Learning Disabilities and Delinquent Behaviors among Adolescents: A Comparison of Those with and without Comorbidity

Abstract

Research is inconclusive on whether adolescents with learning disabilities (LD) engage in more delinquency than adolescents without such deficits. Mixed results may result from a failure to account for the effects of co-occurring disabilities. Using the National Longitudinal Study of Adolescent Health, this study examines delinquency among adolescents without disabilities to youth with LD, Attention Disorder Symptoms (ADS), and comorbid LD/ADS. Results indicate no significant differences in property offenses, or alcohol, tobacco, and marijuana use; however, youth with LD reported significantly more violence than non-disabled youth. Findings illustrate the heterogeneous effects various disabilities have on delinquent behavior. Future research and policy implications will be discussed.

Notes

¹ It should be noted that learning disabilities should not be confused with the construct of academic achievement, as they are qualitatively two distinct concepts (Fuchs et al. 2001). The authors should also clarify that their definition of learning disability is from the U.S. context. The term learning disability means something very different in the context of the United Kingdom and European nations as it refers to what we term in the United States as intellectual disability (formally mental retardation) per the DSM (Diagnostic and Statistical Manual).

² The authors acknowledge that attention disorder symptoms are also linked to conduct disorder; however, conduct disorder is not controlled for the present analysis since that would be controlling for our dependent variable, delinquency. Previous research that has measured conduct disorder using the Add Health Data have used a majority of the same items used in delinquency research also conducted with Add Health Data (see, e.g., Anderson and Hughes 2008; Haberstick et al. 2005).

³ Although the first wave of the Add Health is now twenty years old it is still considered a premier data source for the exploration of adolescent development resulting in over 4500 journal articles, presentations, manuscripts, and dissertations thus far (http://www.cpc.unc.edu/projects/addhealth/publications). Data for the current study were taken both from Wave I (1994–1995) and from Wave IV (2007–2008).

⁴ The overall sample size is approximate and varied between analyses as a few outcome variables had more missing data than others.

⁵ The self-control measure used in this analysis is the 5-item scale originally published in 2004 by Perrone and colleagues and is among the most often used self-control measures in the Add Health Data (Beaver et al. 2008, 2010; Boisvert et al. 2012; Perrone et al. 2004; Wright et al. 2012). Students were asked how often they had trouble paying attention in school, trouble keeping their mind focused, trouble getting along with their teachers, trouble finishing their homework and whether they felt they did everything just right. These items are intended to tap the self-centeredness, physical activities, impulsivity, and simple tasks components of self-control (Perrone et al. 2004). All items were standardized and summed (α = .69). Higher scores represented lower levels of self-control.

⁶ Extreme outliers were trimmed from the sample.

⁷ Such models correct for design effects and cluster sampling procedures such as those used in Add Health. When survey weighting procedures are not used with complex survey designs, standard errors are often underestimated and thus effects or significance is overestimated (Chantala and Tabor 1999). Stata’s “svy” commands were used in this analysis.

⁸ The exponentiated coefficient is particularly helpful with regard to interpreting the strength or size of the effect. For a positive association, the exponentiated coefficient can be interpreted as a factor of increase in the dependent variable as a result of a one unit increase in the independent variable (1- e^b represents the percent increase in the dependent variable for a one unit increase in the independent variable), whereas a negative sign indicates a decrease in the dependent variable for a one unit increase in the independent variable (1- e^b represents the percent decrease in the dependent variable for a one unit increase in the independent variable).

Additional information

Notes on contributors

Mary K. Evans

MARY K. EVANS is an Assistant Professor in the Department of Criminal Justice at the University of Northern Colorado. She received her Ph.D. in Criminology and Criminal Justice at the University of Nebraska at Omaha. Her research interests include juvenile delinquency, marginalized populations, criminal justice policy, and issues pertaining to sex offenders’ management in the community.

Samantha S. Clinkinbeard

SAMANTHA S. CLINKINBEARD is an Associate Professor in the School of Criminology and Criminal Justice at the University of Nebraska at Omaha. She received her Ph.D. in Social Psychology at the University of Nevada, Reno. Her research interests include self-concept, social norms, and motivation as they relate to delinquency and substance use in adolescence and emerging adulthood.

Pete Simi

PETE SIMI is an Associate Professor in the School of Criminology and Criminal Justice at the University of Nebraska at Omaha. He received his Ph.D. in Sociology from the University of Nevada, Las Vegas. His research interests include developmental and social psychological approaches to studying juvenile delinquency, street gangs, and extremist movements.