Do Developmental and Life-Course Theory Risk Factors Equally Predict Age of Onset Among Juvenile Sexual and Nonsexual Offenders?

Abstract

Previous empirical inquiries into the etiology of juvenile sex offending have been largely atheoretical. Consequently, a call for studies conducted utilizing developmental and life-course (DLC) criminological theory has been made to better understand the onset, development, risk, and protective factors of juvenile sex offending. Therefore, this study contributes to the discussion by testing key predictions proposed by the DLC framework regarding the theoretical correlates of early onset offending, as applied to juvenile sex offenders (JSOs) and juvenile nonsex offenders (JNSOs). Drawing on a data set of more than 64,000 youth referred to the Florida Department of Juvenile Justice, results indicate that although the number and severity of risk factors for early age of onset differ between the JSOs and JNSOs, the specific type of risk factors that emerged align with DLC theory predictions. The implications of these findings and contributions for DLC theory are also discussed.

Keywords

developmental and life-course criminology sexual offending juveniles risk factors

Research on juvenile sex offenders (JSOs) has been growing in recent years, perhaps, in part, due to the notable portion of sex crimes that these young offenders commit. Specifically, JSOs are responsible for 20% of all those arrested for sex crimes in the United States in 2013 (Federal Bureau of Investigation, 2014), and commit up to 20% of all rapes, half of all child sexual abuse, and 33% of all sex crimes against other juveniles in the United States (Barbaree, Hudson, & Seto, 1993). In short, JSOs appear to be a sizable and problematic group of offenders who warrant further research on the development of this behavior, and how prevention strategies can be used to intervene prior to the onset of sexual offending.

Several recent studies have made major contributions to our understanding of juvenile sex offending by examining trajectories of JSO behavior (Cale, Smallbone, Rayment-McHugh, & Dowling, 2016; Piquero, Farrington, Jennings, Diamond, & Craig, 2012) and the continuity of sexual offending from adolescence into adulthood (Lussier, Corrado, & McCuish, 2016; Reingle, 2012; van Den Berg, Bijleveld, & Hendriks, 2017; Zimring, Piquero, & Jennings, 2007). This research on the rate, continuity, and desistance of JSOs has made significant headway toward understanding the development and life course of these young offenders, and led to important implications for both criminological theory and public policy.

Specifically, this research has helped to illuminate the empirical realities that juvenile sex offending and recidivism are relatively uncommon, and sex offending continuity (i.e., sexual offending in adolescence and in adulthood) is even more rare. Thus, efforts to try and predict adult sex offending behavior (and develop policies such as registering JSOs for life) based on knowledge of sex offending behavior in adolescence almost always results in a false positive.

Other research has focused on the demographic, social, school, psychological, and family risk factors associated with juvenile sex offending (Baglivio et al., 2014; Calley, 2007; Fox, 2017; Hunter, Hazelwood, & Slesinger, 2000; Rice & Harris, 1997; Robertiello & Terry, 2007; Seto & Lalumière, 2010; Worling, 2001). In general, this research indicates that certain personality traits, psychopathologies, and childhood traumas are significant risk factors for juvenile sex offending. Specifically, Seto and Lalumière’s (2010) meta-analysis of 59 studies on the predictors and correlates of juvenile sex offending compared theoretically derived risk factors for adolescent male sex offenders with adolescent male nonsexual offenders. Results indicated that overall, deviant sexual interests, sexual and physical abuse in childhood, and certain psychopathologies were strong and significant predictors of juvenile sex offending versus nonsexual offending among the all-male samples (see also Hall & Hirschman, 1991, 1992; Marshall & Barbaree, 1990; Ward & Beech, 2006). Notably, JSOs scored lower than nonsex offenders in terms of criminal involvement, antisocial peer associations, and substance use, but JSOs still showed “extensive” histories of criminal behavior, conduct problems, and antisocial tendencies as compared with nonoffenders (Seto & Lalumière, 2010; see also Chewning, 1991; Etherington, 1993; Katz, 1990; Lindsey, Carlozzi, & Eells, 2001; Valliant & Bergeron, 1997).

In a recent study examining the unique and significant risk factors for JSOs among male and female offenders in Florida, Fox (2017) found that having low empathy, high impulsivity, depression and/or psychosis; experiencing sexual abuse, an earlier age of criminal onset; and being male all significantly increased the risk of sexual versus nonsexual juvenile offending, even while controlling for all other theoretically derived measures in the analysis.

Age of Onset and Juvenile Sex Offending

Although this body of research has been an important step toward understanding the correlates and criminal careers of these young offenders, far less research has examined the unique predictors of the age of onset for juvenile sexual offending. For instance, just two studies in Seto and Lalumière’s (2010) meta-analysis examined age of onset, and the results indicated that lower age at first intercourse predicted an earlier age of onset for JSOs. Specifically, Seto and Lalumière (2010) stated that childhood intercourse, likely caused by childhood sexual abuse, is related to the commission of a sex offense (Hanson & Bussiere, 1998; Hanson & Morton-Bourgon, 2005). Treating sexual abuse after it occurs is unlikely to reduce reoffending among identified JSOs, but preventing the abuse may also prevent sexual offending. Thus, identifying the risk factors that increase the likelihood of committing a sexual offense (and at an earlier age) may increase the opportunities to prevent sexual offending from ever occurring.

Indeed, Seto and Lalumière’s (2010) meta-analysis prompted further inquiry into the age of onset for sex offenders (Lussier, Blokland, Mathesius, Pardini, & Loeber, 2015). In fact, Blokland and Lussier (2015) stated that “(t)he most important theoretical question to be answered is whether sex offenders should be regarded as similar or different to nonsexual offenders in terms of the etiology of their offending behavior” (p. 16).

To address this question, Lussier and colleagues (2015) examined the childhood risk factors predictive of criminal onset in adolescence (below age 18) versus adulthood (age 18 and older) for sex offenders. This study, which drew on a sample of 92 sex offenders, showed that youth onset offenders were more likely to live in a poor neighborhood, be exposed to structural deficits, have behavioral issue(s), have a psychological disorder, commit more delinquent acts, and be more sexually active, as compared with the adult onset offenders (Lussier et al., 2015). This study was among the first to demonstrate the wide variety of developmental correlates predictive of an adolescent versus adult onset of sex offending. Similarly, Carpentier, Leclerc, and Proulx (2011) found that sexual offenders with an early onset, prior to age 12, were more likely to have early aggressive behavior, deviant sexual behavior, and parents who were victims of sexual crimes, as compared with those with later criminal onset in adolescence and/or adulthood.

Taken together, these studies support the notion that risk factors predictive of early onset offending will likely differ from predictors of adolescent onset offending among JSOs. However, there has been little consensus on the reason for the earlier age of onset among JSOs. To address this gap, scholars examining the etiology of juvenile sex offending have argued the importance of examining JSOs using a theoretical lens, specifically developmental and life-course (DLC) criminological theories (Lussier, 2017; McCuish & Lussier, 2017). Specifically, Lussier (2017) noted that although developmental changes have been shown to correlate with juvenile sexual offending (such as childhood trauma and abuse), very little is known on the risk factors for the deviant sexual behavior, and particularly, at such an early age. Consequently, Lussier (2017) called for more research on JSOs from a DLC perspective, specifically requesting studies that test the applicability of DLC theories to this unique population, and identify which risk factors are most predictive both of sexual offending and early (vs. adolescent) age of criminal onset.

DLC Theoretical Perspective

DLC theories emerged as a prominent perspective in the 1990s by criminological researchers who believed that the risk and protective factors for offending, and the resultant criminal career patterns, are not the same for all individuals (Farrington, 2003; Loeber & LeBlanc, 1990). Therefore, DLC theories aim to identify the within-individual causes of criminal offending, and the various correlates of criminal behavior over the life course. Unlike other theoretical perspectives in criminology, DLC is comprised of multiple subtheories, each aiming to explain three main issues: (a) the onset and development of criminal behavior over the life course, (b) the risk and protective factors for crime at different ages, and (c) the effect of life events on the course of criminal development (Farrington, 2003).

Although DLC theories have been very successful and increasingly popular within criminology (Farrington, 2003), the tenets of DLC theory have not been applied to examine the unique risk factors contributing to variations in age of criminal onset among JSOs. As Farrington (1989, 1991, 1998) argued that specific explanations were unnecessary for individuals involved in specific types of crime, DLC theory provides an appropriate framework to test whether theoretically derived risk factors are predictive of onset of offending for JSOs and juvenile nonsex offenders (JNSOs).

Therefore, this study seeks to determine whether predictions made regarding the risk factors for early versus adolescent onset of criminal behavior, as proposed by the DLC theoretical framework, equally apply for juvenile sex and nonsex offenders. To do this, we draw on the theoretical predictions made about early versus adolescent onset by DLC theories to evaluate the applicability of these theoretical predictions for juvenile sexual and nonsexual offenders.

DLC Theory and Early Age of Onset

Several major DLC perspectives make predictions regarding the risk factors for early versus adolescent onset of criminal behavior. In general, DLC research suggests that the etiology of early onset offenders arises from neuropsychological deficits that interact with negative social environments such as the family, school, and community to produce the early start and continuity of criminal behavior (Farrington, 2003; Moffitt, 1993; Patterson, DeBaryshe, & Ramsey, 1989; Patterson & Yoerger, 2002; Piquero, Farrington, Fontaine, et al., 2012; Thornberry & Krohn, 2005). Specifically, neuropsychological issues including impulsivity, inattention (i.e., attention-deficit disorder [ADD]), difficult temperaments (i.e., conduct disorder, aggression), and cognitive deficits, as well as weak parental attachment or lack of affection, structural adversity, low interest and performance in school, and abuse and/or neglect in childhood are recurring risk factors in the DLC literature (see Farrington, 2003; Lahey, Moffitt, & Caspi, 2003; Patterson et al., 1989; Patterson & Yoerger, 2002; Piquero, 2001; Piquero & Moffitt, 2005; Thornberry & Krohn, 2005). Early onset offenders are predicted to have few protective factors in terms of family, school, and community support (Farrington, 2003; Moffitt, 1993; Patterson & Yoerger, 2002; Thornberry & Krohn, 2005).

DLC Theory and Adolescent Age of Onset

With respect to “later onset” offenders, who typically show delinquent and criminal behavior in their early- to midteenage years, DLC theories predict that these youth are more susceptible to peer influences, versus family, school, and neuropsychological deficits (Farrington, 2010; Patterson et al., 1989; Patterson & Yoerger, 2002). Instead, adolescent onset offenders are more likely to offend due to social pressures such as peer influence and emulation of deviant peers, and then desist in early adulthood (Moffitt, 1993).

As adolescence is a transition period when children become less dependent on parents and more concerned with individualism and peer acceptance, higher rates of delinquency (particularly for “adult-like” behavior such as underage drinking, sex, and drug use) are more common (Moffitt, 1993). However, as prosocial behaviors are more promoted in adulthood and the need to fill the “maturity gap” decreases, adolescent onset offenders desist (Moffitt, 1993).

Current Study

The purposive selection of the DLC theoretical framework emphasizes the developmental aspect of offending, and unique relationships between psychological, biological, social, parental, and peer influences that may influence onset of offending at different ages. Given the largely atheoretical examination of JSOs and specific call for studies to utilize DLC theory to better understand the onset, development, and risk and protective factors of juvenile sex offending, this study tests whether DLC perspectives on early onset offending can be generalized to JSOs with criminal histories exclusive to sex offending.

This study contributes to the discussion prompted by Lussier (2017) by focusing on two specific developmental stages (childhood and adolescence), and the corresponding DLC-informed risk factors for offending during each developmental stage. By examining the onset of JSO behavior, and the associated risk factors for it, we may be able to better understand the etiology of juvenile sex offending (and how it differs from early onset nonsexual offending) in three novel ways. First, including and testing DLC-informed risk factors for early onset juvenile sex offending can help address a notable limitation in existing research (Lussier, 2017). Second, this is one of the few studies conducted to specifically examine the predictors of age of onset among JSOs. Although it is the norm to employ age of onset as a control variable, in this study, onset of offending will be examined as an outcome measure to determine whether DLC-informed risk factors for early onset offending apply to JSOs. Finally, this study aims to determine whether specific DLC risk factors are unique to early onset juvenile sex offending versus nonsexual offending.

These aims and novel contributions are also important as research on the etiology of juvenile sex offending has often not included a comparison group of nonsex offenders. To this end, Zakireh, Ronis, and Knight (2008) noted, “few studies have included appropriate comparison groups (e.g., juvenile offenders who have not committed sexual crimes). Without such control groups, it is difficult to determine whether observed results are linked with sexual offending in particular or with delinquency in general” (p. 324). Specificity-design studies such as this allow for the determination as to whether a variable distinguishes early onset JSOs from nonsexual and adolescent onset offenders, and is, therefore, a potential causal candidate in the etiology of juvenile sex offending (Garber & Hollon, 1991).

Ultimately, the current study represents an innovative approach to further understanding the etiology of juvenile sex offending. Building on previous studies and calls for this type of research (e.g., Fox, 2017; Lussier, 2017; McCuish & Lussier, 2017; Seto & Lalumière, 2010), this study draws on a large sample of male and female juvenile offenders to determine whether the DLC-informed risk factors for early onset (i.e., neuropsychological, family, structural deficits, school) versus adolescent onset can explain early onset offending specifically for JSOs with criminal histories exclusive to sex offending.

Method

Data

The data set utilized in this study was retrieved from the Florida Department of Juvenile Justice (FDJJ), following approval from the lead author’s university institutional review board. Because Florida does not specify a minimum age for criminal responsibility, referrals for all juvenile offenders, as young as age 7, are included in the FDJJ data set. In total, 64,329 juvenile sex and nonsex offenders were referred to FDJJ and followed until they aged out of the system between 2007 and 2012. In short, if a juvenile committed an offense (status, misdemeanor, felony) prior to age 18, the offense and the offender are included in this database.

Per Florida statute, JSOs must have committed a misdemeanor or felony that met the requirements of a sex offense. Therefore, juveniles with a criminal history comprised entirely of either misdemeanor and/or felony sex offense(s) made up the population of JSOs under study.¹ Conversely, juveniles with a criminal history exclusive to nonsex offenses (violent, property, drug) comprised the population of JNSOs. In other words, juveniles classified as sex offenders could not have criminal histories with nonsex offenses and nonsex offenders could not have criminal histories with sex offenses. Therefore, out of the 64,329 juvenile offenders, 4,153 were JSOs whereas 60,176 were classified as JNSOs.

Measures

In addition to records of criminal history, a referral to FDJJ prompts the issuance of the Positive Achievement for Change Tool (PACT). This tool is used to assess the unique needs and future risk of each adjudicated juvenile offender for programming and placement purposes. The PACT provides extensive information on not readily available information, such as adverse childhood experiences (ACEs) and the presence of psychopathologies (for a complete review, see Fox, Perez et al., 2015). This method of data collection has high validity because self-reported responses are verified by official records, to substantiate the veracity of claims.

The FDJJ offender database and the PACT assessment are widely used in criminology due to the high validity of the measures (e.g., Baglivio et al., 2014; Baglivio, Wolff, Piquero, & Epps, 2015; Fox, 2017; Fox & Delisi, 2018; Fox, Perez et al., 2015; Perez, Jennings, & Baglivio, 2018; Wolff, Baglivio, & Piquero, 2017) and large population of offenders. Interviews and file reviews are conducted to ensure the most accurate responses are obtained (Fox, Perez et al., 2015), and Baglivio and colleagues performed quality checks on the PACT data, which indicate the data are highly reliable (see Baglivio, 2009; Baglivio & Jackowski, 2013). Furthermore, several studies have utilized the same data and exact dichotomous coding as the current study (Fox & Delisi, 2018; Fox, 2017) to indicate the presence or absence of specific risk factors for juvenile sex offending.

Age of Onset

This study draws on insights from Erikson’s (1963) psychosocial developmental stages to guide the operationalization of age of onset. Erikson proposed two major phases of development relevant to this study (i.e., ages where youth in this study began offending). The first stage, industry versus inferiority, emphasizes the school years in childhood (ages 5-12). This is a period when children begin to juggle the growing demands of school, friendships, and extracurricular activities. Conversely, the later ego identity versus role confusion stage (ages 13-18) demarcates childhood from adolescence, where autonomy takes precedence (McMaken, 2000).

To determine whether DLC perspectives can be generalized to onset of juvenile sex offending, and not general offending with sex offense histories, the population was partitioned based on the youths’ offense histories (exclusive sex offenses vs. exclusive nonsex offenses) and age of onset according to Erikson’s (1963) definition. Specifically, all those with a criminal onset at 12 years of age or less were classified as having an “early” onset, whereas those who began their offender careers between 13 and 18 years were classified as “adolescent” onset. The resultant outcome variable represents a dichotomy, where 1 = early onset and 0 = adolescent onset. Of the JSOs, 1,405 were identified as early onset, whereas 2,748 were identified as adolescent onset offenders. Of the JNSOs, 14,613 were early onset offenders, whereas 45,563 identified as adolescent onset offenders.

DLC Risk Factors for Early Age of Onset

Demographics

The demographic control measures of gender and race are included to determine whether, and to what extent, the likelihood of being an early onset versus adolescent onset JSO versus JNSO varies by gender or race (see Ellis, Beaver, & Wright, 2009; Fix, Cyperski, & Burkhart, 2017; Murphy, DiLillo, Haynes, & Steere, 2001). Gender is a dichotomous variable, where 1 = male and 0 = female. Race is coded 1 = White and 0 = non-White.

Structural deficits

DLC theories suggest that less than ideal structural factors may place a strain on caretakers, serving as a hindrance to a successful parent–child relationship (Farrington, 2005; Murray & Farrington, 2010). To tap into potential financial strains, annual family income is included as an ordinal variable, where 0 = less than US$15,000, 1 = US$15,000-US$34,999, 2 = US$35,000-US$49,999, and 3 = US$50,000+.

Child maltreatment

Research indicates that childhood trauma, abuse, and maltreatment relate to higher risk of offending, known as the victim–offender overlap (see Baglivio, Wolff, DeLisi, Vaughn, & Piquero, 2017; Jennings, Higgins, Tewksbury, Gover, & Piquero, 2010; Widom, 1989). These ACEs, particularly childhood sexual abuse, have also been shown to be predictors of sexual offending among juveniles (Fox, 2017; Morais, Alexander, Fix, & Burkhart, 2018; Seto & Lalumière, 2010). For example, McCuish, Cale, and Corrado (2017) found that adolescent sex offenders are more likely than adolescent nonsex offenders to have sexual abuse histories and that family histories of sexual abuse have a strong and significant effect on youth sexual abuse and, consequently, sex offenses committed by youth (McCuish et al., 2017). This study, therefore, examines four types of child maltreatment as predictors of sexual offending and age of onset: emotional neglect, physical abuse, sexual abuse, and witnessing household violence. All are dichotomous measures, where 1 = history of neglect, abuse, or exposure to violence, and 0 = no history of childhood trauma.

School

A dichotomous variable of youth’s involvement in school activities was used, where 1 = not interested in school activities and 0 = interested or involved in school activities.

Familial adversity

Put forth as one of the dominant risk factors during childhood, DLC theories emphasize the salient role parents play, and if that role becomes tenuous, the effects may lead to an early onset of offending (Baglivio et al., 2015; Fox, Jennings, & Farrington, 2015). To examine whether, and to what extent, familial adversity uniquely identifies onset among juvenile offenders, four measures were used: parental supervision (1 = sporadic or inadequate supervision, 0 = consistent supervision), punishment (1 = consistently overly severe, erratic, or insufficient punishment, 0 = consistently appropriate punishment), mental health issues (1 = yes, 0 = no), and incarceration history (1 = yes, 0 = no), which were all dichotomous.

Neuropsychological deficits

Although neuropsychological deficits cannot be measured directly, five psychopathologies are used in the analysis: ADD/attention-deficit hyperactivity disorder (ADHD), anger/irritability, empathy, impulsivity, and psychosis. These psychopathologies are theoretically predicted to relate to an earlier onset of offending according to DLC theory, and have been shown to predict sexual offending, as well as the type of sexual offending behavior and the rate of sex crime recidivism (Becker, Harris, & Sales, 1993; DeLisi et al., 2008; Fagan & Wexler, 1988; Fox, 2017; Fox & DeLisi, 2018; Hanson & Bussiere, 1998; Robertiello & Terry, 2007; Worling, 2001). Both impulsivity and ADD/ADHD measures are dichotomized, with 1 = presence of the specific psychopathology. Conversely, empathy is a binary measure, where 1 = unempathetic toward victims and 0 = empathetic to victims. Anger/irritability is a dichotomous variable, where 1 = consistently gets angry or irritable and 0 = never or occasionally gets angry or irritable. To distinguish between juveniles with psychosis versus those without, a dichotomous variable was included, where 1 = psychotic symptoms and 0 = no psychotic symptoms.

Neuropsychological deficit (ND) × Familial adversity (FA)

To determine whether, and to what extent, the presence of both neuropsychological deficit (ND) and familial adversity (FA) measures are predictive of an early onset of offending for both JSOs and JNSOs, an interaction term was included (Moffitt, 1993). To appropriately test Moffitt’s ND × FA prediction, variables were created for both ND (ADD + anger + empathy + impulsivity + psychosis) and FA (parental supervision + parental punishment + parental incarceration + parental mental illness), which were then multiplied together to create the interaction term. Therefore, juvenile offenders with nonzero values must be afflicted by both ND and FA measures.

DLC Risk Factor for Adolescent Age of Onset

Peers

Indicative of an adolescent onset of offending, DLC predicts that peer influence is highly pronounced during adolescence, when parental influence begins to wane and is replaced by peer influence and social factors (Agnew, 1991; Akers, Krohn, Lanza-Kaduce, & Radosevich, 1979; Chung & Steinberg, 2006; Simons, Whitbeck, Conger, & Conger, 1991). To measure peer influence, a single dichotomized variable was included in the model, where 1 = strongly or somewhat admires/emulates antisocial peers and 0 = does not admire/emulate antisocial peers.

Analytic Technique

To determine whether, and to what extent, DLC theoretical predictions regarding risk factors for early versus adolescent offending can be generalized to JSOs, the analysis proceeds in three stages. First, descriptive statistics and chi-square tests of association and odds ratio (OR) effect sizes are presented to determine the bivariate relationships between DLC theoretically informed risk factors for early and adolescent onset of offending among JSOs and JNSOs (Table 1).²

Table 1.

DLC Risk Factors for Early vs. Adolescent Onset by Juvenile Offender Types.

	JSOs				JNSOs
	Early onset%	Adolescent onset%	χ²	OR	Early onset%	Adolescent onset%	χ²	OR
Sex
Male	93.5	92.9	0.62	1.00	81.2	76.1	168.59***	1.07***
Female	6.5	7.1	0.62	1.00	18.8	23.9	168.59***	1.07***
Race
White	36.4	42.7	15.30***	0.57***	32.9	39.8	223.41***	0.83***
Non-White	63.6	57.3	15.30***	0.57***	67.2	60.3	223.41***	0.83***
Family income
Less than US$15,000	19.9	17.9		1.11	22.2	18.7	510.95***	1.18***
US$15,000-US$34,999	56.5	53.8	19.87***	1.05	57.9	52.5		1.10***
US$35,000-US$49,999	18.1	19.2		0.94	14.7	19.0		0.77***
US$50,000+	5.5	9.2		0.59***	5.2	9.8		0.53***
Household violence
Witnessed household violence	85.8	77.1	44.67***	1.11***	87.6	80.3	396.17***	1.09***
No household violence	14.2	22.9	44.67***	1.11***	12.4	19.7	396.17***	1.09***
Emotional neglect
Emotionally neglected	20.6	14.1	29.30***	1.46***	19.7	17.7	31.06***	1.11***
Never emotionally neglected	79.4	85.9	29.30***	1.46***	80.3	82.3	31.06***	1.11***
Physical abuse
Physically abused	41.4	31.2	43.35***	1.32***	34.0	27.6	218.08***	1.23***
Never physically abused	58.6	68.9	43.35***	1.32***	66.0	72.4	218.08***	1.23***
Sexual abuse
Sexually abused	24.7	20.0	11.84**	1.23***	12.7	10.8	37.95***	1.17***
Never sexually abused	75.3	80.0	11.84**	1.23***	87.4	89.2	37.95***	1.17***
School involvement
No interest in school activities	48.0	37.6	37.54***	1.28***	51.6	45.0	171.90***	1.15***
Interested/involved	52.0	62.4	37.54***	1.28***	48.4	55.0	171.90***	1.15***
Peers
Admires antisocial peers	60.4	46.8	68.78***	1.29***	69.7	62.8	231.69***	1.11***
Does not admire antisocial peers	39.6	53.2	68.78***	1.29***	30.3	37.2	231.69***	1.11***
Parental supervision
Sporadic/inadequate	58.8	45.6	64.23***	1.29***	65.8	54.2	609.87***	1.21***
Consistent	41.2	54.4	64.23***	1.29***	34.2	45.9	609.87***	1.21***
Parental punishment
Severe, insufficient, or erratic	52.2	37.9	76.66***	1.38***	57.7	46.7	526.40***	1.23***
Appropriate punishment	47.8	62.1	76.66***	1.38***	42.3	53.3	526.40***	1.23***
Parental incarceration
History of incarceration	74.0	60.2	76.84***	1.23***	76.7	62.5	1.0e3***	0.92***
No history of incarceration	26.0	39.8	76.84***	1.23***	23.3	37.5	1.0e3***	0.92***
Parental mental illness
History of mental illness	12.8	9.2	12.65***	1.39***	11.1	8.0	134.10***	1.39***
No history of mental illness	87.2	90.8	12.65***	1.39***	88.9	92.0	134.10***	1.39***
ADD/ADHD
History of ADD/ADHD	45.3	31.5	76.94***	1.43***	33.0	20.3	1.0e3***	1.62***
No history of ADD/ADHD	54.7	68.5	76.94***	1.43***	67.0	79.7	1.0e3***	1.62***
Anger/irritability
Consistently angry/irritable	47.9	32.9	88.29***	1.46***	49.7	38.5	567.96***	1.29***
Never/occasionally angry/irritable	52.1	67.1	88.29***	1.46***	50.3	61.5	567.96***	1.29***
Empathy
No empathy to victims	29.0	21.3	30.39***	1.36***	28.8	21.4	336.63***	1.34***
Empathetic to victims	71.0	78.7	30.39***	1.36***	71.2	78.6	336.63***	1.34***
Impulsivity
High impulsivity	54.7	41.9	61.06***	1.30***	54.5	43.2	570.47***	1.26***
High self-control	45.3	58.1	61.06***	1.30***	45.5	56.8	570.47***	1.26***
Psychosis
Psychotic symptoms	9.8	6.5	13.86***	1.51***	6.5	4.5	95.25***	1.44***
No psychotic symptoms	90.2	93.5	13.86***	1.51***	93.5	95.5	95.25***	1.44***
% of sample	33.8	66.2			24.3	75.7

Note. Total JSOs n = 4,153; early onset n = 1,405; adolescent onset n = 2,748. Total JNSOs n = 60,176; early onset n = 14,613; adolescent onset n = 45,563. ORs shown for first level of binary variables. DLC = developmental and life course; JSO = juvenile sex offender; JNSO = juvenile nonsex offender; OR = odds ratio; ADD = attention-deficit disorder; ADHD = attention-deficit hyperactivity disorder.

p < .01. ***p < .001.

Next, multivariate binary logistic regressions are conducted to determine whether DLC risk factors significantly predict early versus adolescent criminal onset among JSOs and JNSOs, after controlling for all other theoretical and demographic covariates. As DLC theory emphasizes the cumulative impact of neuropsychological deficits (NDs) and familial adversity (FA) in increasing the risk of chronic, violent, and other types of severe offending (i.e., that the cumulative effect of experiencing familial adversity and neuropsychological deficits is the strongest risk factor for early onset for all offending types; see, for example, Moffitt, 1993), moderation analyses examining the interaction between ND and FA on the risk of early versus adolescent onset among the JSO and JNSOs are run for each analysis. Finally, a multinomial logistic regression is conducted to determine whether DLC risk factors significantly distinguish early onset JSOs and early onset JNSOs, when compared with juvenile offenders of any kind with adolescent onset. The interaction term of ND and FA is also included to evaluate the moderating effect of these cumulative risks on early onset JSO and JNSOs as compared with all forms of adolescent onset juvenile offenders.

Together, this analytic plan will allow for an assessment of whether, and to what extent, DLC theoretical predictions align with the observed risk factors for early and adolescent onset of offending among the two distinct groups of juvenile offenders.

Results

Multivariate Analysis: JSOs

As noted in the DLC literature, criminal behavior, family adversity, neuropsychological deficits, and childhood trauma tend not to occur in isolation. Therefore, prior to running multivariate regression models, an analysis of multicollinearity was first conducted. Diagnostic results show that although several variables tend to occur in tandem, the variance inflation factors (VIFs) for all covariates in the analyses were satisfactory (range = 1.05-1.71).

As the next step of the analytic process, a series of multivariate logistic regression models were estimated to determine the ability of the above noted DLC risk factors to predict early versus adolescent onset of offending, when controlling for all other covariates, for both juvenile sex and nonsex offenders (Table 2). The initial models (labeled Models 1 and 3 in Table 2) evaluate the theoretical and demographic risk factors’ ability to predict age of onset for the JSO and JNSO groups. A second set of models containing the summed ND and FA interaction term was added into the models for both JSO and JNSO groups (labeled Models 2 and 4 in Table 2), to perform a more precise test of the predictions made by DLC theory. Results of the multivariate binary logistic regression models distinguishing early versus adolescent onset among the JSO and JNSO youth are presented in Table 2 below. In each case, early onset is the predicted outcome, and adolescent onset is the reference category.

Table 2.

Multivariate Logistic Regression of DLC Risk Factors Predicting Early vs. Adolescent Onset Among JSOs and JNSOs.

	JSOs				JNSOs
	Model 1		Model 2		Model 3		Model 4
	OR	CI	OR	CI	OR	CI	OR	CI
Male	1.34	[0.99, 1.80]	1.34^†	[1.00, 1.80]	1.41***	[1.34, 1.50]	1.42***	[1.34, 1.50]
White	0.74***	[0.63, 0.86]	0.74***	[0.63, 0.87]	0.74***	[0.70, 0.77]	0.74***	[0.70, 0.77]
Family income	0.92	[0.84, 1.01]	0.92	[0.84, 1.01]	0.82***	[0.80, 0.85]	0.82***	[0.80, 0.85]
Household violence	1.12	[0.90, 1.38]	1.13	[0.91, 1.41]	1.17***	[1.09, 1.25]	1.15***	[1.08, 1.23]
Emotional neglect	1.24*	[1.02, 1.50]	1.24*	[1.02, 1.50]	0.94*	[0.89, 0.99]	0.94*	[0.89, 0.99]
Physical abuse	1.10	[0.92, 1.32]	1.10	[0.92, 1.32]	1.04	[0.99, 1.10]	1.05	[0.99, 1.10]
Sexual abuse	1.05	[0.87, 1.28]	1.05	[0.87, 1.28]	1.09*	[1.01, 1.17]	1.09*	[1.01, 1.17]
School involvement	1.10	[0.95, 1.29]	1.10	[0.95, 1.29]	1.03	[0.99, 1.08]	1.03	[0.99, 1.08]
Peers	1.18^†	[1.00, 1.40]	1.18*	[1.00, 1.40]	0.93**	[0.88, 0.97]	0.92**	[0.88, 0.97]
Poor parental supervision	1.04	[0.87, 1.26]	1.00	[0.82, 1.22]	1.20***	[1.14, 1.27]	1.23***	[1.16, 1.30]
Poor parental punishment	1.17	[0.97, 1.41]	1.12	[0.91, 1.37]	1.09**	[1.04, 1.15]	1.12***	[1.06, 1.19]
Parental incarceration	1.41***	[1.19, 1.66]	1.36**	[1.14, 1.63]	1.62***	[1.54, 1.70]	1.65***	[1.57, 1.74]
Parental mental illness	1.06	[0.84, 1.33]	0.99	[0.76, 1.28]	1.10**	[1.02, 1.18]	1.14**	[1.06, 1.24]
ADD/ADHD	1.52***	[1.30, 1.78]	1.45***	[1.21, 1.73]	1.70***	[1.62, 1.79]	1.76***	[1.66, 1.87]
Anger/irritability	1.28**	[1.09, 1.50]	1.21*	[1.01, 1.47]	1.20***	[1.15, 1.26]	1.24***	[1.17, 1.31]
Empathy	1.05	[0.89, 1.25]	0.99	[0.80, 1.22]	1.08**	[1.03, 1.14]	1.13***	[1.06, 1.20]
Impulsivity	1.02	[0.86, 1.21]	0.98	[0.81, 1.18]	1.14***	[1.08, 1.19]	1.17***	[1.11, 1.24]
Psychosis	1.12	[0.86, 1.46]	1.05	[0.79, 1.40]	1.10*	[1.01, 1.20]	1.14**	[1.04, 1.26]
ND × FA interaction			1.03	[0.98, 1.03]			0.98*	[0.97, 1.00]

Note. DLC = developmental and life course; JSO = juvenile sex offender; JNSO = juvenile nonsex offender; OR = odds ratio; CI = confidence interval; ADD = attention-deficit disorder; ADHD = attention-deficit hyperactivity disorder; ND = neuropsychological deficit; FA = familial adversity; DV = dependent variable; reference = adolescent onset.

†

p < .055. *p < .05. **p < .01. ***p < .001.

Four DLC risk factors emerged as statistically significant predictors of an early onset of juvenile sex offending in Model 1, after holding all other theoretical and demographic variables constant: emotional neglect, parental incarceration, ADD/ADHD, and anger/irritability. JSOs who experienced emotional neglect were 24% more likely to have early versus adolescent onset (OR = 1.24, p < .05), having a parent incarcerated increased the odds of early onset among JSOs by 41% (OR = 1.41, p < .001), a history of ADD/ADHD increased the odds of early onset by 52% (OR = 1.52, p < .001), and anger/irritability increased the odds of early onset by 28% (OR = 1.28, p < .01). Admiration/emulation of deviant peers bordered on statistical significance as a risk factor predicting early onset for JSOs (OR = 1.18, p < .055). Each of these risk factors, except admiration of deviant peers, is a risk factor for early onset JSOs according to DLC theory. Race/ethnicity was also a significant predictor of an early onset sex offending as a demographic control measure in the model. Surprisingly, gender was not significant.

In Model 2, which includes the interaction term evaluating the cumulative effect of neuropsychological deficits and familial adversity, results are largely similar to Model 1. Emotional neglect (OR = 1.24, p < .05), parental incarceration (OR = 1.36, p < .01), ADD/ADHD (OR = 1.45, p < .001), history of anger/irritability (OR = 1.21, p < .05), and admiration/emulation of deviant peers (OR = 1.18, p < .05) were statistically significant predictors of an early criminal onset among the JSOs. Although the effect sizes differed slightly in some cases, none changed in direction or lost significance. However, the interaction term for neuropsychological deficits and family adversity was not a significant predictor of age of onset among the JSOs.

Multivariate Analysis: JNSOs

Model 3 of Table 2 presents the results of the multivariate binary logistic regression predicting age of onset among JNSOs. In total, 16 risk factors emerged as significant predictors for early versus adolescent onset among the JNSOs, after controlling for all other theoretical and demographic variables. These risk factors, nearly all of which presented in the theoretically expected direction, indicate that the number of DLC risk factors increasing risk of early onset among JNSOs exceeds the number for JSOs.

Specifically, childhood sexual abuse increased the odds of early onset by 9% among the JNSOs (OR = 1.09, p < .05), whereas witnessing household violence raised the risk of early onset by 17% (OR = 1.17, p < .001). Interestingly, emotional neglect slightly reduced the risk of early onset among the JNSOs (OR = 0.94, p < .05), a finding contrary to theoretical predictions.

All four familial adversity items significantly predicted early onset among JNSOs, as sporadic/inadequate supervision increased the odds by 20% (OR = 1.20, p < .001), severe/insufficient punishments increased the odds by 9% (OR = 1.09, p < .01), and having a parent with a history of incarceration and mental illness increased the odds of early onset by 62% (OR = 1.62, p < .001) and 10% (OR = 1.10, p < .01), respectively.

Neuropsychological deficits all increased the risk of early onset among JNSOs, in line with DLC predictions. ADD/ADHD increased risk of early onset among the JNSOs by 70% (OR = 1.70, p < .001), whereas anger/irritability raised the odds by 20% (OR = 1.20, p < .001). Having low empathy increased the risk of early onset by 8% (OR = 1.08, p < .01), whereas having high impulsivity increased the odds of an early onset by 14% (OR = 1.14, p < .001). JNSOs with psychosis were 10% more likely to have an early onset of offending (OR = 1.10, p < .05).

Admiration and emulation of antisocial peers was shown to reduce the risk of early onset (in other words, increase the risk of adolescent onset) by 7% among the JNSOs, when controlling for all other factors (OR = 0.93, p < .01). This finding is directly aligned with DLC theoretical expectations. School involvement was not a statistically significant predictor of age of onset among the nonsexual offenders. Gender and race/ethnicity were both significant predictors of early onset sex offending as demographic control measures in the model.

A moderation analysis of the cumulative effects of neuropsychological deficits and familial adversity on early onset among nonsex offenders is presented in Model 4. All risk factors distinguishing early onset among the JNSOs remained significant in the model, and changed only slightly, if at all, in terms of their effect size. Interestingly, the interaction term was shown to have a small effect as a significant predictor of adolescent (not early) onset offending among the nonsexual offender group (OR = 0.98, p < .05). This finding, which is theoretically unexpected, is elaborated on in further detail in the “Discussion” section, to follow.

Multinomial Analysis: Early Onset JSOs

Finally, a series of multinomial logistic regression models were estimated to determine the ability of the above noted DLC risk factors to predict early onset offending for both JSOs and JNSOs, when compared with all adolescent onset juvenile offenders. The initial models (labeled Model 1 and 3, respectively) evaluate the risk factors’ ability to predict early onset for the JSO and JNSO groups. Once again, a second set of models containing an interaction term for the cumulative risk of neuropsychological deficits and familial adversity was estimated for both JSO and JNSO groups (labeled Model 2 and 4, respectively). Results of the multinomial regression models of risk factors distinguishing age of onset among the JSO and JNSO youth are presented in Table 3. In each case, the ability for risk factors to predict early onset for the JSO and JNSO youth is compared with all offenders with adolescent onset (the reference category).

Table 3.

Multinomial Logistic Regression of DLC Risk Factors Predicting Early vs. Adolescent Onset Among JSOs and JNSOs.

	JSOs				JNSOs
	Model 1		Model 2		Model 3		Model 4
	OR	CI	OR	CI	OR	CI	OR	CI
Male	6.53***	[5.15, 8.27]	6.51***	[5.13, 8.25]	1.31***	[1.24, 1.38]	1.31***	[1.24, 1.39]
White	0.71***	[0.62, 0.80]	0.71***	[0.63, 0.81]	0.74***	[0.70, 0.77]	0.74***	[0.70, 0.77]
Family income	0.87***	[0.81, 0.94]	0.87***	[0.81, 0.94]	0.83***	[0.80, 0.85]	0.83***	[0.80, 0.85]
Household violence	0.99	[0.82, 1.19]	1.02	[0.84, 1.23]	1.17***	[1.10, 1.25]	1.16***	[1.09, 1.24]
Emotional neglect	1.09	[0.94, 1.26]	1.08	[0.94, 1.26]	0.95*	[0.90, 1.00]	0.95*	[0.90, 1.00]
Physical abuse	1.16*	[1.00, 1.33]	1.15^†	[1.00, 1.33]	1.04	[0.99, 1.10]	1.04	[0.99, 1.10]
Sexual abuse	3.15***	[2.69, 3.70]	3.17***	[2.70, 3.72]	0.99	[0.92, 1.07]	0.99	[0.92, 1.06]
School involvement	0.96	[0.85, 1.08]	0.96	[0.85, 1.08]	1.04	[1.00, 1.09]	1.04	[1.00, 1.09]
Peers	0.64***	[0.56, 0.74]	0.64***	[0.56, 0.74]	0.97	[0.92, 1.02]	0.97	[0.92, 1.01]
Poor parental supervision	1.01	[0.87, 1.17]	0.95	[0.80, 1.11]	1.21***	[1.14, 1.27]	1.23***	[1.16, 1.30]
Poor parental punishment	1.02	[0.88, 1.18]	0.94	[0.80, 1.12]	1.11***	[1.05, 1.17]	1.13***	[1.07, 1.20]
Parental incarceration	1.49***	[1.30, 1.71]	1.41***	[1.22, 1.64]	1.61***	[1.54, 1.70]	1.65***	[1.56, 1.74]
Parental mental illness	1.16	[0.97, 1.39]	1.04	[0.84, 1.28]	1.09**	[1.02, 1.17]	1.14**	[1.05, 1.23]
ADD/ADHD	2.37***	[2.10, 2.69]	2.20***	[1.90, 2.55]	1.65***	[1.57, 1.73]	1.70***	[1.61, 1.80]
Anger/irritability	0.99	[0.87, 1.13]	0.92	[0.79, 1.07]	1.22***	[1.17, 1.28]	1.26***	[1.19, 1.33]
Empathy	1.20*	[1.04, 1.38]	1.09	[0.91, 1.29]	1.07**	[1.02, 1.13]	1.12**	[1.05, 1.19]
Impulsivity	1.20**	[1.05, 1.38]	1.12	[0.96, 1.31]	1.12***	[1.07, 1.18]	1.16***	[1.09, 1.22]
Psychosis	1.49***	[1.22, 1.82]	1.36**	[1.08, 1.69]	1.07	[0.98, 1.17]	1.12*	[1.02, 1.23]
ND × FA interaction			1.04^†	[1.00, 1.08]			0.99^†	[0.97, 1.00]

†

p < .055. *p < .05. **p < .01. ***p < .001.

As shown in Model 1 of Table 3, JSOs with early onset were distinguished by seven DLC risk factors: physical abuse, sexual abuse, parental incarceration, having ADD/ADHD, low empathy, high impulsivity, and psychosis. Specifically, physical abuse (OR = 1.16, p < .05) and sexual abuse (OR = 3.15, p < .001) in childhood increased the odds of early onset sex offending by 16% and 215%, respectively, as compared with youth offenders with adolescent onset. Among the familial adversity measures, having a parent with a history of incarceration increased the odds of early onset by 49% (OR = 1.49, p < .001). With respect to neuropsychological deficits, a history of ADD/ADHD (OR = 2.37, p < .001), low empathy (OR = 1.20, p < .05), high impulsivity (OR = 1.20, p < .01), and psychosis (OR = 1.49, p < .001) all increased the odds of early onset for JSOs. Aligned with DLC predictions, admiration/emulation of antisocial peers reduced the risk of early onset among JSOs by 36% when controlling for all other factors (OR = 0.64, p < .001).

To determine whether, and to what extent, a combination of neuropsychological deficits and familial adversity measures were predictive of an early onset among JSOs, an interaction term was added to Model 2. Except for low empathy and high impulsivity, all risk factors remained statistically significant predictors of early onset sex offending. Furthermore, the interaction term was borderline statistically significant in the theoretically predicted direction (OR = 1.04, p < .055).

Multinomial Analysis: Early Onset JNSOs

As shown in Model 3 of Table 3, JNSOs with early onset were distinguished from youth with adolescent onset of offending by the following DLC risk factors: witnessing household violence, sporadic/inadequate parental supervision, severe/insufficient parental punishment, parental incarceration, parental mental illness, having a history of ADD/ADHD, high anger/irritability, low empathy, and high impulsivity. Results indicate that witnessing household violence increased the risk of being an early onset JNSO by 17%, when compared with any type of adolescent onset juvenile offender (OR = 1.17, p < .001). All four of the familial adversity items significantly predicted early onset, as sporadic/inadequate supervision increased the odds by 21% (OR = 1.21, p < .001), severe/insufficient punishments increased the odds by 11% (OR = 1.11, p < .001), and having a parent with a history of incarceration and mental illness increased the odds of early onset by 61% (OR = 1.61, p < .001) and 9% (OR = 1.09, p < .01), respectively.

Furthermore, having a history of ADD/ADHD increased the odds of early onset nonsex offending by 65% (OR = 1.65, p < .001), whereas high anger/irritability increased the odds by 22% (OR = 1.22, p < .001). In addition, having low empathy and high impulsivity increased the odds of early onset by 7% (OR = 1.07, p < .01) and 12% (OR = 1.12, p < .001), respectively.

Contrary to theoretical predictions, emotional neglect slightly reduced the risk of early onset among the JNSOs (OR = 0.95, p < .05). In addition, neither school involvement³ nor admiration/emulation of antisocial peers were significant predictors in this model.

The interaction of neuropsychological deficits and familial adversity measures was included as a predictor of early onset offending among JNSOs in Model 4. Although the interaction term bordered on statistical significance (OR = 0.99, p < .055), all risk factors (now including psychosis) remained statistically significant predictors of early onset nonsexual offending.

Discussion

This study aimed to examine the applicability of DLC theory-informed risk factors, including neuropsychological deficits, family adversity, structural deficits, lack of school involvement, and deviant peers, in differentiating early versus adolescent onset among JSOs and JNSOs. After conducting a series of bivariate analyses and multivariate regression analyses (logistic and multinomial logistic), several important findings emerged.

First, although DLC risk factors were found to distinguish early onset from adolescent onset of offending for both JSOs and JNSOs, as theoretically predicted, it is clear that the number of risk factors predicting early onset among the nonsex offenders far exceeded those for the sex offenders (refer to Table 2). This finding is very interesting, as it supports DLC theoretical predictions that risk factors for early onset differ in kind from risk factors for adolescent onset; however, JNSOs with an early onset seemed to be disproportionately characterized by nearly all DLC risk factors, whereas early onset was less distinguishable among the JSOs. In other words, the early onset JNSOs seemed to simply be characterized by a greater degree of risk, but JSOs might be characterized by a specific kind of risk.⁴

Specifically, JSOs with early versus adolescent onset showed more issues relating to familial adversity/maltreatment (i.e., emotional neglect, parental incarceration) and neuropsychological deficits (i.e., ADD/ADHD, anger/irritability), whereas JNSOs with early onset were distinguished from their counterparts with adolescent onset by every single DLC risk factor with the exception of physical abuse and school involvement.

These findings align with previous research on JSOs, which indicate that in general, psychopathologies and childhood trauma/family adversity are primary significant risk factors for juvenile sex offending (Fox, 2017; Hall & Hirschman, 1991, 1992; Marshall & Barbaree, 1990; Seto & Lalumière, 2010; Ward & Beech, 2006). The present study also builds on past research by indicating that even among JSOs, those with psychopathologies and childhood trauma/family adversity are at greater risk of early onset of offending.

Furthermore, when comparing the early onset JSOs to youth with any form of adolescent onset offending, sexual abuse was very strong and significant as a predictor, increasing the risk of early onset sexual offending by more than 200%. This echoes findings from prior research, which consistently shows that sexual offending, particularly with an earlier age of onset, is predicted by childhood sexual abuse (Carpentier et al., 2011; Fox, 2017; Hanson & Bussiere, 1998; Hanson & Morton-Bourgon, 2005; Seto & Lalumière, 2010).

Results of this study also differ from DLC theoretical predictions and prior research in a few ways. For instance, peer influence was a significant or bordering significant predictor of early, not adolescent, onset among JSOs in several models. This contradicts a major DLC prediction that admiration and association with deviant peers would increase risk of adolescent onset (Moffitt, 1993). However, it should be noted that in the multinomial logistic regression comparing early onset JSOs with juvenile offenders with adolescent onset, peer influence significantly increased the risk of adolescent onset, as theoretically expected.

In addition, in the initial logistic regression models, sexual abuse served as a risk factor for early onset (which is expected) among the JNSOs (which is not expected). This finding raises an interesting question, as to whether sexual abuse was more strongly predictive of early onset, or for nonsex offending, as the results may be affected by the fact that adolescent onset nonsex offenders are far less likely to have been sexually abused in childhood compared with early onset JNSOs (and adolescent onset JSOs). In the follow-up multinomial analysis, results fall more in line with DLC theory and prior research, as childhood sexual abuse was only predictive of early onset JSOs when compared with adolescent onset offenders. In other words, when able to compare the ability of sexual abuse to distinguish between early onset JSOs and adolescent onset offenders, sexual abuse significantly and substantially increased the risk of sex offending at an earlier age, as seen in prior research (Fox, 2017; Seto & Lalumière, 2010).

Another surprising departure was the general lack of significance for the interactive effects of familial adversity and neuropsychological deficits, which was expected to significantly increase the risk of early onset offending. Only in the multinomial logistic regression, where the interaction was bordering on significance (p < .055), did the effects present in the expected direction by increasing risk of early onset among the sex offenders. In all other models, the results were either nonsignificant, bordering significance, or operated in the opposite direction. Future research should aim to examine the cumulative effects of familial adversity and neuropsychological deficits, particularly as they appear to be strongly related to early onset and juvenile sex offending outside of the moderation analyses.

Finally, although the current study did not aim to compare risk factors for sexual versus nonsexual offenders generally (instead, focusing on risk factors for varied ages of onset for each group), the multinomial analysis was able to shed some light on the distinctions between these groups. For instance, early onset JSOs were distinct from adolescent onset juvenile offenders as they showed a significantly increased risk of sexual abuse, physical abuse, low empathy, impulsivity, psychosis, ADD/ADHD, and parental incarceration. Each of these risk factors, which fall primarily in the neuropsychological deficit and childhood maltreatment categories, are in alignment both with prior research and DLC theoretical predictions.

Importantly, nonsex offending youth with early onset were distinguished from adolescent onset offenders by a similar (but larger) set of significant risk factors, to include low empathy, impulsivity, ADD/ADHD, anger/irritability, parental incarceration, low parental supervision, harsh punishment, parental mental illness, and witnessing household violence. The risk factors unique to the early onset nonsex offending youth appear to fall more in the realm of familial adversity, potentially illustrating a unique distinction for early onset nonsex offenders that is worth additional investigation in future research.

Limitations and Future Research

Of course, it is important to note the limitations of the current study. For instance, the FDJJ includes both JSOs and JNSOs; however, JNSOs encompassed a variety of offenders, including violent offenders. Future research should separate nonsex offense categories to better understand risk factors predictive of violent, sexual, and nonsexual offending during early and adolescent onset. Also, due to the nature of the sample, researchers should exercise caution when generalizing these findings. These findings provide insight into the risk factors predictive of early onset sex offending for sex-only offenders and are not reflective of risk factors predictive of the versatile (i.e., “sex-plus”) offenders. In addition, although the PACT risk assessment is verified by, among other legitimate sources, official court records and Department of Children and Families (DCF) reports, there is a chance that some forms of neglect or abuse were never reported. Besides possible underreporting issues, the data account for only the existence of abuse/neglect and not the frequency or dates of the events. Future research should employ longitudinal data to determine causality, specifically focusing on the temporal order between sexual abuse, psychopathologies, and onset of sex offending.

Conclusion

DLC theories, in general, predict that certain developmental, social, environmental, psychological, and biological factors increase the risk of offending, but may not apply equally to all individuals (Farrington, 2003; Loeber & LeBlanc, 1990). For instance, for some people, DLC theory would expect that high levels of neuropsychological deficits and familial adversity would increase the risk of early age of onset and chronic future offending, whereas antisocial peer influence serves as a substantial risk factor for adolescent onset and limited offending behaviors for others (Moffitt, 1993). This study aimed to examine whether DLC-informed risk factors were able to distinguish early onset from adolescent onset among juvenile sex and nonsex offenders, thereby indicating the applicability of DLC theory to the understanding of onset among both offender groups (see Farrington, 1989, 1991, 1998; Lussier, 2017).

Overall, results of this study indicate that the risk factors for early onset differ in kind from risk factors for adolescent onset, and generally align with predictions made by DLC theory. However, a unique finding and contribution of this study is that the number of DLC risk factors predicting early onset for nonsex offenders was much higher than for the sex offenders. In other words, the early onset JNSOs appear to be disproportionately characterized by nearly all DLC risk factors, indicating a greater degree of risk, whereas early onset JSOs were characterized by a more narrow and specific kind of risk, primarily relating to neuropsychological deficits and trauma/abuse in childhood.

Consequently, these findings indicate both the need for further research on JSOs from a DLC perspective, as well as a need for early life-course prevention, risk assessment, and intervention efforts (Wijetunga, Martinez, Rosenfeld, & Cruise, 2018). Early family parent training, nurse–practitioner partnerships, and self-control improvement programs are examples of specific programs that are both effective in the short and long term (Piquero, Farrington, Welsh, Tremblay, & Jennings, 2009; Piquero, Farrington, Diamond, et al., 2016; Piquero, Jennings, & Farrington, 2010; Piquero, Jennings, Farrington, Diamond, & Reingle Gonzalez, 2016) and yield a considerable cost benefit particularly in terms of preventing the onset of a young person’s criminal career (Cohen, Piquero, & Jennings, 2010).

Footnotes

Authors’ Note

We would like to thank the reviewers, Action Editor Dr. Eric Beauregard, and Editor-in-Chief Dr. Michael Seto for their invaluable feedback on the prior version of this article. Special issue Guest Editor Dr. Wesley Jennings had no involvement in the editorial process for this manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

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