis portion of this paper it was shown that the eigenvalues indicated a three factor solution. The fourth factor did, however, yield an eigenvalue of 0.96. When the fit indices were examined for the three factor solution, the normed chi square, CFI, TLI, and the RMSEA were all subpar. When the four factor solution was examined, it yielded a much more cohesive set of fit indices, but had a TLI lower than the suggested value of .95 which, as Byrne (1994) indicated, is still an acceptable model fit, but indicated some noise in the factor structure.
27
One of the biggest limitations of this study is that it is an EFA. Osborned and Fitzpatrick (2012) note that EFAs merely present a solution based on the data being used. This means that although this study did use a sub-sample of the multi-sample used by Brouillette-Alarie et al. (2016) (n= 2,569), the fact that this sample was smaller and did not include the exact original sample reduces the probability of an exact replication. To address this limitation in the future a Confirmatory Factor Analysis (CFA) would be more conducive to giving firm answer about model fit.
Finally, the sample used was from only one treatment center in one area of the country. There are a number of confounds that can arise due to the homogeneity of the sample. For example, what if this sample happened to comprise a larger than normal population of violent sexual offenders? This could have repercussions that affect the EFA. For future analyses a sample from multiple treatment centers should be used, thus ensuring a heterogenous sample. Not only would a heterogenous sample equalize any confounds, but it would also give further power to the solutions found and improve generalizability.
0 Conclusions
The search for a latent trait model that assesses underlying psychological behaviors of sex offender static actuarials encompasses a decade of research (Allen & Pfugradt, 2014;
Barbaree, Langton, Blanchard, & Cantor, 2009; Barbaree, Langton, & Peacock, 2006; Boughner, 2010; Brouillette-Alarie & Proulx, 2013; Knight & Thornton, 2007; Langton, Barbaree, Hansen, Harkins, & Peacock, 2007; Roberts, Boren, & Thornton, 2002; Seto, 2005; Walters, Deming, & Elliott, 2009) and Brouillette-Alarie et al. (2016) was the first to take into account the nuances of not only the items included in the factor analysis, but also the subtleties of the extraction method and rotational method. Although the current study is not without its aforementioned limitations, it adds to this body of knowledge by shedding light on a potential new factor structure for the Static and giving insight into what the potential underlying factors are measuring. This
knowledge has the potential to address some of the field’s elusive problems, such as the stand-alone reliability for both sexual violence and sadism. By understanding the underlying
mechanisms of such constructs through factor analysis of actuarials, we could not only improve our prediction of future behaviors, but also improve treatment and the measurement of dynamic treatment change.
1 Table 1.
Brouillette-Alarie et al.(2016) Rotated Factor Loadings with GEOMIN Rotation for 13 Items from the Static 99R and Static 2002R. (N=2,569)
Factor 1 Persistence/ paraphilia Factor 2 Youthful Stranger Aggression Factor 3 General Criminality Age at release in
years (reversed)
-.40 .77 -.02
Juvenile sex arrest .34 .45 .15
High rate of sex offending/ Prior sex offenses
.70 .02 .34
Noncontact sex conviction
.55 -.06 -.00
Male victim .66 -.08 -.23
Two or more young victims, one unrelated
.82 .01 -.25
Breach of conditional release
-.05 .18 .83
Few years free prior to index
.04 .35 .68
Never lived with a lover for at least 2 years
.00 .84 -.27
Index nonsexual violence -.39 .46 .23
Unrelated stranger victim .22 .56 .04
Prior sentencing .07 -.05 .93
Prior nonsexual violence -.26 -.01 .86
Eigenvalues 4.24 2.52 1.92
Percent of variance accounted for
24.3 14.5 12.6
2 Table 2.
Rotated Factor Loadings with GEOMIN Rotation for 13 Items from the Static 99R and Static 2002R. Heywood Case in Factor 4 (N=533)
Factor 1 Factor 2 Factor 3 Factor 4
Age at release in years (reversed)
-.29* -.1 .52* .02
Juvenile sex arrest .33* .31* .49* .04
High rate of sex
offending/Prior sex offenses
.63* .45* -.01 .29*
Noncontact sex conviction
.39* .02 -.27* .61*
Male victim .60* -.02 .28* -.1
Two or more young victims, one unrelated
.73* -.02 .15 -.02
Breach of conditional release
-.06 .79* .18 .03
Few years free prior to index
.02 .84* .34* -.04
Never lived with a lover for at least 2 years
-.02 .03 .8* .04
Index nonsexual violence -.61* .14 .07 .39*
Unrelated stranger victim -.03 -.06 .05 1.09*
Prior sentencing .03* .89* -.07 .00
Prior nonsexual violence -.26 .65* -.14 -.01
Eigenvalues 3.96 2.46 1.85 1.27
Percent of variance accounted for
30.5 18.9 14.2 9.8
3 Table 3.
Rotated Factor Loadings with GEOMIN Rotation for 12 Items from the Static 99R and Static 2002R. Item 4 Removed (N=533)
Factor 1 Young Antisociality Factor 2 General Criminality Factor 3 Pedophilia Factor 4 Agonistic Continuum Age at release in
years (reversed)
.88* -.02 -.34* -.02
Juvenile sex arrest .45* .33* .37* .05
High rate of sex offending/ Prior sex offenses
-.04 .48* .59* .11
Male victim .04 -.07 .63* -.04
Two or more young victims, one unrelated
-.01 -.02 .75* -.11
Breach of conditional release
.24* .83* -.05 -.00
Few years free prior to index
.34* .85* .02 -.02
Never lived with a lover for at least 2 years
.51* -.03 .04 .30*
Index nonsexual violence -.01 .01 -.61* .77*
Unrelated stranger victim .05 .03 .03 .85*
Prior sentencing -.04 .91* .03 -.05
Prior nonsexual violence -.07 .648 -.26* .03
Eigenvalues 3.83 2.32 1.83 0.96
Percent of variance accounted for
31.93 19.4 15.28 8.02
4 Table 4.
Factor Loadings on Principal Axis Factoring with OBLIMIN Rotation on External Validity Scales Child Preference(N=533)
Pedophilia Factor
Hebephilia Factor
SSPI Item 2 .91
SRA Item 1 .84
SSPI Item 3 .75 -.39
Crimtot12 .71
SSPI Item 4 .58
SSPI Item 1 .57
SRA Item 5 .57
Excl. Heb .87
5 Table 5.
Correlations of Static Factors and External Validity Scales (N=533) Young Antisociality Factor General Criminality Factor Pedophilia Factor Agonistic Continuum Factor Antisociality Scale
.35** (508) .77** (508) -.10* (508) .35** (508)
Density of Sexual Offense Scale
.05 (519) .49** (519) .61** (519) .15** (519)
Offense
Violence Scale
.15** (452) .19** (452) -.54** (452) .54** (452)
Hebephilia Scale
-.08 (525) -.03 (525) .07 (525) -.08 (525)
Pedophilia Scale
-.13** (518) -.00 (518) .73** (518) -.25** (518)
Impulsivity in Offense Scale
.27** (487) .22** (487) -.16** (487) .41** (487)
Impulsivity in Lifestyle Scale
.35** (442) .56** (442) -.12* (442) .38** (442)
Nonsexual Violence Scale
.10* (520) .69** (520) -.10* (520) .31** (520)
Stranger Offense Scale
.14** (459) .29** (459) .26** (459) .40** (459)
6 Appendix 1
Coding rules for combining the Static-99 and Static 2002 (Brouillette-Alarie et al. 2016)
▪ Age at release in years (reversed) - Age at release was subtracted from 100, which replaces age at release (Static 99R/2002R) resulting in a range of 15-82.
▪ Juvenile sex arrest - This item is coded dichotomously 0 for no and 1 for yes and is indicated by any juvenile arrest for a sexual offence and if said juvenile is convicted as an adult for a separate sexual offense (Static 2002R).
▪ High rate of sexual offending/prior sex offences - This item is made up of the sum of prior sex offences (Static 99), prior sentencing occasions for sexual offences (Static 2002R), and rate of sexual offending (Static 2002R). The range of the sum is 0 to 7.
▪ Noncontact sex conviction - This item consists of any convictions for noncontact sex offences (Static 99R), an any sentencing occasions for noncontact sex offences (Static 2002R). These identical items are counted once, giving this item a range of 0 to 1.
▪ Male victim - The identical items any male victim (Static 99R/2002R) are counted once, making the range for this item 0 to 1.
▪ Two or more young victims, one unrelated - This item is composed of young unrelated victims (Static 2002R) and is dichotomous, 0 or 1.
▪ Breach of conditional release - This item comes from any community supervision violation item (Static 2002R) and is also dichotomous, 0 or 1.
▪ Few years free prior to index - This item is composed of years free prior to sex offense (Static 2002R) and is dichotomous, 0 or 1.
▪ Never lived with a lover for at least 2 years - The item is composed of ever lived with (Static 99R) and is dichotomous, 0 or 1.
7
▪ Index nonsexual violence - The item is composed of index non sexual violence (Static 99R) and is dichotomous, 0 or 1.
▪ Unrelated/stranger victim - This item merges any unrelated victim (Static 99R and 2002R counted once) and any stranger victim (Static 99R and 2002R counted once). The range for this item is 0 to 2.
▪ Prior sentencing - The following item merges any prior involvement with the criminal justice system (Static 2002R) and prior sentencing occasions for anything (Static 2002R) and then sums it with prior sentencing dates (Static 99R) resulting in a range from 0 to 4.
▪ Prior nonsexual violence - This item is the sum of prior nonsexual violence (Static 99R) and any prior non sexual violence sentencing occasion (Static 2002R), resulting in a range of 0 to 2.
8 Appendix 2
Items Selected to Measure Each External Validity Domain Pedophilia
This items selected to measure this domain capture a sexual preference for children, particularly prepubescent children, usually defined as ages 3 -10 years of age (Seto, 2017). Because there has been some debate on the exact age cut off, for the purpose of this scale we look at children 11 years or younger (Seto, 2008; 2018). It is important to note that the offender age must be greater or equal to 16 years old and they must be 5 or more years older than the victim, to rule out a Romeo and Juliet scenario (American Psychiatric Association, 2013). The pedophilia scale, which had a Cronbach alpha of .85, comprised the following items:
▪ SRA Item 1 - Which is Child-Preference and defined as a stronger sexual response to children than adults and child is defined as prepubescent females (12 years or younger) and prepubescent or young teenage males. In the coding system 2 = generally applies, 1 = partially applies, and 0 = does not apply (Thornton & Knight, 2015).
▪ SSPI Item 1 - Offender has male victim. The coding system for this item is 2 = yes and 0 = No, female victims only (Seto & Lalumière, 2001).
▪ SSPI Item 2 - Offender has more than one child victim. In the coding system 1 = yes and 0 = No, single victim only (Seto & Lalumière, 2001).
▪ SSPI Item 3 - Offender has a victim aged 11 or younger. The coding system for this item is 1 = yes and 0 = No child victims were 12 or 13 years old (Seto & Lalumière, 2001).
▪ SSPI Item 4 - Offender has an unrelated child victim. In the coding system for this item 1 = yes and 0 = no, related victims only.
9
▪ SRA Item 5 - This is labeled as Emotional Congruence with Children and is used to identify an offender as finding it easier to relate to children than to adults. The scoring rules are 2 = generally applies, 1 = partially applies, and 0 = does not apply (Thornton & Knight, 2015).
Hebephilia
This scale addresses an offender’s preference for pubescent children which as Seto (2017) points out as being age 11-14 years old. However, like with pedophilia there has been some argument with the age cutoff particularly due to the fact that children are maturing at an earlier age then once was normal (Seto, 2017). That being said, for the purposes of this paper this scale will define hebephilia as 12 or 14 as so it will not overlap with Seto’s SSPI items. The hebephilia scale was comprised of following items and had a Cronbach alpha of .712.
▪ Heb_Vic - This item was coded from the MTC data using the victim age items and indicates that an offender has a victim age 12 to 14.
▪ Excl_Heb - Indicates that an offender only has victims age 12-14 year old and was coded from the MTC database using victim age.
Antisociality
This scale captures antisocial personality type both from the adult and juvenile
perspective. It also includes multiple offense types, negative attitudes towards intervention, and failure upon release. It is made up of the following items and has a Cronbach alpha of .878.
▪ PCLFCET4 - This is item is captured from the PCL-R items 10, 12, 18, 19, and 20 that make up the fourth facet of the PCL-R and captures antisocial personality type (Hare, 1991).
10
▪ SVR-20 Item 10 - Past Nonviolent Offenses. This item addresses an individual’s
propensity for serious non-sexual violence. The coding for this item is n = no, ? = Maybe, y = yes, and O = omit (Boer et al., 1997).
▪ VRAG Item 5 - Criminal history score for nonviolent offenses (from the Cormier-Lang systems) prior to the index offense. This item looks for a pattern of both adult and
juvenile nonviolent charges. It is coded as 0 = -2, a score of 1 or 2 = 0, and a score of 3 or above = +3 (Quinsey et al., 1998).
▪ A-SOAP-II Item 13 - Multiple of Offenses. This item is limited to legally charged offenses which include; sexual offenses (e.g. rape, indecent assault, and gross lewdness); non-sexual offenses (e.g. assault, kidnapping, attempted murder, and manslaughter); property offenses (e.g. burglary, breaking and entering, and embezzlement); fraudulent offenses (e.g. fraud, using stolen credit cards, and counterfeiting); drug offenses (e.g. drug trafficking and giving alcohol to a minor); serious motor vehicle offenses (e.g. operating to endanger, chronic speeding, and leaving the scene of an accident); conduct offenses (e.g. disorderly conduct, malicious mischief, and failure to pay child support) other rule breaking offenses (e.g. there is no clear victim but the law has been broken, such as escape from legal custody, and failure to appear). This item is coded as 0 = 1 type of offense, 1 = 2 types of offenses, and 2 = 3 or more types of offenses (Prentky &
Righthand, 2003).
▪ A-SOAP-II Item 11 - Antisocial behavior age 18 or older. This item includes vandalism and destruction to property, other non-assaultive offenses, fighting, assault, assault & battery, carrying or use of a weapon in the commission of a crime, such as armed
11
endanger, operating under the influence). The item is coded as 0 = none or minimal, 1 = moderate (2 or 3 of the listed criteria are met), and 2 = strong (4 or more of the criteria are met) (Prentky & Righthand, 2003)
▪ RM 2000 Item 3 - Criminal Appearances. This item captures the frequency of criminal appearances and is coded as 0 = 4 or less and 1 = 5 or more (Thornton, 2002).
▪ MnSOST-R Item 10 - Is there evidence of adolescent antisocial behavior in the file? This Item is looking for a patterned history of antisocial acts prior to adulthood. The item is coded as -1 = no indication, 0 = some relatively isolated antisocial acts, and +2 = persistent, repetitive pattern (Epperson et al., 1998).
▪ A-SOAP-II Item 9- Juvenile antisocial behavior. This item addresses nonsexual delinquent behavior as vandalism, malicious mischief, fighting and physical violence, theft or robbery, motor vehicle related charges (e.g. reckless driving, or operating under the influence). The item is coded as 0 = none or minimal, 1 = moderate (2 or more criteria present) and 2 = strong (4 or more different criteria present) (Prentky & Righthand, 2003).
▪ RM 2000 Item 10 - Burglary. This item is coded as 0 = no and 1 = yes (Thornton, 2002).
▪ VRAG Item 6 - Failure on prior conditional release. This item includes probation or parole violations, bail violation, failure to comply. This item is coded as 0 = no and +3 = yes (Quinsey et al., 1998).
▪ SVR-20 Item 11 - Negative attitudes towards intervention. This item captures an offender’s rejection of correctional or mental health support or a lack of motivation to utilize said support. The coding for this item is n = no, ? = Maybe, y = yes, and O = omit (Boer et al., 1997).
