Parity or Disparity? Outcomes of Court-Involved Youth With and Without Disabilities

Abstract

Youth with disabilities are overrepresented in the juvenile justice system, but few studies have investigated the mechanisms by which this occurs. In this study, we considered how juvenile court adjudication and length of commitment in secure facilities contributed to disproportionality in court involvement and detention, addressing an important gap in the intersection of disability and juvenile justice literature. Using linked educational and juvenile justice records of 41,812 youth, we sought to ascertain whether, among juvenile offenders, youth with disabilities had higher likelihood of adjudication as delinquent or placement in secure facilities for longer periods of time compared to youth without disabilities. Results indicated that youth with and without disabilities were adjudicated and placed similarly, suggesting that disparities contributing to overrepresentation of youth with disabilities in detained populations may manifest earlier in youths’ involvement in the justice system. We conclude with implications for research, policy, and practice.

Keywords

special education disabilities disproportionality juvenile court juvenile justice adjudication sentencing

The juvenile justice court system handles more than 885,000 delinquency court cases each year (Hockenberry & Puzzanchera, 2018), and although the system’s mission is for contact to be rare, fair, and beneficial (U.S. Department of Justice [DOJ], 2015), disproportionate involvement of traditionally marginalized groups is an enduring problem (National Council on Disability [NCD], 2003) and public health concern (Society for Adolescent Health and Medicine [SAHM], 2016). Indeed, the United States has the highest rates of juvenile justice involvement and detention of developed nations (Aizer & Doyle, 2015; Barnert, Perry, & Morris, 2016). Youth with disabilities present a particular challenge as they may comprise upward of 85% of youth in juvenile detention, leading to calls to better understand this disproportionality and disrupt this school-to-prison pipeline (NCD, 2015). However, due to limited data on these youths’ involvement at different points of contact (e.g., arrest, referral, adjudication, disposition, detention; see Kincaid & Sullivan, 2019, for discussion) and variability in determination of disability status (e.g., school records, clinical evaluation, self-report, parent report), we know relatively little about the experiences and outcomes of students with disabilities as they move through the system. Better understanding the nature of their involvement in the juvenile justice system is an important precursor to identifying targeted, malleable policy or practice levers to reduce overrepresentation and improve students’ outcomes. As such, the present study examined the outcomes of court involvement, particularly adjudication as delinquent and placement in a juvenile justice facility (hereafter referred to as placement), among youth with disabilities relative to their peers without disabilities.

Juvenile Justice Outcomes and Disproportionality

The school-to-prison pipeline and disproportionality in juvenile justice involvement are concerning because involvement—particularly placement—is associated with limited educational achievement and attainment (Aizer & Doyle, 2015; Hjalmarsson, 2008; Leone, Meisel, & Drakeford, 2002), lower adult earnings (Kerley, Benson, Lee, & Cullen, 2004), increased risk of gang involvement, future delinquency, and recidivism (e.g., Aizer & Doyle, 2015; Bernburg, Krohn, & Rivera, 2006; Mendel, 2011), and lower health and social outcomes across the lifespan (Barnert et al., 2016). Placement in correctional or detention facilities exacerbates these risks and increases youths’ risk for experiencing maltreatment and sexual abuse (Mendel, 2011). Generally, stakeholder intends detention to provide both punishment and rehabilitation to deter future delinquent behavior, but research indicates it does not and poses multiple risks to youths’ behavioral, mental, and physical health (Lambie & Randell, 2013; SAHM, 2016). Unfortunately, individuals who are racial/ethnic minority, of lower socioeconomic status (SES), or identified with disabilities are at increased risk of involvement, compounding, and perpetuating social and health disparities for these groups (Barnert et al., 2016; SAHM, 2016; Swayze & Buskovick, 2014).

Disparities in juvenile justice involvement can arise at several points, from arrest through placement and beyond, as contact and state actors’ decisions about how to treat youth occur at multiple points throughout. Given persistent findings of racial disproportionality in juvenile justice involvement, including harsher penalties for minority youth than similarly charged White youth with comparable histories (for discussion, see Coalition for Juvenile Justice, 2010), the Juvenile Justice and Delinquency Prevention Act ([JJDPA]; 42 U.S.C. 5601, et seq.) was amended in 2002 to require states to measure racial/ethnic disproportionate minority contact (DMC) at nine contact/decision points. These included (a) arrest by the police, (b) referral to court, (c) diversion by county attorneys out of the courts to alternative rehabilitation programming, (d) secure detention prior to court, (e) referral to juvenile court—that is, charges filed, (f) adjudication as delinquent, (g) probation, (h) placement in secure correctional facilities, and (i) transfer to the criminal/adult courts. An individual may exit the system at each point of contact or proceed through multiple points. Research indicates that racial disproportionality occurs at each point of contact, with national data demonstrating that racial minority youth have elevated risk for most outcomes, with the exception of diversion, relative to their White peers (Hockenberry & Puzzanchera, 2018). The JJDPA was reauthorized in December 2018 as the Juvenile Justice Reform Act of 2018 (JJRA; 2018), requiring that states track the aforementioned nine contact points for both racial and ethnic disproportionality, determine which points have disproportionate outcomes for racially and ethnically diverse youth, and establish a plan to address disproportionate outcomes. In addition, the 2018 reauthorization requires states reporting annually on involvement of youth with disabilities to be included in an annual report to Congress (§207. 34 U.S.C. 11117).

Overrepresentation of Youth With Disabilities

As with racial/ethnic minority youth, disparities for youth with disabilities can occur at any contact/decision point in the juvenile justice system. Scholars have long considered this an important dimension of the school-to-prison pipeline and an artifact of inappropriate school supports for students with disabilities (for example, see Osher, Woodruff, & Sims, 2002). The intersections of special education law—that is, the Individuals with Disabilities Education Act (IDEA)—and juvenile justice have been the impetus for many class action suits on behalf of students with disabilities (Katsiyannis, Barrett, & Zhang, 2012), including at least one high-profile case that proceeded through the U.S. Courts of Appeals (Morgan v. Chris L., for review, see Rivkin, 2009). Such cases have prompted some local reform, but disparities and relations to effective implementation of IDEA remain problematic (for discussion, see Katsiyannis et al., 2012). Data submitted as part of states’ annual reporting requirements under IDEA indicate that 0.4% of students with disabilities are served in correctional facilities (Katsiyannis et al., 2012), but this estimate likely underrepresents placement of youth with disabilities given the nature of such reporting by schools and facilities, poor interagency coordination of students’ records and services, relatively short average sentencing for youth relative to IDEA’s reporting requirements, and issues with identification of special needs before and during such placement.

Studies attempting to ascertain the prevalence of youth with disabilities involved in the juvenile justice system typically captured disability identification within a single juvenile facility or through survey methods of facility or state department administrators. Using these methods, authors generally agree that youth with disabilities are overrepresented in correctional facilities (Bullis & Yovanoff, 2005; Cheely et al., 2012; Krezmien, Mulcahy, & Leone, 2008; Quinn, Rutherford, Leone, Osher, & Poirer, 2005). For instance, a survey of state directors of juvenile facilities indicated that around 33.4% of youth had an identified disability and were receiving services (Quinn et al., 2005), while other reports noted youth with disabilities comprised 45% (Krezmien et al., 2008), 58% (Bullis & Yovanoff, 2005), and even 85% of samples (NCD, 2015). In one state, a 2013 survey of youth comparing those in correctional facilities with a matched sample of mainstream youth indicated that 51% of placed youth had an Individualized Education Plan (IEP), compared with 17% of nonplaced youth (Swayze & Buskovick, 2014), but did not include information on disabilities types or needs. Elsewhere, findings suggest that in some states and for certain disabilities, such as autism, youth with disabilities had lower risk of placement but received longer sentences when placed (Cheely et al., 2012; D. Zhang, Barrett, Katsiyannis, & Yoon, 2011). In addition, detained youth with disabilities are unlikely to receive appropriate instructional services or behavioral supports despite their legal entitlements to educational services (Geib, Chapman, D’Amaddio, & Grigorenko, 2011; Kvarfordt, Purcell, & Shannon, 2005; Morris & Thompson, 2008), thus undermining their educational and social outcomes.

Theories on the overrepresentation of youth with disabilities in the juvenile justice system proffer three overarching explanations for these disparities, namely, differential susceptibility to delinquent behavior and subsequent involvement in juvenile justice due to (a) a cascade of disengagement and delinquency following youths’ school failure, (b) cognitive and interpersonal differences related to youths’ disabilities, or (c) differential treatment of individuals with disabilities by agents of the juvenile justice system (Keilitz & Dunivant, 1986; NCD, 2003; Quinn et al., 2005). These theories have been tested infrequently in research, although related correlational studies abound (e.g., Pyle, Flower, Fall, & Williams, 2016), and results are equivocal, but early support coalesced around the notion of differential treatment (Brier, 1989; Keilitz & Dunivant, 1986; NCD, 2003). Where the disproportionate representation of youth with disabilities among detained youth is concerned, the most proximal points of contact that may contribute to the observed overrepresentation are court referral and adjudication.

Juvenile Court Referrals

Court petitions rely on the judgment by county attorneys on the appropriateness of addressing youths’ offenses through diversion programs or the courts. Petition to court indicates that the county attorney selected a formal approach to processing a youth’s alleged offense and carries a higher likelihood that the youth will have a juvenile court record. Unlike race/ethnicity, states have not systematically collected or reported data on youths’ disability status; this will change under the JJRA of 2018. At present, the limited available research indicates that youth with disabilities are overrepresented in the juvenile courts compared to their peers without disabilities, but estimates vary by geographic region, method for determining disability status, and covariate adjustment (Kincaid & Sullivan, 2019; D. Zhang et al., 2011). When educational records and juvenile court records have been linked for first-time offenders, students with disabilities are 1.39 (Kincaid & Sullivan, 2019) to 1.93 times (Aizer & Doyle, 2015) more likely to appear in court than students without disabilities, but this disproportionality does not appear to be robust to sociodemographic controls for sex, race/ethnicity, or SES (Kincaid & Sullivan, 2019). When disability categories are disaggregated, however, the attenuation of overrepresentation of youth with disabilities is explained by the heterogeneity across special education categories, with students categorized as having an emotional or behavioral disorders (EBD) or other health impairment (OHI) were overrepresented, while other disability categories were underrepresented (Kincaid & Sullivan, 2019). Elsewhere, considering all offenses, county attorneys referred youth with disabilities to court twice as often as youth without disabilities (D. Zhang et al., 2011). These patterns suggest that youth with disabilities may be overrepresented in placement in facilities due to higher rates of court referral.

Juvenile Adjudication and Placement

Adjudication as delinquent in the juvenile court system involves the court entering an official finding of guilt (Hockenberry & Puzzanchera, 2018). Annually, approximately 53% of cases petitioned to juvenile court result in delinquency adjudications and, like petition to the courts, rates vary by race/ethnicity, location, and type of offense (Hockenberry & Puzzanchera, 2018). In general, youth who commit crimes that involve public order offenses (56%) are the most likely to be adjudicated delinquent, followed by property offenses (53%), crimes against persons (52%), and drug law violations (50%). Of youth who receive a delinquency adjudication, most are assigned probation (63%), followed by placement (26%) or other sanction (11%).

Research on adjudication and placement for youth with disabilities is equivocal and sparse. Two studies, both from South Carolina but applying different methods for disability identification, found both a higher and lower likelihood of adjudication for youth with disabilities compared to youth without disabilities. The first study, examining recidivism rates (i.e., repeat offending) using data on all jurisdictions from the South Carolina Department of Juvenile Justice (SCDJJ) for 100,955 youth, of which 5,016 were identified as having a disability through intake caseworker identification, found that youth with disabilities received a higher number of delinquency adjudications (M = 2.35) than their peers without disabilities (M = 1.85), but were placed at the same rates, albeit for longer sentences (8 months on average vs. 6 months; D. Zhang et al., 2011). However, repeat offenders may have different risks of adjudication than first-time offenders given states’ rules and regulations around types of offenses, recommendations for petitioning to court, and suggestions for adjudication. In the second study, researchers investigated adjudication outcomes of youth with autism spectrum disorders (ASDs) identified from clinic and school records and linked with SCDJJ juvenile court records, matching each of the youth identified with ASD with three peers without ASD (Cheely et al., 2012). They found youth with ASD were at higher likelihood of diversion (odds ratio [OR] = 1.56) and lower likelihood of adjudication (OR = 0.56; Cheely et al., 2012). Thus, although this research suggests disproportionality may characterize youths’ risk of adjudication as delinquent, additional research on the experiences of adjudication as delinquent and placement among youth with disabilities compared to students without disabilities generally, and by disability types, may help elucidate potential disparities.

Present Study

The purpose of the present study was to investigate risk that youth with disabilities are at differential risk of adjudication and, if subsequently placed, longer sentences, compared to their peers without disabilities. This study added to the literature by linking educational and juvenile justice records to consider the relations of special education status and disability category to both adjudication and placement, thus providing a more nuanced description of the experiences of youth with disabilities within juvenile justice and allowing for identification of potential differences and disparities where past research has not. The following research questions guided this study:

Research Question 1: Are youth with disabilities more likely to receive a more restrictive adjudication than youth without disabilities?

Research Question 2: For youth adjudicated delinquent and committed to a detention facility, do youth with disabilities receive longer commitments than youth without disabilities?

Because national data on youth with disabilities’ involvement in juvenile justice are not available, this study leveraged linkable longitudinal records from one state’s departments of education and corrections to investigate the outcomes related to disability status.

Method

Data Sources and Procedures

Data for this study came from two sources: the Minnesota Department of Education’s Minnesota Automated Reporting Student System (MARSS) and the Minnesota Court Information System State Court Administrator’s Office (SCAO) for all youth in the state for 2008 to 2012. Data for this study were provided by the cross-system data project, Minnesota Linking Information for Kids (MinnLInK), funded by a grant from the National Science Foundation. Both MinnLInK and the authors’ university institutional review board approved the present study.

The state used MARSS to gather statewide student-level data on student achievement, special education records, and demographics. For this study, the state provided students’ data for the 2008–2009 to 2012–2013 academic years. MARSS allowed linking of the base sample from the 2008 to 2009 academic year—that is, 230,760 students in fifth to eighth grade—to data through 2012 to 2013. Students’ school district responsible for mandatory state testing, transcripts, federal accountability standards, and special education data reporting entered the data used here. Multiple schools provided information when youth attended multiple schools within a district or multiple districts, concurrently, or enrolled in state-approved alternative or online learning programs. SCAO provided data for the years 2008 to 2012 for 43,418 youth involved in the courts at any point.

We used Link Plus, a probabilistic matching program (Centers for Disease Control and Prevention, 2015), to match cases across data sources. Following initial matching by Link Plus, MinnLInK staff hand-matched all cases with a probabilistic match greater than 10% according to first name, middle initial, surname, and birthdate. We considered cases to be a “true” match if their first name, middle initial, surname, and birthdate were all matches; their first name, surname, and birthdate matched, but their middle name was misspelled, missing, included only an initial, or an initial for a different middle name; their middle initial, surname, and birthdate matched, but their first name had mismatched spelling or omitted letters; or their first name, middle initial, and birthdate matched, but their surname had mismatched spelling or omitted letters. We considered any other variations nonmatches. The overall matching rate between systems was 99.4%.

Analytic Sample

The analytic sample included all students referred to court who had complete data on the study variables extracted from MARSS and SCAO. Youth who had an unknown adjudication (e.g., pending or open cases) or who were in court for extradition-related cases (i.e., crimes in another state) were excluded, resulting in an analytic sample of 41,812 students. See Table 1 for descriptive characteristics of the full and analytic samples. (Note: Although we report race in the description of the sample in Table 1, it is not included in the regression analyses described below due to low cell sizes precluding reliable estimation.)

Table 1.

Demographic Characteristics of Full Sample and Court-Involved Analytic Sample.

Variable	Full sample (N = 230,760)		Analytic sample, court-involved (N = 41,812)
Variable	n	%	n	%
Male	118,201	0.51	26,766	0.64
Race/ethnicity
White	178,885	0.82	28,293	0.70
Black	20,025	0.09	6,895	0.17
Hispanic	13,254	0.06	3,221	0.08
Native American	5,042	0.02	2,057	0.05
Asian/Pacific Islander	13,554	0.06	1,346	0.03
Free/reduced priced lunch	98,676	0.43	26,604	0.64
Identified with disability	43,771	0.19	10,251	0.25
Autism spectrum disorder	3,904	0.05	329	0.02
Emotional or behavioral disorder	6,520	0.08	3,136	0.16
Specific learning disability	12,100	0.15	3,125	0.16
Speech or language impairment	4,615	0.06	323	0.02
Physical/sensory disabilities	1,985	0.02	168	0.01
Other health impairment	7,470	0.09	2,153	0.11
Developmental cognitive disability	2,609	0.03	338	0.02
Section 504	4,568	0.06	678	0.03

Note. Percentages reflect the percent of youth within each group.

Measures

Disability status

We derived disability status from MARSS data indicating the student qualified for or received services under an IEP or Section 504 plan. Cases coded as not having disabilities included those students who never evaluated or qualified for services, whose parent refused eligibility or services, or who received only early intervening services. For students involved in the juvenile justice system, we took disability status from the academic year immediately prior to court involvement to isolate relations for disability to later identification given findings that juvenile justice contact is associated with later identification of special needs (e.g., Aizer & Doyle, 2015).

Primary disability

For students with disabilities serviced via an IEP, MARSS provided 13 primary disability categories under Minnesota Administrative Rules, Chapter 3525 defining special education eligibility criteria (https://www.revisor.mn.gov/rules/?id=3525). MARSS did not capture secondary categories of eligibility, making each disability category exclusive. These categories included developmental cognitive disability (DCD), specific learning disability (SLD), EBD, ASD, speech-language impairment (SLI), OHI, and a composite category, sensory/physical impairments, that combined the remaining low-incidence categories (physically impaired, deaf/hard of hearing, blind/visually impaired, deaf-blind, traumatic brain injury, and severely multiply impaired) for these analyses due to low frequencies that precluded inclusion individually. For the purposes of these analyses, students who had Section 504 plans were included as an additional category of disability. We used students without disabilities as the referent group in analyses.

Offense

Offenses reported in the SCAO data were coded using an adaptation of federal categories from the DOJ (2011): (a) crimes against persons that included offenses involving acts against other people such as an assault, robbery, and sexual offenses; (b) crimes against property such as trespassing or vandalism; (c) drug law violations such as use of controlled substances or driving under the influence of such substances; (d) crimes against public order, such as obstruction of justice, noise complaints, and offenses involving weapons; and (e) other offenses, which included petty offenses (e.g., underage smoking, outside past curfew) and minor traffic-related offenses referred to the courts. We used the other offenses category as the referent in analyses as it represents the least serious category of offenses. For youth with multiple offenses recorded by SCAO, we used the most egregious offense committed during their first court appearance in analyses to control for repeat offending and varying numbers of committed offenses.

Adjudication

SCAO recorded the following outcomes for each case: (a) dismissed or acquitted, (b) acquitted due to mental illness or deficiency, (c) adjudicated delinquent, or (d) wavier to the adult court system. We excluded cases with an unknown adjudication (n = 896), and those who were certified to stand trial in the adult court system (n = 6) from the analytic sample. For the purposes of this study, we coded adjudication outcomes dichotomously, dismissed/acquitted, or adjudicated delinquent, with adjudication as the outcome of interest and dismissal as the referent.

Length of placement

We operationalized the length of placement as the number of days a youth was committed to a detention facility.

Data Analysis

We used logistic and linear regression models to address the research questions. Prior to analyses, the data were assessed using cross-tabulations that included all possible predictors and the outcomes of interest to ensure adequate cell sizes for each analysis. To answer the first research question regarding potential differences in adjudication between students with and without disabilities, we fit four models (see Equation 1): Model 1 with only disability status as a predictor of adjudication; Model 2 with disability category as the only predictor; and Model 3 with disability category and offense type. Due to low cell sizes in cross-tabulations, we assessed these models separately:

{Adju}_{i} = α + β_{1} X_{1} + \dots β_{n} X_{n} + ε .

(1)

The outcome in Equation 1 represents the youth’s adjudication with dismissal as the referent outcome, α represents the average log odds of adjudication of delinquency contingent on the other predictors, β₁ through β_n represent additional covariates in subsequent models, and ε represents variance not otherwise captured by the model. Results for these analyses include the log odds and adjusted relative risk (RR) for effect size, calculated following the equation provided by J. Zhang and Yu (1998).

We used linear regression to answer Research Question 2 regarding potential differences in length of placement between adjudication for students with and without disabilities. Due to the low frequency of placement (N = 511), the linear regression models only evaluated the impact of disability status and the highest incidence disability categories (EBD, OHI, and SLD) on length of commitment (see Equation 2):

{Length}_{i} = α + β_{1} X_{1} + \dots β_{n} X_{n} + ε .

(2)

The outcome in Equation 2 is the number of days a youth was placed by a court judge, the intercept represents the average days youth without disabilities were placed, and β_n represents whether the youth had a disability or, in a subsequent model, the disability category. Following analysis, we assessed model fit using the Akaike information criterion (AIC).

Results

Among youth in the total sample, 18% were involved in the juvenile courts at any point during the 5 years captured in the study. Of those, 25% had a disability. Adjudication as delinquent occurred in 44.1% of court-involved cases. Rates of adjudication were 44.0% and 44.2%, respectively, for students with and without disabilities. Similarly, among those cases resulting in delinquency adjudication, 2% resulted in placement. Students with disabilities were 1.03 times as likely to be placed as students without disabilities and represented 25% of the placement population, compared with 19% of school enrollment in the state.

As shown in Table 2, students with disabilities received delinquency adjudications at rates consistent with peers when only disability status was considered in Model 1 (RR = 0.99, p > .05). Although disaggregating youth by disability category improved model fit, as indicated by comparison of the AIC for each model, likelihood of adjudication was similar to, or lower than, students without disabilities for all categories except EBD (RR = 1.06, p < .01) and SLI (RR = 1.17, p < .01). Incorporation of offense type further attenuated any differences in likelihood of adjudication by disability category relative to no disability for all except EBD (RR = 1.13, p < .001), SLI (RR = 1.20, p < .001), and SLD (RR = 1.05, p < .05). Offenses against persons were least likely to result in adjudication (RR = 0.52, p < .001).

Table 2.

Correlates of Adjudication as Delinquent Among Youth With and Without Disabilities.

	Model 1		Model 2		Model 3
Covariates	B (SE)	RR	B (SE)	RR	B (SE)	RR
Disability status	−0.01 (0.02)	0.99
Autism spectrum disorder			−0.37** (0.12)	0.80	−0.20 (0.12)	0.90
Developmental cognitive disability			−0.23* (0.11)	0.87	−0.08 (0.12)	0.96
Emotional or behavioral disorder			0.10** (0.04)	1.06	0.24^† (0.04)	1.12
Other health impairment			0.02 (0.05)	1.01	0.10* (0.05)	1.05
Physical/sensory disabilities			−0.04 (0.16)	0.98	−0.01 (0.16)	1.00
Specific learning disability			−0.08* (0.04)	0.95	−0.03 (0.04)	0.98
Speech or language impairment			0.30** (0.11)	1.17	0.39^† (0.12)	1.20
Section 504			−0.13 (0.08)	0.93	−0.12 (0.08)	0.94
Offense
Person					−1.03^† (0.04)	0.52
Property					−0.29^† (0.03)	0.85
Drug					−0.22^† (0.04)	0.89
Public order					−0.13^† (0.03)	0.93
Intercept	−0.23^† (0.01)		−0.23^† (0.01)		−0.08^† (0.01)
AIC	56,170		56,147		54,510
N	40,916		40,916		40,194

Note. RR ratio calculated using J. Zhang and Yu’s (1998) log odds to RR conversion. B = log odds; RR = relative risk; AIC = Akaike information criterion.

†

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

After adjudication, placement was an infrequent outcome, but averaged 65.67 days for all placed youth. Neither disability status nor disability category was significant predictors of length of placement (see Table 3).

Table 3.

Length of Time Youth With Disabilities Were Placed.

Covariates	B	SE	B	SE
Disability status	−1.19	7.82
Emotional or behavioral disorder			2.36	13.81
Other health impairment			13.55	17.53
Specific learning disability			−6.02	13.00
Intercept	65.67^†	3.92	65.67^†	4.00
R ²	.00		.00
N	511		479

Note. B = log odds.

†

p < .001.

Discussion

Previous research has indicated disparities in juvenile justice involvement of youth with and without disabilities, particularly overrepresentation of youth with disabilities in correctional facilities (Quinn et al., 2005). This study investigated whether there were disparities in adjudication and length of placement for students with disabilities relative to their peers without disabilities that may contribute to observed disproportionality. The present study leveraged linked educational records and court data, with findings indicating few differences in adjudication, except for students with high incidence disabilities, and no differences in length of placement for students with and without disabilities. Contrasting with earlier studies (e.g., Quinn et al., 2005), results did not show significantly elevated RR of placement for students with disabilities, although they did represent a greater share of the placed population than the school population. Where significant differences were found for students with EBD, SLD, and SLI, results indicated 5% to 20% increased risk of adjudication, the clinical significance of which is debatable given disagreement in the education and health literature on what constitutes problematic RR (e.g., Cummings, 2009; Sullivan & Artiles, 2011).

Given past research on higher adjudication rates of youth with disabilities over time (D. Zhang et al., 2011) and their overrepresentation in correctional facilities (Quinn et al., 2005), these results were surprising. These differences may be attributable to a variety of factors, including location, temporal changes, methods, or combinations thereof. For example, D. Zhang and colleagues (2011) used administrative records for South Carolina youth born from 1981 to 1988, whereas Quinn and colleagues (2005) surveyed correctional facility administrators for most states, asking them to report on 2000 IDEA census data. Thus, although present results do not indicate students with disabilities are more likely to be adjudicated during their first offense than their peers, the disparities that arise at the point of court referral (Kincaid & Sullivan, 2019) or their increased risk of recidivism (D. Zhang et al., 2011) may put them at a greater risk of elevated juvenile justice involvement and placement. Future research should consider how disparities may emerge across the various points of contact and how trends differ across localities. Research on other dimensions of disproportionality for students with disabilities within schools has highlighted the importance of place and local implementation of national and state policy to students’ outcomes (e.g., Skiba, Artiles, Kozleski, Losen, & Harry, 2016; Sullivan & Bal, 2013).

Likewise, the lack of differences in placement length may have varied from past findings due to differences in location or methods since D. Zhang and colleagues (2011) investigated disparities in South Carolina using disability status self-reported or assigned by juvenile justice caseworker, as opposed to the educational records used here. Elsewhere, placement averaged between 3 and 4 months, so the comparably low rates found here may reflect states’ or judges’ differences in norms in committing youth to secure facilities. In addition, here placement included only youth in county detention centers, not treatment centers or group homes, which may have different lengths of stay than the traditional detention centers. Nonetheless, given that placement typically exceeded 2 months, students likely missed substantial school time. Research on chronic absenteeism, as defined as being absent 10% or more of the school year (approximately 18–20 days), may provide insight into the effect of juvenile placement as absenteeism is associated with poor reading proficiency (Lamdin, 1996) and lower school completion (Balfanz & Byrnes, 2013), and may exacerbate risks associated with special needs, behavioral difficulties, and juvenile justice involvement, warranting attention in research and practice.

The present study calls into question the differential treatment hypothesis for court outcomes. However, these findings only apply to two of the nine DMC contact points, and cannot speak to differential treatment at other points of contact with law officials such as police officers, county attorneys, or their staff. That is, differential treatment may primarily occur prior to adjudication. Further research may support or refute this supposition.

Limitations and Future Directions

This study adds to the sparse research on the experiences of youth with disabilities within the juvenile justice system, but it is not without limitations. First, as this study drew on educational and administrative data for a single state, findings may not be generalizable to other states, particularly those with markedly different populations or education and juvenile justice systems. Second, this study used juvenile court records for cases closed between 2008 and 2012 for the most egregious offense a youth committed during their first court referral and may not represent all court cases at the time, multiple offenses, or recidivism. Third, this study could not explore county-level differences in the treatment of youth offenders, which may mask important patterns given attorney and judge discretion in handling of individual cases. Finally, the relatively small number of cases in which students were placed precluded more nuanced analyses (e.g., considering outcomes for student by all disability types or race) and comparison with prior research.

Future research could include all offenses resulting in referral to court across multiple court occasions to account for both the difference in quantity and nature of juvenile offending and concomitant outcomes. This would provide information about whether youth with disabilities are more likely to receive an adjudication of delinquency over time due to the volume of offenses, court appearances, or some combination of the two. Future studies should also consider the relations of court outcomes and race/ethnicity. We could not include race/ethnicity in the current analyses due to low cell sizes for the regression given our focus on outcomes for students with disabilities, and by disability category. We opted not to collapse race/ethnicity into a dichotomous White/non-White variable because of the variability in juvenile justice involvement of racial/ethnic minority youth at the national level (Hockenberry & Puzzanchera, 2018) and our desire to avoid such oversimplification censured in other areas of special education disparities (e.g., Skiba et al., 2016). Because some minority groups are at greater risk of being adjudicated and may be placed at higher rates than White youth (Hockenberry & Puzzanchera, 2018), and some racial minority groups are more likely to be identified with disabilities (Skiba et al., 2016), future research should investigate the relations between racial/ethnic minority students’ experiences within special education and juvenile justice.

As a field, we would benefit from replication of this research at multiple levels, including individual, county, state, and nation, because understanding disproportionality at multiple levels is an important first step to understanding causative factors related to disproportionality (Bollmer, Bethel, Garrison-Mogren, & Brauen, 2007). Understanding disproportionality at these various levels can both provide a baseline for measuring effects of future interventions and allow for the manipulation of malleable factors at various levels (e.g., school intervention, county attorney or judge training, state laws, federal policy). Although there was parity in outcomes of students with and without disabilities in the present analysis, involvement in a system that undermines their developmental trajectories disadvantages all students. Even “short” placement can be disruptive and undermine likelihood of school completion (Aizer & Doyle, 2015). For students with disabilities, and especially those from minority backgrounds, future research should consider intersectionality of statuses and compounding risks to educational, social, and financial outcomes. Importantly, the present findings do not disprove disproportionality in the juvenile justice system or negate decades of evidence of the overrepresentation of youth with disabilities in the system. This study considered data from one state and for two points of contact. Given long-standing evidence of inequity, it is important to explore differential treatment in all aspects of the system. As noted elsewhere, it may be that disparities occur early in youths’ involvement (Kincaid & Sullivan, 2019) rather than the more terminal outcomes explored here.

Implications for Policy and Practice

Although this study did not find widespread disproportionality in adjudication or length of commitment, the high rates of youth involvement in the juvenile justice system remains a concern, particularly for students with high-incidence disabilities such as EBD, SLD, and SLI. Involvement, particularly placement in correctional facilities, is an expensive and ineffective endeavor with negative consequences for individuals, communities, and society (Aizer & Doyle, 2015; Barnert et al., 2016; SAHM, 2016). As Aizer and Doyle (2015) noted, researchers should situate research on disparities and outcomes of juvenile justice involvement in broader context of social and human capital given that involvement is not just an issue of equity and social justice, but economic and national development. Accurate data to support identification and monitoring of youth outcomes are necessary for assessing disparities and efficacy of interventions to reduce juvenile justice participation and disparities. This may be supported by JJRA’s (2018) annual reporting requirements.

Disparities notwithstanding, nearly one in five youth interacted with juvenile justice; one quarter of these had an identified disability. For students with disabilities, families and schools can leverage the federal and state laws’ “presumption in favor of addressing the needs of a significant proportion of status offenders through the special education system rather than the juvenile justice system” (Tulman & Weck, 2009, pp. 885–866), as well as JJRA’s (2018) emphasis on research-based practices. Tulman and Weck (2009) provided several recommendations for families and attorneys to safeguard the rights of students with disabilities to advocate for effective educational, social-emotional, and behavioral supports to address the youth offenses and avoid the negative outcomes associated with escalation through the points of juvenile justice contact. When cases do proceed through the courts, special education attorneys and expert witnesses (e.g., teachers, school psychologists, administrators) can help to articulate effective special education alternatives to detention and work with staff in correctional facilities to ensure a continuation of services across systems. Finally, with the forthcoming annual reporting requirements that include disability status, stakeholders at all levels can leverage data availability to explore the experiences and outcomes across contact points and inform advocacy, practice, and policy refinement. Taken together, such efforts can “help prevent students with education-related disabilities from being flushed down the school-to-prison pipeline” (Tulman & Weck, 2009, p. 907).

Footnotes

Associate Editor: Bryan Cook

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by a grant from the National Science Foundation (SMA1338489) provided through the Minnesota Linking Information for Kids (MinnLInK) project.

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