Volume 1, Issue 1 • Fall 2011

Table of Contents


Measuring Recidivism in Juvenile Corrections

Barron County Restorative Justice Programs: A Partnership Model for Balancing Community and Government Resources for Juvenile Justice Services

Parents Anonymous® Outcome Evaluation: Promising Findings for Child Maltreatment Reduction

Assessing Efficiency and Workload Implications of the King County Mediation Pilot

The Impact of Juvenile Drug Courts on Drug Use and Criminal Behavior

Missouri’s Crossover Youth: Examining the Relationship between Maltreatment History and Risk of Violenc

Assessing and Improving the Reliability of Risk Instruments: The New Mexico Juvenile Justice Reliability Model

School Policies, Academic Achievement, and General Strain Theory: Applications to Juvenile Justice Settings

COMMENTARY: School Policies, Academic Achievement,
and General Strain Theory: Applications to Juvenile
Justice Settings

Janay B. Sander, The University of Texas at Austin
Jill D. Sharkey, University of California Santa Barbara
Alexandra Lamari Fisher, The University of Texas at Austin
Stacey Bates, Pflugerville Independent School District, Austin Psychology and Assessment Center
Jenny A. Herren, Judge Baker Children’s Center, Harvard Medical School

Janay B. Sander, Department of Educational Psychology, University of Texas at Austin; Jill D.
Sharkey, Department of Educational Psychology, University of California Santa Barbara; Alexandra Lamari Fisher, Department of Educational Psychology, University of Texas at Austin; Stacey Bates, Pflugerville Independent School District and Austin Psychology and Assessment Center, Austin, Texas; Jenny A. Herren, Judge Baker Children’s Center, Harvard Medical School.

Correspondence concerning this article should be addressed to Janay B. Sander, Department of Educational Psychology, University of Texas at Austin, Austin TX 78712.
E-mail: janay.sander@mail.utexas.edu

Key words: juvenile delinquency, general strain theory, learning disabilities, psychological assessment


This review provides a discussion of school-related policies and how they relate to juvenile justice (JJ) practices. The goal of this article is to provide an interdisciplinary understanding that integrates education and school psychology with JJ literature. The paper goes beyond a general review of the known educational challenges common in juvenile offender populations to focus on academic and emotional challenges in school settings and how these challenges can foster delinquency according to general strain theory (Agnew, 2005). The shared understanding may facilitate research, influence policies, and stimulate advocacy to address school challenges facing youth who may be at risk for juvenile crime and recidivism.


For several decades, a consistent thread in the juvenile justice literature has addressed patterns of low educational attainment (Cottle, Lee, & Heilbrun, 2001; Felson & Staff, 2006; Maguin & Loeber, 1996): Academic failure, school disciplinary exclusion, and dropout predict youth delinquency and crime; academic success is a buffer for juvenile crime (Foley, 2001). Although outcomes associated with high school dropout include increased rates of unemployment, substance abuse, and criminal activity, only 15% of youth served by the juvenile justice (JJ) system graduate with a high school diploma (Stanard, 2003). Conversely, educational attainment is important for eventual gainful employment, job satisfaction, greater social capital (Andres & Grayson, 2003), and lower recidivism (Zgoba, Haugebrook, & Jenkins, 2008). The overall problem of low academic achievement has many causes, among which is the failure to recognize when a youth should qualify for special education status.

Low achievement and its connection to special education status is not completely understood. A consistent concern in JJ settings, special education status is not a reliable indicator of delinquency. The special education rate for JJ populations, about 35% (Kvarfordt, Purcell, & Shannon, 2005; Zabel & Nigro, 1999), is well above the approximate 13% national rate of special education service delivery in public schools (National Center for Education Statistics, 2010). While the average age of the adjudicated youth is 15 years (approximately 9th-10th grade), the average reading level is 4th grade or lower (Vacca, 2008). Academic skill level, and who does not qualify for or receive appropriate special education services, may be more important school-based indicators of delinquency risk than special education status.

However, the individual student is not the only concern; school environments with high retention, low attendance, ineffective behavioral management, low teacher instructional skill, and reactive and punitive discipline strategies are associated with academic failure, suspension, and dropout (Christle, Jolivette, & Nelson, 2005). In light of the large literature base and consensus that academic failure and school-based risks promote negative outcomes, the paucity of information on how school experiences and policies relate to juvenile delinquency theory is striking (Sander, Sharkey, Olivarri, Tanigawa, & Mauseth, 2010).

Given the scope of the problem, and that JJ and public schools serve an overlapping population of youth at risk for school failure and crime, a policy approach that could permeate multiple systems offers a promising solution. Furthermore, creating informed policies based on theory and research is important to avoid confusion. With these tenets in mind, we offer an interdisciplinary lens with which to view chronic school-specific concerns, including suggestions for policy and research.

Low Intelligence and Learning Challenges:

Policy Side Effects vs. Background Risks

Academic failure, together with the presence of “low intelligence,” is discussed repeatedly in the JJ literature (Juvenile Justice Educational Enhancement Program, 2005; Leone, Christle, Nelson, Skiba, Frey, & Jolivette, 2003), but there is a dynamic relationship among policies, educational practices, and causes of academic failure. Several circumstances in schools could be related to the apparent connections among academic achievement, the presumed trait of “low intelligence,” and delinquency. These circumstances include assessment practices, special education eligibility procedures, and the overall quality of educational interventions.

Assessment Practices and Decision Making with Regard to Diversity

Before elaborating on current policies and practices, it is necessary to acknowledge some historically rooted concerns, documented as early as the 1920s, about assessment (Valencia & Suzuki, 2001). The issue of bias and fairness in testing has been and still is hotly debated. There is no satisfying consensus as to why there are lingering differences in cognitive assessment scores among racial and ethnic groups, or how to change tests or test practices to fairly address cultural differences while retaining useful and meaningful scores (Griffore, 2007; Helms, 2006; Mayer & Hanges, 2003). Testing is useful but imperfect. It is important for professionals conducting and using assessments, as well as researchers incorporating assessment data, to remain aware of these caveats.

Identifying Learning Disabilities: Eligibility and Barriers

There are several contributions to chronic school problems that have not been adequately discussed in the JJ literature: (a) who qualifies for special education, (b) who does not qualify for special education services, and (c) the overall quality of education and instruction that youth involved in JJ receive in any setting. To begin, eligibility for special education due to a learning disability has, historically, been based on scores, typically discrepancies between scores (Meyer, 2000). Interestingly, the widely used learning disability (LD) qualification criteria, namely the discrepancy model, have no empirical or theoretical foundation (Flanagan & Alfonso, 2011).

“Discrepancy” refers to the difference between cognitive abilities test scores and academic achievement scores. To qualify for special education for an LD, the following conditions must be met: (a) there must be a discrepancy between ability and achievement, (b) low achievement is not due to lack of appropriate instruction, and (c) there is educational need, such as the student is doing poorly in classes. In other words, when there is low achievement but not a score profile or evidence of educational need that meets eligibility criteria, the student does not receive special education services.

The criteria for special education eligibility differ considerably from state to state. Several statistical formulas, known as “discrepancy formulas,” are used in various states to calculate the discrepancy between a juvenile’s educational ability and achievement, such as standard-score differences (42% of states), regression formulas (13% of states), and expectancy formulas (8% of states) (Meyer, 2000). Within states that apply the standard-score difference, about one-third use a 15-point discrepancy, another one-third require a 22-point discrepancy, and the final third use different discrepancy formulas (Meyer, 2000).

The discrepancy formula is usually applied on standardized tests with a mean of 100 points and standard deviation of 15 points; a “severe discrepancy” is one standard deviation. For example, a student with a cognitive abilities composite standard score of 100 (an average score) and a math achievement score of 84 (a below average score) displays a significant discrepancy between ability and achievement. This student would potentially be assessed as having a specific learning disability in mathematics computation.
In another example, a student with a cognitive abilities composite score of 90 (a low average score) who is also struggling in school with the same math achievement score of 84 would not be eligible for special education services for a specific learning disability. This student would be considered to be performing in mathematics at a level consistent with his or her intellectual abilities, not as having an ability-achievement discrepancy. Students with abilities in the low average range would have to show dramatically lower achievements to attain a discrepancy evaluation; that is, their achievement scores would have to be 75 or lower, even if their actual achievements are equal to many other students already receiving special education support for academic challenges.

This scenario is relevant to JJ populations who frequently achieve scores in the low average range in research (Maguin & Loeber, 1996). “Low average intellectual ability” is a standardized cognitive abilities score (in lay terms, intelligence quotient, or IQ) between 85 and 90 points. This “low average” or “low” intelligence is a risk factor cited in several research studies on juvenile crime (Felson & Staff, 2006; Maguin & Loeber, 1996), but the direction of the relationship between IQ and delinquency has mixed findings and no consensus in the literature (Menard & Morse, 1984; Ward & Tittle, 1994). Undiagnosed learning problems can be problematic, fostering emotional distress and experiences of shame over time (Orenstein, 2000). School eligibility policies may be one of the important outside variables explaining the inconsistent and confusing connections between special education and delinquency.

Furthermore, in the JJ system, youths experience interruptions in academic instruction for a variety of reasons: detainment awaiting adjudication, placement changes, or truancy. While it is difficult to determine the average length of stay in juvenile detention facilities, more than 50% of detained juveniles tend to be released within 30 days (OJJDP, 2008). Although such short placement time is favorable in some ways, the hidden cost is that it leads to a noted interruption of school attendance and instruction (Children’s Defense Fund, 2010). Given that LD cannot be considered if lack of exposure to instruction is the cause for low achievement, it is likely many JJ youth would not be considered for special education—even if it is appropriate.

Potential Improvements in the Identification of Specific Learning Disabilities

Currently, educational practices are changing due to changes in the federal laws. The resulting assessment changes have policy implications for juvenile assessment centers as well as schools. In 2004, federal lawmakers reauthorized the Individuals with Disabilities Education Act, or IDEA. One of the most significant changes is decreased emphasis on the above-mentioned discrepancy formula. One alternative to the use of the discrepancy formula for assessing potential learning disabilities is the cross-battery assessment (XBA) (Flanagan, Ortiz, & Alfonso, 2007). Becoming increasingly common throughout the United States, this assessment is based on the most current intelligence theory, the Cattell-Horn-Carroll (CHC) theory, and involves the identification of a specific area of cognitive weakness that accounts for an academic skill deficit. XBA does not rely on detecting discrepancies between cognitive and achievement scores.

Solidly research-based, XBA and CHC theory provide a theoretical framework that allows psychologists to better conceptualize the relationship between cognitive abilities and academic achievement (Institute for Applied Psychometrics, 2009). In addition, the XBA tools allow for the systematic consideration of linguistic and cultural concerns (Flanagan & Alfonso, 2011). The shift in focus from discrepancy to cognitive weaknesses helps to clarify the reasons for low achievement: that is, whether low achievement is due to a lack of instruction or to a true cognitive weakness that is impairing an academic skill development. For youths involved with JJ in particular, it is often impossible to determine whether low academic achievement is due to lack of instruction. The XBA framework is preferable to the discrepancy formula for identifying the underlying cognitive reasons for setbacks in achievement. There are additional benefits to using XBA within JJ settings. Since XBA does not require different tests, only a different approach to test interpretation, the XBA approach could be integrated into existing juvenile assessment centers’ current psychological battery protocols. The XBA framework holds promise for more accurately identifying the unique challenges in understanding academic achievement in JJ populations.

Learning Interventions in Juvenile Justice Settings

Once a juvenile’s academic deficit is identified, the next step is to provide effective intervention. Evidence-based educational and instructional interventions are sorely lacking in the JJ literature. This is a striking absence given the prevalence of learning challenges in JJ populations. There are enough studies to conduct a meta-analysis of the effectiveness of delinquency interventions, including educational services such as tutoring or vocational programs, on school success (see Wilson, Lipsey & Soydan, 2003). These educational services alone, however, are not equal to empirically-supported academic interventions in terms of improving academic skills, nor are they clearly effective in reducing crime.

For example, only a handful of studies have been conducted on the effectiveness of reading interventions in juvenile detention facilities (Krezmien & Mulcahy, 2008). Evaluations of interventions in other academic areas, such as math and writing, are practically non-existent in the JJ literature. Staff, including educators and detention and probation officers, are often unaware of how to (a) note the presence of learning challenges or (b) make appropriate recommendations to address them (Kvarfordt et al., 2005). In a recent meta-analysis of academic outcomes in juvenile delinquency interventions examining published and unpublished studies from 1974 to 2010 in school and JJ settings, only 14 studies (out of 250+ studies identified) could be included in the final analysis—none included empirically supported academic interventions (Sander, 2011). The absence of empirically based academic interventions in JJ populations is alarming, but there are resources to increase the presence of research-based interventions for academic achievement.

To help fill the gap that currently exists, the Institute for Education Sciences (IES) and the U.S. Department of Education maintain publicly available educational intervention resources on a variety of topics, grade-levels, and instructional methods, called the What Works Clearinghouse (WWC) (Institute for Education Sciences & U.S. Department of Education, n.d.). The resources available from the WWC show that hiring or training teachers and staff to provide educational interventions, not just special education or general education per se, is important.

Summary: Assessment, Special Education Eligibility and Learning Disabilities

In brief, the recurrent finding in the literature that juvenile offenders frequently have “low intelligence” could be an artifact resulting from several sources, including (a) historical assumptions about culture in assessment practices, (b) eligibility policies that rely on “wait to fail” and discrepancy methods, and (c) an evolving perspective on what an LD is and how it is measured. In many ways, students who have “low average” intelligence are also those struggling academically, yet based on discrepancy formulas and other exclusionary criteria, such as ruling out a lack of instructional exposure, may not qualify for special education services. Current assessment practices are evolving to better address concerns about ethnic, linguistic, and cultural differences. In summary, it is a feasible policy shift to prioritize identifying sources of school-delinquency patterns. The changes do not require additional staff, simply applying contemporary learning assessment practices and providing empirically supported academic intervention methods via trained professionals already working in JJ settings. Taking programs already available in many public schools and adopting them in JJ settings could facilitate progress in determining appropriate educational interventions and research in JJ settings.

More specifically, research is needed in every aspect of academics, including reading, writing, vocational training, mathematics, overall educational attainment, and the employment trajectory. Specifically, incorporating good research design, theory, and academic interventions would help to advance research and practice (Lipsey & Cullen, 2007).

Emotional and Behavioral Difficulties in Schools and Strain

Emotional and behavioral disorders, in addition to academics, are of concern for juvenile offenders in relation to schools. Youth in the JJ system have a high rate of mental disorders relative to the general community population (GCP). These disorders include: conduct disorder (81% in the JJ system vs. 9.5% in the GCP), mood disorders (56% in the JJ system vs. 20.8% in the GCP), and attention deficit hyperactivity disorder (ADHD) (18.5% in the JJ system vs. 8.1% in the GCP) (see Davis, Bean, Schumacher, & Stringer, 1991; Kessler et.al., 2005).

In education settings, juveniles are evaluated for emotional or behavioral disorders to see if they are eligible for special education. They may be eligible if they meet the criteria for “emotional disturbance” (ED), which may or may not align with a mental disorder diagnosed in the GCP or psychiatric setting. (In some cases youth with ADHD may be considered eligible for special education under the category of “other health impairment,” which is not a mental disorder category—it is a medical disorder eligibility category). For most emotional and behavioral disorders, eligibility for special education in the category of ED is based on

…exhibiting one or more of the following characteristics over a long period of time and to a marked degree that adversely affects a child’s educational performance: (a) An inability to learn that cannot be explained by intellectual, sensory, or health factors; (b) An inability to build or maintain satisfactory interpersonal relationships with peers and teachers, (c) Inappropriate types of behavior or feelings under normal circumstances, (d) A general pervasive mood of unhappiness or depression, and (e) A tendency to develop physical symptoms or fears associated with personal or school problems…. (United States Department of Education, IDEA, 2004).

Understanding rates of mental illness among juveniles, as well as knowing how ED influences their educational needs, is challenging to consolidate. The needs of juveniles with either mental disorders or ED outnumber the available resources across settings. Some mental health experts estimate that more than 20% of all school-age children have mental health needs severe enough to require some treatment (Hoagwood & Erwin, 1997), while less than 1% of all children receive special education for ED in public schools. The overall percentage of children receiving special education for any reason is about 13% of students (National Center for Education Statistics, 2010).

Precise information on the prevalence of ED in the JJ system is particularly challenging to collect, but one such study appears trustworthy. Quinn et al. (2005) conducted a national survey of juvenile corrections with the Center for Effective Collaboration and Practice and the National Center on Education, Disability and Juvenile Justice during the 2001-2002 academic year. There was a 73% response rate from the 51 heads of state departments of juvenile corrections and the combined juvenile and adult correctional systems. Results indicated that 47.7% of youth who were receiving special education services within the juvenile corrections systems met the criteria for ED. During the same academic year, public schools reported that only 8.2% of youth in special education programs met the criteria for ED (U.S. Department of Education, 2002).

It is important for juvenile offenders to have access to special education services for LD and ED in the public schools, but the definitions and eligibility criteria for both ED and special education differ across settings. From the school’s perspective, special education eligibility is based solely on whether and how the disability is interfering with a child’s ability to learn. Thus, ED or a mental disorder diagnosed by an outside (non-school) psychologist might not lead to a juvenile’s eligibility for special education for ED in the schools. Concomitantly, meeting the criteria for special education does not guarantee that youth will qualify for psychological services with county mental health services or community-based organizations for the treatment of ED or a mental disorder.

Finally, due to ambiguities within ED criteria, such as a “social maladjustment” exclusionary clause (for thorough discussion of social maladjustment, see Olympia et al., 2004), youths with ED are often underserved in the public schools (Gresham, 2007). Even when a student may qualify to receive special education services under the ED label, there are additional barriers, such as certain school policies, that may impede academic success. Some of these barriers are disciplinary actions and school policies for disruptive or aggressive behavior that could be due to ED but may not be identified as such.

School Discipline Policies

Discipline policies designed to reduce behavior problems are often counterproductive in reducing overall school behavior problems, and seem to facilitate the transition from schools to prisons for many youths (Leone et al., 2005). Educational administrators at all levels have incorporated zero tolerance policies that require mandatory expulsion for students who commit certain offenses. Zero tolerance policies not only interfere with students’ participation in instruction time, they also tend to exacerbate the behavior they are intended to prevent (Morrison, Anthony, Storino, Cheng, Furlong, & Morrison, 2001). Zero tolerance policies are implemented inconsistently and disproportionately affect minority groups and students with special needs (Morrison et al. 2001). In addition, behavior related to a disability, such as impulsivity, inattention, or processing difficulties can be misinterpreted as non-compliance with or defiance of rules and be punished. Rather than being punished for disability-related behaviors, students exhibiting these behaviors would benefit from receiving appropriate and specific interventions (Johnson, 2007). Some studies have reported that expelled students with JJ involvement who do not receive such interventions face emotional and learning challenges in schools (Sander, et al. 2010; Morrison et al. 2001).

When students are punished for exhibiting areas of disability and not provided with appropriate interventions to help overcome them, it is unlikely that anything other than school disengagement will result. In transition planning, particularly for youths who may qualify for special education by virtue of having ED, building in supports for the externalizing behaviors that are part of the disability status in schools may help buffer some of the exclusionary policies for these youths. According to the IDEA (2004), students may not receive disciplinary actions for behaviors that are a result, or manifestation, of a disability. For JJ staff, it is important to be aware of this legal aspect so they can advocate for needed supports for youth. The advocacy could help in designing a more successful school or community reintegration/transition plan.

Overall, the negative consequences of zero tolerance policies are not surprising, given the loss of academic instruction time, increased unsupervised free time, and feelings of alienation from school (Sharkey, Bates, & Furlong, 2004). On the other hand, school-based interventions that do address delinquency are being investigated; it is clear that both JJ and schools serve these same youth. Positive discipline policies that teach appropriate behaviors and use positive approaches over punishment strategies are generally effective in reducing delinquency (Wilson & Lipsey, 2007). The JJ system should keep in mind the empirical data that positive strategies are more effective than negative ones, and should consider collaboration and outreach to encourage schools to employ these approaches with youths served by both systems.

Another caveat to the discipline and exclusionary policy concern is the overall parallel inequity in schools and JJ settings. Interestingly, these school policies and their effects mirror the over-representation and disproportionate distribution of more severe punishments for specific ethnic, racial, and cultural groups within the JJ systems (Johnson, 2007). There is a negative correlation between academic achievement and discretionary removal for disciplinary purposes (Clarke, 2002), and this is a widespread concern of systemic inequity.

For example, while public school disciplinary referral rates for White males and African American males are equal, the reasons teachers opt for disciplinary referral are different. According to Skiba et al. (2002), White males were more likely to be referred for objective offenses such as using obscene language, vandalism, smoking, and leaving school without permission. African American males were more likely to be referred for more subjective behaviors such as disrespect, loitering, excessive noise, and threatening harm. While threats are not to be taken lightly, perceiving an action as a threat depends upon a teachers’ judgment of what constitutes a threat. African American students are overrepresented on all measures of school discipline, with disproportionality increasing as the punishment became harsher, even when controlling for socioeconomic status (Skiba, et al. 2002). Changing these large-scale patterns in schools and JJ systems requires changing school policies.

The root of the disparity between White and African American students in terms of disciplinary action and eventual exclusion from school appears to be a complex, surreptitious, and system-wide pattern of discretionary decisions, access, and policies (Gregory, Skiba, Noguera, 2010). These facilitate school failure and increase delinquency. In the face of a large-scale and complex phenomenon, theory can help to clarify the issues. We offer an existing theory to facilitate understanding of how patterns, policies, and practices noted in schools link to delinquency.

Proposed View of General Strain Theory in Education Settings

According to strain theory, strain creates negative emotion and the resulting affective experience is exacerbated by several conditions, including (a) failure to use or the ineffective use of coping strategies (cognitive, behavioral, or emotional), (b) lack of adequate social support, and (c) blocked goals. Crime is likely when the following three conditions are met: strain is present, the costs of delinquency are low, and the benefits of delinquency are high (e.g., having nothing to lose with an opportunity to gain status) (Agnew, 2003, 2005). General strain theory is empirically supported and does explain and predict some aspects of juvenile crime and delinquency (Eitle, 2010; Moon, Morash, McCluskey, & Hwang, 2009; Jennings, Piquero, Gover, & Pérez, 2009; Piquero & Sealock, 2010).

In an important clarification of strain theory, Agnew (2001) added that only certain types of strain would result in crime and delinquency; specifically, strain caused by circumstances the juvenile views as unjust, strain that is of long duration, strain that becomes linked with low social control, and strain the juvenile perceives as being high in magnitude. The specific empirical link from general strain theory to school-based risks for strain, however, is limited and inconsistent. Although poor educational outcomes are repeatedly linked to ongoing delinquency, surprisingly few studies have reported support for the presence of school-based strain. It seems clear that strain caused by blocked educational attainment does not predict delinquency (Agnew, 2001). Researchers have also examined the effects of other forms of strain on juveniles. One study that directly examined and modeled some specific school factors within the context of general strain theory offered modest support for the overall theory. In that study, which used a national archival dataset, variables comprising strain were (1) school safety and (2) exposure to criminal activity (Lee & Cohen, 2008).

Although important, these aspects of strain are narrow indicators of school experiences. It is unclear how those definitions fit within the theorized forms of strain that would lead to delinquency. It is necessary to consider how unjust, high in magnitude, or linked to low social control the strain events are for an individual student before we can begin to understand delinquency-inducing strain. Schools are complex systems with many considerations, including the process and policies that guide educational service delivery decisions. Based on some of the school patterns discussed here, we offer a different perspective and some options for thinking about how school-based risks fit with strain theory.

First, we propose that school-based sources of strain are related primarily to the youth’s experience of frustration or shame stemming from the emotional experience of inadequacy in classroom tasks (Orenstein, 2000), not from the “blocked goal” of school success or high grades. A sense of inadequacy in performing classroom tasks seems an especially likely source of strain for those who are assessed with LD in particular, and helps to explain the consistent finding of low academic achievement and low intelligence in the juvenile justice literature spanning three decades.

Second, the issue of ED qualification or exclusion, and the particular situation in which a student receives disciplinary consequences for behaviors that may be related to an ED (whether diagnosed or undiagnosed) are likely culprits for strain that would facilitate delinquency. The prevalence of zero tolerance policies, irrespective of the ED manifestation question, may also be a source of strain. These policies seem connected to strain in the form of (a) unjust experience in school and (b) consequences that would erode a student’s connection to schools and weaken delinquency deterrence.

Finally, in our view, any of the experiences mentioned above would facilitate the ongoing disengagement from school and weaken those sources of social control that would otherwise deter delinquency. We are using the large literature base and making educated hypotheses to link the long-standing policies in education systems with the theoretical and research based literature in juvenile justice. The complexity of the problem requires interdisciplinary understanding and considerable future research to sort out. All of the proposed connections between schools and general strain theory outlined above will require research. We hope that someone—or many researchers, actually, given the scope of the problem—reading this paper will conduct such research.


Understanding the connections between JJ and public school policy will help to facilitate success for juvenile offenders and improve educational outcomes. The educational challenges that many juvenile offenders have experienced during their school histories are considerable, but these challenges are only vaguely addressed in the current literature.

There are several ways that juvenile facilities can assist in identifying and reducing potential sources of strain, including: (a) using emerging assessment practices to identify LD, (b) implementing and conducting research on educational interventions using realistic quasi-experimental research designs, and (c) collaborating with other schools and community agencies to address sources of strain for juveniles with mental health concerns for the purposes of prevention, intervention, and transition planning. The results of such shifts in policy could lead to the incorporation of delinquency theory and educational advances within the field of JJ.

About the Authors

Janay Boswell Sander, Ph.D., is assistant professor in the Department of Educational Psychology, University of Texas at Austin.

Jill D. Sharkey, Ph.D., is academic coordinator at the University of California, Santa Barbara. Dr. Sharkey conducts her research at the Center for School-Based Youth Development. Her publications focus on gender and ethnic differences in emotional and behavioral problems, student engagement, risk and resilience, school discipline, school safety and violence, and screening and assessment for antisocial behavior.

Alexandra Lamari-Fisher, M.A., is a graduate student at the University of Texas at Austin.

Stacey Leigh Bates, Ph.D., is a licensed specialist in school psychology, Pflugerville Independent School District, and an associate at Austin Psychology and Assessment Center, Austin, Texas.

Jenny Herren, Ph.D., is a postdoctoral fellow at the Judge Baker Children’s Center/Harvard Medical School.


Agnew, R. (2001). Building on the foundation of general strain theory: Specifying the types of strain most likely to lead to crime and delinquency. Journal of Research in Crime and Delinquency, 38, 319-361. doi:10.1177/0022422780103004001

Agnew, R. (2003). An integrated theory of the adolescent peak in offending. Youth & Society, 34(3), 263-299. doi:10.1177/0044118X02250094

Agnew, R. (2005). Juvenile delinquency: Causes and control (2nd ed.). Los Angeles, CA: Roxsbury Publishing Company.

Andres, L., & Grayson, J. (2003). Parents, educational attainment, jobs and satisfaction: What’s the connection? A 10-year portrait of Canadian young women and men. Journal of Youth Studies, 6(2), 181-202. doi:10.1080/1367626032000110309

Children’s Defense Fund (2010, October). Juvenile Detention Reform in Ohio (fact sheet). Retrieved May 1, 2011 from Annie E. Casey Foundation Website: http://www.aecf.org/~/media/Pubs/Initiatives

Christle, C. A., Jolivette, K., & Nelson, C. M. (2005). Breaking the school to prison pipeline: Identifying school risk and protective factors for youth delinquency. Exceptionality, 13(2), 69–88. doi:10.1207/s15327035ex1302_2

Cottle, C. C., Lee, R. J., & Heilbrun, K. (2001). The prediction of criminal recidivism in juveniles: A Meta-analysis. Criminal Justice and Behavior, 28(3), 367-394. doi:10.1177/0093854801028003005

Clarke, F.L. (2002) Zero-tolerance discipline: The effect of teacher discretionary removal on urban minority students. (Unpublished doctoral dissertation). University of Texas, Austin, TX.

Davis, D. L., Bean, G. J., Schumacher, J. E., & Stringer, T. L. (1991). Prevalence of emotional disorders in a juvenile justice institutional population. American Journal of Forensic Psychology, 9(1), 5–17.

Eitle, D. (2010). General strain theory, persistence, and desistance among young adult males. Journal of Criminal Justice, 38(6), 1113–1121. doi:10.1016/j.jcrimjus.2010.08.003

Felson, R. B., & Staff, J. (2006). Explaining the academic performance-delinquency relationship. Criminology: An Interdisciplinary Journal, 44(2), 299-319. doi:10.1111/j.1745–9125.2006.00050.x

Flanagan, D. P., & Alfonso, V. C. (2011). Essentials of specific learning disability identification. Hoboken, NJ: John Wiley & Sons, Inc.

Flanagan, D. P., Ortiz, S. O., & Alfonso, V. C. (2007). Essentials of cross-battery assessment (2nd ed.). Hoboken, NJ: John Wiley & Sons, Inc.

Foley, R. M. (2001). Academic characteristics of incarcerated youth and correctional education programs: A literature review. Journal of Emotional and Behavioral Disorders, 9(4), 248–259. doi:10.1177/106342660100900405

Gregory, A., Skiba, R. J., & Noguera, P. A. (2010). The achievement gap and the discipline gap: Two sides of the same coin? Educational Researcher, 39(1), 59–68. doi:10.3102/0013189X0935762

Griffore, R. J. (2007). Speaking of fairness in testing. American Psychologist, 62(9), 1081–1082. doi:10.1037/0003-066X.62.9.1081

Gresham, F. M. (2007). Response to intervention and emotional and behavioral disorders: Best practices in assessment for intervention. Assessment for Effective Intervention, 32(4), 214–222.

Helms, J. E. (2006). Fairness is not validity or cultural bias in racial-group assessment: A quantitative perspective. American Psychologist, 61(8), 845–859. doi:10.1037/0003-066X.61.8.845

Hoagwood, K., & Erwin, H. (1997). Effectiveness of school-based mental health services for children: A 10-year research review. Journal of Child and Family Studies, 6, 435–451. doi:10.1023/A:1025045412689

Institute for Applied Psychometrics (2009). CHC cognitive-achievement relations: What we have learned from the past 20 years of research. Retrieved March 15, 2011 from http://www.iapsych.com/chccogachmeta/chccogachmeta.html

Institute for Education Sciences & U. S. Department of Education (n.d.). What Works Clearinghouse. Retrieved February 4, 2011, from http://ies.ed.gov/ncee/wwc/

Jennings, W. G., Piquero, N. L., Gover, A. R., & Pérez, D. M. (2009). Gender and general strain theory: A replication and exploration of Broidy and Agnew’s gender/strain hypothesis among a sample of southwestern Mexican American adolescents. Journal of Criminal Justice, 37(4), 404–417. doi:10.1016/j.jcrimjus.2009.06.007

Johnson, O. C. A. (2007). Disparity rules. Columbia Law Review, 107(2), 374–425.

Juvenile Justice Educational Enhancement Program (2005). Incarcerated delinquent youths: Educational deficiencies and best practices. In Bales, W.D., Benebey, D., Blomberg, T.G., Ciftci, S., Dwyer, C., Jensen, E., et al (Eds.), 2005 Annual report to the Florida Department of Education (pp. 99-116). Retrieved June 6, 2011 from http://www.criminologycenter.fsu.edu/jjeep/pdf/annual2005/chapter6ar05.pdf

Kessler, R.C., Berglund, P., Demler, O., Jin, R., Merikangas, K.R., & Walters, E.E. (2005) Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the national comorbidity survey replication. Archives of General Psychiatry, 62, 593–602. doi:10.1001/archpsyc.62.6.593

Krezmien, M. P., & Mulcahy, C. A. (2008). Literacy and delinquency: Current status of reading interventions with detained and incarcerated youth. Reading & Writing Quarterly, 24, 219–238. doi:10.1080/10573560701808601

Kvarfordt, C. L., Purcell, P., & Shannon, P. (2005). Youth with learning disabilities in the juvenile justice system: A training needs assessment of detention and court services personnel. Child & Youth Care Forum, 34(1), 27–42. doi:10.1007/s10566-004-0880-x

Lee, D. R., & Cohen, J. W. (2008). Examining strain in a school context. Youth Violence and Juvenile Justice, 6(2), 115–135. doi:10.1177/1541204007308430

Leone, P.E., Christle, C.A., Nelson, C.M., Skiba, R., Frey, A., & Jolivette, K. (2003) School failure, race, and disability: Promoting positive outcomes, decreasing vulnerabilities for involvement with the juvenile delinquency system. Retrieved May 1, 2011 from EDJJ: The National Center on Education, Disability, and Juvenile Justice: http://www.edjj.org/Publications/list/leone_et_al-2003.pdf

Lipsey, M. W., & Cullen, F. T. (2007). The Effectiveness of correctional rehabilitation: A review of systematic reviews. Annual Review of Law and Social Science, 3, 297–320.

Maguin, E., & Loeber, R. (1996). Academic performance and delinquency. In M. Tonry (Ed.), Crime and justice: A review of research, 20 (pp. 145–264). Chicago, IL: University of Chicago Press.

Mayer, D. M., & Hanges, P. J. (2003). Understanding the stereotype threat effect with ‘culture-free’ tests: An examination of its mediators and measurement. Human Performance, 16(3), 207–230. doi:10.1207/S15327043HUP1603_3

Menard, S., & Morse, B. J. (1984). A structuralist critique of the IQ-delinquency hypothesis: Theory and evidence. American Journal of Sociology, 89, 1347–1378.

Meyer, M. S., (2000). The Ability-achievement discrepancy: Does it contribute to an understanding of learning disabilities? Educational Psychology Review, 12(3), 315–337. doi:10.1023/A:1009070006373

Moon, B., Morash, M., McCluskey, C.P., & Hwang, H. (2009) A comprehensive test of general strain theory: Key strains, situational- and trait-based negative emotions, conditioning factors, and delinquency. Journal of Research in Crime & Delinquency, 46(2), 182–212. doi: 10.1177/0022427808330873

Morrison, G. M, Anthony, S., Storino, M. H., Cheng, J. J., Furlong, M. J., & Morrison, R. L. (2001). School expulsion as a process and an event: Before and after effects on children at risk for school discipline. In R. J. Skiba & G. G. Noam (Eds.), Zero tolerance: Can suspension and expulsion keep schools safe? (pp. 45–71). San Francisco: Jossey–Bass.

National Center for Education Statistics (2010). The condition of education 2010. U.S. Department of Education, Institute of Education Science. Accessed April 2, 2011 at http://nces.ed.gov/pubs2010/2010028_2.pdf

Office of Juvenile Justice and Delinquency Prevention (2008). Census of juveniles in residential placement 2006. Washington, D.C.: OJJDP. Accessed April 2, 2011 at http://ojjdp.ncjrs.gov/ojstatbb/corrections/qa08401.asp?qaDate=2006

Olympia, D., Farley, M., Christiansen, E., Pettersson, H., Jenson, W., & Clark, E. (2004). Social maladjustment and students with behavioral and emotional disorders: Revisiting basic assumptions and assessment issues. Psychology in the Schools, 41(8), 835–847. doi:10.1002/pits.20040

Orenstein, M. (2000). Picking up the clues: Understanding undiagnosed learning disabilities, shame, and imprisoned intelligence. Journal of College Student Psychotherapy, 15(2), 35–46.

Piquero, N.L., & Sealock, M.D. (2010) Race, crime, and general strain theory. Youth Violence & Juvenile Justice, 8(3), 170–186. doi: 10.1177/1541204009361174

Quinn, M.M., Rutherford, R.B., Leone, P.E., Osher, D.M., & Poirier, J.M. (2005) Youth with disabilities in juvenile corrections: A national survey. Exceptional Children, 71(3), 339–345

Sander, J. B.( 2011). Meta-analytic study: Academic outcomes of interventions for juvenile delinquency. San Francisco, CA: National Association of School Psychologists Annual Convention, February 24, 2011.

Sander, J. B., Sharkey, J. D., Olivarri, R., Tanigawa, D. A., & Mauseth, T. (2010). A qualitative study of juvenile offenders, student engagement, and interpersonal relationships: Implications for research directions and preventionist approaches. Journal of Educational & Psychological Consultation, 20(4), 288–315. doi:10.1080/10474412.2010.522878

Sharkey, J.D., Bates, M. P., & Furlong, M. J. (2004). Ethical considerations regarding the prediction of violent behavior in schools. In M. J. Furlong, M. P. Bates, D. C. Smith, & P. M. Kingery (Eds.). Appraisal and Prediction of School Violence. (pp. 1-10). Hauppage, NY: Nova Science Publishers.

Skiba, R. J., Michael, R. S., Nardo, A. C., & Peterson, R. L. (2002). The color of discipline: Sources of racial and gender disproportionality in school punishment. Urban Review, 34, 317–342.

Stanard, R. (2003). High school graduation rates in the United States: Implications for the counseling profession. Journal of Counseling & Development, 81(2), 217–221.

U.S. Department of Education. (2002). Twenty-fourth annual report to Congress on the implementation of the Individuals with Disabilities Education Act. Washington, DC: Author. Retrieved from the U.S. Department of Education http://www2.ed.gov/about/reports/annual/osep/2002/section-ii.pdf

United States Department of Education (n.d.) Building the Legacy: IDEA 2004. Retrieved January 11, 2011 at http://idea.ed.gov/explore/homehttp://idea.ed.gov/explore/home

Vacca, J. S. (2008). Crime can be prevented if schools teach juvenile offenders to read. Children and Youth Services Review, 30, 1055–1062. doi:10.1016/j.childyouth.2008.01.013

Valencia, R. R., & Suzuki, L. A. (2001). Intelligence testing and minority students: Foundations, performance factors, and assessment issues. Thousand Oaks, CA US: Sage Publications, Inc.

Ward, D. A., & Tittle, C. R. (1994). IQ and delinquency: A test of two competing explanations. Journal of Quantitative Criminology, 10(3), 189–212. doi:10.1007/BF0222121

Wilson, S., & Lipsey, M. W. (2007). School-based interventions for aggressive and disruptive behavior: Update of a meta-analysis. American Journal of Preventive Medicine, 33(2 Suppl), S130–S143. doi:10.1016/j.amepre.2007.04.011

Wilson, S., Lipsey, M. W., & Soydan, H. (2003). Are mainstream programs for juvenile delinquency less effective with minority youth than majority youth? A meta-analysis of outcomes research. Research on Social Work Practice, 13(1), 3-26. doi:10.1177/1049731502238754

Zabel, R. H., & Nigro, F. A. (1999). Juvenile offenders with behavioral disorders, learning disabilities, and no disabilities: Self-reports of personal, family, and school characteristics. Behavioral Disorders, 25(1), 22–40.

Zgoba, K. M., Haugebrook, S., & Jenkins, K. (2008). The influence of GED obtainment on inmate release outcome. Criminal Justice and Behavior, 35(3), 375–387. doi:10.1177/0093854807311853

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