Girls with emotional disturbance and a history of arrest: characteristics and school-based predictors of arrest.
Article Type:
Report
Subject:
Special education (Demographic aspects)
Arrest (Psychological aspects)
Mental illness (Demographic aspects)
Teenage girls (Psychological aspects)
Authors:
Gage, Nicholas A.
Josephs, Nikki L.
Lunde, Kimberly
Pub Date:
11/01/2012
Publication:
Name: Education & Treatment of Children Publisher: West Virginia University Press, University of West Virginia Audience: Professional Format: Magazine/Journal Subject: Education; Family and marriage; Social sciences Copyright: COPYRIGHT 2012 West Virginia University Press, University of West Virginia ISSN: 0748-8491
Issue:
Date: Nov, 2012 Source Volume: 35 Source Issue: 4
Product:
Product Code: 8294000 Education of Handicapped; 9105115 Special Education Programs; 9101430 Arrest Procedures NAICS Code: 61111 Elementary and Secondary Schools; 92311 Administration of Education Programs; 9221 Justice, Public Order, and Safety Activities
Geographic:
Geographic Scope: United States Geographic Code: 1USA United States

Accession Number:
310741891
Full Text:
Abstract

Research suggests that girls receiving special education services for Emotional Disturbance (ED) may have unique characteristics and needs. Similarly, juvenile justice research has identified unique characteristics of court-involved girls. This study examined characteristics of girls with ED and a history of arrest. Additionally, classroom-based behavioral performance from elementary school was examined using logistic regression to identify whether or not early predictors of arrest could be identified. Results indicated that girls with ED and a history of arrest were suspended from school less often than boys with ED, but the rate of later arrest was equivalent. Comparisons between girls with ED and girls with ED and a history arrest indicated that low-income, urban, African-American girls with ED were more likely to be arrested. Lastly, girls with ED exhibiting elevated hyperactivity during elementary school were more likely to have a history of arrest by middle and high school.

KEYWORDS: emotional disturbance, gender, delinquency

Children and youth receiving special education services for ... emotional disturbance (ED) experience bleak short and long-term outcomes, including increased rates of arrest, and present many challenges to schools, families, and communities (Bradley, Doolittle, & Bartolotta, 2008; Bradley, Henderson, & Monfore, 2004; Wagner, Kutash, Duchnowski, Epstein, & Sumi, 2005). Although a large body of research has examined characteristics and outcomes for children and youth with ED, a paucity of research has examined characteristic variations by gender (Cullinan, Osborne, & Epstein, 2004). Gender differences for students identified with ED have been highlighted as an area lacking delineation across behavioral and academic performance, as gender differences appear evident (Cullinan et al., 2004; Rice & Yen, 2010; Young, Sabbah, Yonung, Reiser, & Richardson, 2010). Similarly, juvenile justice research has highlighted gender differences indicating divergent behavioral and historical patterns related to recidivism and criminal typology (Funk, 1999; Schwalbe, 2008; Sharkey, Furlong, Jimerson, & O'Brien, 2003), and that girls become court-involved for different reasons than boys (e.g. victims of abuse, family dysfunction, school difficulties; Cauffman, 2008; Chamberlain & Moore, 2002; Leve & Chamberlain, 2004; Schaffner, 2007). To date, little research has examined characteristics and outcomes for girls receiving special education services for ED and a history of arrest.

Girls and Delinquency

Data suggest that girls' rates of delinquency, particularly acts of violence, are increasing (Colman, Kim, Mitchell-Herzfeld, & Shady, 2009; Davis, Fisher, Gershenson, Grudzinkas, & Banks, 2009; Good-kind, Wallace, Shook, Bachman, & O'Malley, 2009; Lederman, Dakof, Larrea, & Li, 2004; Leve & Chamberlain, 2004; Schaffner, 2007). For example, between the years 1980 and 2006, girls' arrests for assault increased by 395% (Goodkind et al., 2009). According to the National Center for Juvenile Justice, arrest rates were on the decline for both genders, but girls' rates of arrest have declined more slowly than boys (Tracy, Kemf-Leonard, & Abramoske-James, 2009). Further, between 2002-2006, the rate of arrests for girls was relatively stable, while the rate for boys was on a steady decline. Girls' rates of engagement in serious crimes compared with boys also appear to be shifting, with girls exhibiting increased rates of robbery and embezzlement. There has also been an increase in the rate of more serious crimes. Girls' rates of larceny-theft increased by 13.9% compared to a 3.5% increase for boys and homicide increased by 51.3% between 2003 and 2007, compared to only 0.3% increase for boys.

Examination of trends in offenses leading to court involvement by age for girls indicates that 31% of violent crimes leading to juvenile arrest occur prior to 15 years of age. Similarly, 42% of vandalism offenses, 39% of weapons offenses, 40% of disorderly conduct offenses, and 31% of runaways were reported for girls less than 15 years old. Further, following a conviction, girls generally appear to be punished more severely than boys. Tracy et al. (2009) found that female delinquents were more likely to be detained for status offenses, to be committed to a residential correctional facility, and to receive the harshest possible sanction allowed within a juvenile court.

Characteristics of Girls with a History of Arrest. A number of factors have been correlated with increased rates of girls' delinquency, including the influence of delinquent siblings and neighborhood characteristics, such as urbanicity and the presence of gangs. Tracy et al., (2009) contend that race/ethnicity may be the most salient predictor of delinquency for girls. Goodkind et al. (2009) examined a longitudinal nationally representative dataset of 10th to 12th grade students to assess patterns of delinquency by gender, finding violent behavior exhibited by girls was most prevalent among African-Americans. African-American girls also reported rates of fighting incidences similar to those of Caucasian boys. African-American and Hispanic girls' rates of injuring another individual badly (as measured by the victim needing bandages or a doctor's care) have significantly increased across time. Additionally, girls from diverse ethnic and racial backgrounds are disproportionately represented in the juvenile justice system (Goodkind et al., 2009). In 2004, African-American girls represented 31% of all juvenile arrest cases for girls in the U.S. while only representing 17% of all juveniles.

Lederman et al. (2004) identified six historical and behavioral characteristics associated with increased rates of arrest for adolescent girls: dysfunction, trauma and sexual abuse, mental health and substance abuse problems, high-risk sexual behaviors, school problems, and affiliation with deviant peers. Additional factors that have been repeatedly suggested to influence female adolescent court involvement are IQ family conflict, and biological criminality (Leve & Chamberlain, 2004). Specifically, a child who has a parent with a criminal record increases their odds of an early arrest by 15 to 283 times. Chamberlain and Moore (2002) found that 43% of a sample of adolescent girls in the juvenile justice system had mothers who had been convicted of a crime (compared to 22% of boys' mothers) and 63% of the girls had fathers who had been convicted of a crime (compared to 22% of boys' fathers).

Across various professional fields (e.g. social work, sociology, criminology), there is overwhelming agreement that there is a link between childhood victimization and later juvenile offending for girls (Cauffman, 2008; Chamberlain & Moore, 2002; Simkins & Katz, 2002), with over 90% of girls in the juvenile justice system having prior exposure to sexual, physical, or emotional abuse (Schaffner, 2007). History of abuse and neglect seem to be particularly predictive of later juvenile offending. Girls with histories of abuse and neglect are more than twice as likely to be arrested, when compared to adolescents who do not have a prior history of emotional trauma. Incarcerated female adolescents have often been subject to the experience of a significant trauma. A difference, however, is that boys are generally more likely to have witnessed a violent event, whereas girls are more likely to have been the victim of a violent event (Cauffman, 2008). Lederman et al. (2004) conducted a study with 493 adolescent girls in a short-term juvenile detention center. Of this sample, 84% had experienced a significant trauma. Girls are also more likely to be victims of sexual abuse and they are typically victims for a longer duration of time, often resulting in severe emotional and psychological trauma (Simkins & Katz, 2002).

Limited research has been conducted examining how girls' fare after they leave the juvenile justice system. Colman et al. (2009) tracked 849 released girls from New York State-operated juvenile correctional facilities. The study followed the participants from 16 years of age through to their 28th birthday, finding a recidivism rate of 81% and 34% imprisoned at least once during the twelve-year study. One factor that appeared to decrease the rate of adult recidivism was older age upon first arrest.

Mental Health and Girls with a History of Arrest. Adolescent girls in the juvenile justice system are more likely than the boys to suffer from a psychiatric illness (Davis et al., 2009; Lederman et al., 2004). According to Davis et al. (2009) adolescent girls who seek or are referred for help in the public mental health system, compared to those who do not receive services, experience a greater risk of later becoming a member of the juvenile court system. Specifically, these girls were found to be more likely to face arrest charges, be arrested at an earlier age, be arrested more frequently, and be charged with more serious offenses. Further, it was estimated that one-third of adolescent girls who received mental health services were arrested by age 18 and one-half arrested by age 25. Many girls with a history of arrest meet the criteria for at least one psychiatric disorder, including depression, anxiety disorders, and substance abuse disorders (Lederman et al., 2004). One study found that two-thirds of incarcerated adolescent boys and three-quarters of incarcerated adolescent girls met criteria for at least one disorder. Lederman et al. (2004) found that 78% of the participating adolescent girls met the criteria for at least one mental health disorder and also reported school problems, including expulsion, suspension, or being held back a grade. Graduation rates are also predictably low; one study found that only 16.8% of incarcerated female adolescents graduate from high school (Cauffman, 2008).

Girls Receiving Special Education Services for Emotional Disturbance

Although research supports early-onset conduct problems as a significant predictor of court involvement for adolescent girls (Leve & Chamberlain, 2004), little research has examined differential patterns of court involvement for girls identified with emotional disturbance (ED) as defined by the IDEA. Research suggests gender differences in behavior topography for students with ED (Rice, Merves, & Srsic, 2008; Rice & Yen, 2010). Boys are more likely to exhibit externalizing behaviors, which include antisocial behavior, fighting, and abnormally high anxiety levels. In contrast, girls are more likely to manifest ED as internalizing behaviors, such as anxiety, shyness, withdrawal, hypersensitivity, and physical complaints (Chamberlain & Moore, 2002; Young et al., 2010). Due to this gender difference, girls are often more likely to remain un-identified because their symptoms are more covert and harder to detect (Gresham & Kern, 2004).

Youth with ED are often involved with multiple service systems, including special education, juvenile justice, and child welfare. In a descriptive study of 93 secondary students with ED, Malmgren and Meisel (2002) found that the average age at which participants were identified for special education services was 10 years, the average age of initial contact with a child welfare agency was 12.9 years, and the average age of first contact with a juvenile justice agency was 12.9 years. Welfare records further indicated that youth with ED were prone to suffer from high rates of abuse and neglect, with 49% having experienced physical abuse, 29% sexual abuse, and 75% neglect. Academic problems were also relatively common, with 35% having been held back in a grade at least once and 27% having been suspended at least once prior to entering a special education class setting. Additionally, it was determined that girls were often identified for special education services later than boys.

Purpose

To date, little research has specifically examined girls with ED and a history of arrest. To address this gap, this study examined gender differences between girls and boys with ED and a history of arrest and difference between girls with ED and girls with ED and a history of arrest to provide context and comparison. Additionally, to inform potential intervention research, this study examined elementary, school-based, predictors of later arrest for girls with ED. By understanding (1) characteristics of girls with ED and (2) potential early school-based predictors of arrest for girls with ED, special educators and other school-based professionals may be able to intervene and decrease the likelihood of girls diagnosed with ED and possible court-involvement. Specific research questions were:

R1. Are boys and girls with ED and a history of arrest different across demographic characteristics and their rates of arrest?

R2. Are demographic differences evident between girls with ED that have a history of arrest and those that do not?

R3. Can teacher reported in-class behaviors from elementary school serve as predictors of later arrest for girls with ED?

Method

To address the research questions, a secondary data analysis of the Special Education Elementary Longitudinal Study (SEELS) was conducted. SEELS is a national policy study of elementary and middle school students with disabilities mandated by the U.S. Department of Education and developed and implemented by SRI International, a U.S. based non-profit research organization specializing in educational research. SEELS collected data in three waves over five years (20012006) on a sample of students receiving special education services between the ages of six and 12 years, their families, their teachers, and their schools. The SEELS database provides weighted estimates of a nationally representative sample of students with disabilities and includes a great deal of data at the student-level for analysis (Wagner, Kutash, Duchnowski, & Epstein, 2005). SEELS began by identifying a random sample of 245 school districts from the universe of school districts, stratified to represent different geographic regions, school enrollment sizes, and SES levels based on free and reduced lunch prevalence. Then, students with disabilities were randomly selected from the population of students with disabilities within the 245 districts. The initial sample of subjects with ED included 1,081, and their results were subsequently weighted to represent estimates of students nationwide (Wagner et al., 2005).

Sample

To examine the three different, yet interrelated research questions outlined above, the study used only data from students receiving special education services for ED. The SEELS database includes a disability category variable for all subjects based on school reported disability; identification was not independently confirmed but based on school records. This sample does not include students with emotional or behavioral problems that do not qualify them for special education services or those students who do not have a disability as identified by their respective schools. The wave one weighted sample included 150,208 boys and 38,766 girls with ED and 137,159 boys and 28,257 girls with ED for wave three. The attrition rate over time from wave one to wave three accounted for the reduction in sample size. It should also be noted that not all students completed every assessment measure, therefore sample sizes within the demographics tables vary. The age distribution was similar for both genders with 66.2% of the boys and 64.7% of the girls between the ages of 10 and 12 years during Wave One and 48.3% of the boys and 43.7% of the girls between the ages of 15 and 17 years for wave three.

Instruments and Variables

Demographics. A core set of demographic variables was available within the data sets. This study used the following time-invariant demographics: ethnicity, urbanicity, which included urban, suburban and rural settings, and income. Ethnicity and income was reported by the subject's parent/guardian and the urbanicity, or the size of city the students attended school in, was reported based on school information.

Parent interview. Across all three waves, the SEELS study conducted a parent/guardian interview over the telephone. The interview focused on student and family characteristics, out-of-school activities, and satisfaction with school programs. A complete copy of the interview is available for download at http://seels.net/info_reports/parent_interview.htm. This study utilized the following variables from the parent interview:

Teacher survey. Across all three waves, the language arts teacher for each subject was sent a survey. Language arts were defined as instruction in language arts, reading, English, language, functional communication, writing, or literature. A complete copy of the survey is available at http://seels.net/info_reports/language_teacher.htm. This study utilized the following variables from the teacher survey:

Although other measures were available within the data set based on teachers' ratings of student's in-class behaviors, the in-class behavior scale and hyperactivity scale were used because they were based on only two or three observed characteristics. Using a variable with a greater number of characteristics would make it difficult to parcel out key characteristics for further study and require the development of a larger measure of characteristics as a potential predictor.

Data Analysis

As noted above, this study used weighted estimates from a stratified sample, which necessitates accounting for inaccurate standard errors and potential bias in the unweighted estimator (Menard, 2002). This study used the Statistical Package for the Social Sciences (SPSS) 15.0 Complex Samples module for all analyses to accurately analyze the data and account for inaccurate standard error estimates. First, descriptive statistics were analyzed for each of the variables included in the study. Next, chi-square ([x.sup.2]) analyses were used to address research questions one and two. To identify predictors of arrest in middle and high school, logistic regression analysis was used. Inter-correlations were conducted between included variables to examine the presence or absence of multicollinearity among the predictors, and three logistic regression models were developed and analyzed. The first model examined whether or not two in-class behavioral measures from elementary school were significantly related to middle and high school history of arrest for girls with ED:

ln([^.p]/1-[^.p]) = [[beta].sub.0] + [[beta].sub.1]BehScale + [[beta].sub.2]HyperActScale + e

Next, three time-invariant demographic variables were added to the model to identify (1) whether specific demographic variables were significantly related to history of arrest by middle and high school for girls with ED and (2) whether or not the demographic variables impacted the results for the in-class behavioral measures. The parent reported income with 16 categories was used instead of the three-category income variable used for the descriptive statistics:

ln([^.p]/1-[^.p]) = [[beta].sub.0] + [[beta].sub.1]BehScale + [[beta].sub.2]HyperActScale + [[beta].sub.3]Income + [[beta].sub.4]Eihnicity + [[beta].sub.5]Urbanicity + e

Last, the first model was reanalyzed, but for just boys with ED to indentify whether or not there were model differences between the genders.

Results

Demographics

Descriptive statistics are provided in Tabel for demographic comparisons between boys and girls with ED. The ethnicity composition is almost exactly the same, with the exception of more Hispanic boys with ED (10.1%) than girls (7.1%). More girls with ED live in suburban settings (53.1%), including large cities and suburbs, than boys (41.3%). The income distribution was fairly equitable across gender. The greatest difference was in the history of suspension during elementary school. A much larger proportion of boys were suspended in elementary school (50.7%) than girls (19.7%). In fact, history of suspension was the only variable with statistically significant differences between boys and girls ([x.sup.2]= 15.67, df= 1, p < .000). There was not a statistically significance difference between history of arrest for boys and girls ([x.sup.2]= 0.539, df = 1, p = .406).

Table 2 provides descriptive statistics for girls with ED with a history of arrest and for those without. Results suggest that a greater percentage of African-American girls with ED were arrested (33.5%) than Caucasian girls (24.9%) and, overall, there was a significant difference between arrest history and ethnicity ([x.sup.2] = 5.76, df = 2, p = .05). Additionally, a much greater percentage of girls with a history of arrest attended urban schools, but differences among urbanicity were not statistically significant. Differences in income were also evident, with lower social-economic status (SES) girls with ED having a history of arrest, which was statistically significant ([x.sup.2]= 5.94, df = 4, p = .031), as well as a greater percentage of girls with ED coming from homes where the head of household had less than a high school diploma, which was statistically significant ([x.sup.2] = 6.83, df = 3, p = .040). Overall, these results suggest that, using nationally representative estimates, a greater percentage of girls with ED and a history of arrest are African-American, low-income, and live in urban settings.

Logistic Regression

A goal of this study was to determine whether or not observable in-class behaviors as reported by teachers in elementary school could serve as potential predictors of arrest by middle and high school. Table 3 provides the intercorrelation between history of arrest and the predictor variables. Although many of the correlation coefficients are statistically significant as a function of the sample size, none of the coefficients exceed .37.

The first model examined whether or not the classroom behavior scale or the hyperactivity scale could serve as elementary predictors. The mean classroom behavior scale scores were 3.56 and 3.20 for those without a history of arrest and for those with a history of arrest, and the means for the hyperactivity scale scores were 6.25 and 6.50 respectively. Results from Table 4 suggest that, together, both variables significantly predicted whether or not a female student with ED was arrested (Wald F = 4.957, df = 13, N = 17,102, p = .025). The odds ratios suggest that the odds of being arrested increase as the students exhibit problematic behaviors across both measures, but the odds increase is larger for the hyperactivity scale. However, the Nagelkerke pseudo [R.sup.2] value was .09, meaning the model only accounted for 9% of the variance in history of arrest.

When ethnicity, income and urbanicity were included in the model as additional covariates, the influence of the in-class behavior scale in predicting a significant amount of the variance was reduced. The model remained significant (Wald F = 6.336, df =6, N = 12,686, p = .024) indicating that together, the variables significantly predicted history of arrest. Table 5 indicates that the odds ratio of the hyperactivity scale increased from 1.780 to 2.432. Additionally, the odds ratio for ethnicity was very large at 4.960, suggesting that the odds of arrest are greater for minority youth (Caucasian was recorded as one, African-American was recorded as two). However, the ethnicity result should be interpreted with caution because of the large standard error. The Nagelkerke pseudo [R.sup.2] value was .32, indicating the model accounted for 32% of the variance in history of arrest. Overall, these results suggest that female students' with ED odds of being arrested by wave three increased if they scored higher on the Hyperactivity Scale when controlling for ethnicity, income, and urbanicity in the model.

The final model examined whether or not the in-class behavioral measures similarly predicted arrest for boys with ED. The mean scores for boys with ED on the classroom behavior scale were 3.68 and 3.89 for those without a history of arrest and for those with a history of arrest, and the mean scores for the hyperactivity scale were 6.82 and 6.88, respectively. Table 6 presents the findings from the first logistic regression model, but analyzed with 52,248 boys with ED. The results indicate that, although the odds ratios suggest an increase in negative in-class behavior increases the odds of later arrest, neither the classroom behavior scale nor the hyperactivity scale were significant predictors of arrest by middle or high school. These results suggest that there may be differences between boys and girls around behavioral manifestations during elementary school and later court involvement.

Discussion

This study examined characteristics of girls receiving special education services for ED and a history of arrest. Additionally, to inform potential intervention research, the study examined early predictors of later arrest for girls with ED. Overall, the results were consistent with prior research, but for only girls receiving special education services for ED. Unlike prior research, this study examined early predictors of later arrest and found teacher reported hyperactivity to be the single best predictor.

Characteristics of Girls with ED and a History of Arrest

Across the included characteristics, only a history of suspension was found to be significantly different between boys and girls with ED. This tangentially supports the contention that girls are more likely to manifest ED as internalizing behaviors, because anxiety, shyness, withdrawal, hypersensitivity, and depression are not typically associated with school suspensions (Chamberlain & Moore, 2002; Young et al., 2010). Interestingly, even though girls were less likely to be suspended from school, no significant differences were found in regards to history of arrest. This result, combined with the suspension result, matches other research (e.g. Chesney-Lind & Okamoto, 2001), and suggests girls' reasons for arrest may be different than boys. Although there is evidence of increased violent crimes, girls are often arrested for status offenses, such as running away, which could be linked to internalizing typography, such as depression and withdrawal, or escaping from in-home violence and other family related trauma.

Findings comparing girls with ED and girls with ED and a history of arrest indicated a number of significant differences. The results suggest that low-income, African-American girls receiving special education services for ED living in urban environments were more likely to be arrested in middle and high school. These findings are consistent with previous research for girls with a history of arrest (e.g. Tracy et al., 1999) and indicate the same pattern is present for girls with ED and a history of arrest.

Predictors of Later Arrest for Girls with ED

To inform further research, we modeled whether behavioral school-based early predictors were related to later arrest for girls with ED. Two teacher-reported scales of in-class behavior were used. The classroom behavior scale measured students' compliance and homework completion, while the hyperactivity scale measured teacher perceptions related to hyperactivity in the classroom. When controlling for ethnicity, urbanicity, and income, the hyperactivity scale was the most predictive measure of later arrest for girls. These results indicate that girls exhibiting characteristics often associated with Attention Deficit Hyperactivity Disorder, such as impulsivity, in elementary school are more likely to have a history of arrest by middle and high school. Even more interesting was that the same model was not significant for boys. Overall, the study points to hyperactivity as a potential targeted intervention area for young girls that may have a concomitant impact on the rates of arrests for girls. Further, the hyperactivity characteristics are not typically associated with internalizing behaviors, which may highlight a link between externalizing behavioral manifestations in early grades related to later history of arrest.

Limitations

Although this study utilized a nationally representative weighted sample, a number of methodological limitations necessitate highlighting. First, the sample analyzed was weighted, meaning the results are estimates from the population. Even though care was taken for accurate estimates, the results should be interpreted with caution. Additionally, the validity of the independent variables, specifically the in-class behavior scale and hyperactivity scale, necessitates further research. Standardized behavioral measures would have been ideal, but were unavailable in the dataset. The in-class behavior scale did not measure just student compliance, but also homework completion, which could be related to a series of non-classroom behavior related influences, such as academic ability and home environment. Additionally, the study was unable to compare results between and across girls not receiving special education services for ED, reducing generalizability of results, as the sample was specific only to girls with ED. Lastly, There is no way to verify whether or not some of the girls included in earlier studies of girls and delinquency received special education services for ED.

Future Directions

Research. The results of this study suggest a number of areas for future research. First, research should examine whether the demographic characteristics and early predictors maintain as girls with ED continue into late high school and adulthood. Second, studies should collect more targeted, ideally longitudinal, data on girls with ED and a history of arrest. By targeted, we refer to valid and reliable measures of behavior, including standardized measures, and more details about the types of crimes resulting in arrest. Questions remain as to whether or not the girls without disabilities truly do not have a disability or whether schools are failing to accurately identify them. Studies should be conducted comparing girls with a history of arrest without disabilities and those with ED. Finally, given the findings reported here, studies should examine interventions that reduce impulsive and hyperactive behaviors exhibited by young girls and determine whether or not these interventions have a positive, lasting effect on reducing court involvement for girls with ED.

Practice. Girls with ED need targeted and effective evidence-based practices to address their behaviors of concern, whether external (e.g. physical aggression) or internal (e.g. depression). The results of this study indicate that the field needs to identify ways to address the disproportional representation of African-American girls with ED arrested. Unfortunately, there is no single answer to address this problem. Understanding that African American girls with EDs' risk for later arrest is elevated may allow practitioners to (a) be aware of differential treatment/punishment for these girls and (b) develop better data systems to track disproportionality of court involvement and examine potential school-based reasons for the disproportionality, including teacher, administrator, or measurement bias.

The findings related to impulsivity and hyperactivity in elementary school as significant predictors of later arrest have direct implications for practice. Research should continue to confirm this link, but practitioners can use this information to begin identifying young girls with ED demonstrating impulsive and hyperactive behavior for more targeted interventions, including function-based and small group social skills interventions. Efforts put forth early may pay off later for these girls, potentially dramatically impacting their lives and future outcomes.

Conclusions

The results of this study are concerning, but can inform future research and practice to prevent court involvement for girls with ED. Further research is necessary to identify potential pathways beyond student-level characteristics (i.e. ethnicity, urbanicity, and SES) related to higher rates of arrest. Although student-level characteristics are essential macro-level indicators, causal models examining more nuanced and specific student-level characteristics, such as behavioral topography (i.e. internalizing or externalizing), academic performance, or other specific, measurable characteristics, related to history of arrest are necessary to identify potential pivotal areas of intervention.

Overall, the study found that young girls exhibiting hyperactive behavioral characteristics in classrooms as reported by their teachers were more likely, or had greater odds, of being arrested in middle and high school, controlling for ethnicity, urbanicity, and SES. This finding, in tandem with the findings for boys, although preliminary, indicates that girls have different behaviors predictive of later arrest. Girls may need different preventative interventions to reduce court involvement. Without targeted interventions, these girls will continue down difficult long-term negative trajectories. By understanding potential indicators, we as field may be able to reduce the rate of arrest for girls with ED. Although these results are preliminary, they indicate that targeted areas can be identified and intervened.

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Correspondence to Nicholas Gage, Center for Behavioral Education and Research, Neag School of education, University of Connecticut,

Nicholas A. Gage

University of Connecticut

Nikki L. Josephs

Manhattanville College

Kimberly Lunde

University of Connecticut
Education level of head of household: during wave three, the parent
  or guardian was asked the question "What is the highest year or grade
  you finished in school?" Head of household designation was based on
  the following: if a subject lived with both parents, the highest
  education level was coded as head of household; if the child lived
  with one parent, the educational level of the parent/guardian
  interviewed was recorded.

  Income: during wave three, the parent or guardian was asked their
  annual income. The response was recorded into one of 16 value
  categories broken out into $5,000 increments, e.g. $05,000,
  $5,001-10,000, etc.

  History of suspension in lementary school: during wave one, the
  parent or guardian was asked the question "Has your child ever
  been suspended from school?" Responses included yes, no, don't know,
  or refused. All subjects' parent/guardian in this study responded
  with yes or no.  History of arrest: during wave three, the parent
  or guardian of a child 12 years of age or older was asked the
  question "Has your child ever been arrested?" If the child was 12
  years or older before wave three, the parent was asked, "Has
  your child been arrested in the past two years?" The final
  variable included a yes or no answer representative of all
  three waves. No data were collected on the reason for arrest
  or type of crime committed.


Classroom behavior scale: during wave one, the teacher was asked a
  series of questions about the subjects' in-class behavioral
  performance. This variable was created based on the response for "how
  often does this student (1) follow your directions and (2) complete
  homework on time". Responses were recorded on a three point Likert
  scale with "never," "sometimes," or "always". The final variable had
  a scale of 2 to 6, with 2 being never and 6 being very often.
  Hyperactivity scale: during wave one, the teacher was asked a series
  of questions about the subjects' behavioral performance focused on
  hyperactivity. The variable was created using the same process as the
  in-class behavior scale, but was based on the following questions:
  "how often does this student (1) easily transition from one classroom
  activity to another, (2) get easily distracted, and (3) act
  impulsively. The final variable had a scale of 3 to 9, with three
  being never and 9 being very often.


Table 1 Descriptive Statistics for Arrested Youth with ED by Gender

Category            Variable    Boys % (n)   Girls % (n)   Total % (n)

Ethnicity

                       White  61.8 (92935)  64.7 (25088) 62.4 (118023)

            African-American  25.5 (38354)  27.3 (10600)  25.9 (48954)

                    Hispanic  10.1 (15197)    7.1 (2746)    9.5 (7943)

               Asian/Pacific    2.2 (3246)       0.0 (0)    1.7 (3246)
                    Islander

             Native American     0.2 (303)     0.9 (332)     0.3 (634)

             Multi-Racial or     0.2 (274)       0.0 (0)     0.2 (274)
                       Other

Urbancity

                       Rural  18.7 (27388)   13.2 (5073)  17.6 (32461)

                    Suburban  41.3 (60433)  53.1 (20461)  43.8 (80894)

                       Urban  40.3 (58542)  33.7 (12997)  38.7 (71540)

Income

                 $25,000 and  42.1 (62737)  48.9 (18761)  43.5 (81498)
                       Under

                  $25,001 to  30.6 (45535)  29.3 (11218)  30.3 (56753)
                     $50,000

                Over $50,000  27.3 (40664)   21.8 (8359)  26.2 (49023)

Education
Level of
Head of
Household

              Less than High  20.7 (26780)   14.3 (4726)  19.4 (31506)
                      School

                 High School  38.3 (49649)  43.3 (14291)  39.3 (63950)
             Graduate or GED

                Some College  27.0 (34975)  34.2 (11295)  28.5 (46270)

              B.A. or Higher  14.0 (18187)    8.2 (2690)  12.8 (20877)

History of
Suspension
in
Elementary

                          No  49.3 (69809)  71.9 (26391)  53.9 (96200)

                         Yes  50.7 (71855)  28.1 (10294)  46.1 (82149)

History of Arrest by
Middle and
High
School

                          No 83.6 (109366)  80.3 (27794) 83.9 (137159)

                         Yes  16.4 (21452)   19.7 (6805)  17.1 (28257)


Table 2 Descriptive Statistics for Girls with ED by History of
Arrest

Category                  Variable         Not  Arrested    Total %
                                    Arrested %     % (n)        (n)
                                           (n)

Ethnicity

                             White        66.3      61.7       65.4
                                       (18439)    (4197)    (22636)

                  African-American        24.9      33.5       26.6
                                        (6915)    (2276)     (9191)

                          Hispanic         8.8   0.0 (0)        7.1
                                        (2440)               (2440)

                   Native American     0.0 (0)       4.9  1.0 (332)
                                                   (332)

Urbancity

                             Rural        12.5       9.0       11.8
                                       (34501)     (614)     (4064)

                          Suburban        57.9      50.9       56.5
                                       (15957)    (3461)    (19418)

                             Urban        29.6      40.1       31.7
                                        (8151)    (2730)    (10882)

Income

                 $25.000 and Under        44.7      69.8       49.7
                                       (12432)    (4747)    (17180)

                        $25,001 to        26.1      26.7       26.2

                           $50,000     (7241))    (1820)     (9060)

                      Over $50,000        29.2       3.5       24.2
                                        (8121)     (238)     (8359)

Education Level
of Head of
Household

                    Less than High        10.7      37.4       15.1
                            School     (25990)    (1821)     (4420)

                       High School        41.6      33.4       40.2
                   Graduate or GED     (10136)    (1627)    (11763)

                      Some College        38.6      29.2       37.1
                                        (9419)    (1422)    (10840)

                    B-A. or Higher         9.1   0.0 (0)        7.6
                                        (2220)               (2220)

History of
Suspension in
Elementary

                                No        76.1      64.6       73.9
                                       (20095)    (4066)    (24161)

                               Yes        23.9      35.4       26.1
                                        (6309)    (2230)     (8539)


Table 3 Intercorrelations for History of Arrest and Predictor Variables

Measure                          1        2       3        4     5   6

1. History of Arrest             -

2. In-Class Behavior Scale -.17 **        -

3. Hyperactivity Scale      .06 **   .36 **       -

4. Ethnicity                .06 **      .00 -.21 **        -

5. Income                  -.25 **  -.36 **  .29 **  -.33 **      -

6. Urbanicity               .09 **      .02    -.28  -.37 **  .25**  -

Note. ** p < .000

Table 4 Logistic Regression Predicting Arrest for girls with ED

                                        95% Confidence
                                     Interval for Odds
                                                 Ratio
Variable        [beta]    SE    Odds     Lower   Upper       p
                               Ratio

Classroom        -.928  .398    .395      .168    .929   .035 *
Behavior
Scale

Hyperactivity     .323  .118   1.381     1.071   1.780   .016 *
Scale

Note. * p < .05


Table 5 Logistic Regression Predicting Arrest of Girls with ED
with Covariates

                                        95% Confidence
                                     Interval for Odds
                                                 Ratio
Variable        [beta]    SE    Odds     Lower   Upper       p
                              Ratio

Classroom       -.678   .484    .528      .170   1.516    .194
Behavior
Scale

Hyperactivity    .889   .298   2.432     1.239   4.774  .015 *
Scale

Income          -.229   .143    .795      .795    .576    .142

Urhanicity      -.267  1.626    .765      .019  30.317    .873

Note. * p < .05

Table 6 Logistic Regression Predicting Arrest for Boys with ED

                                        95% Confidence
                                     Interval for Odds
                                                 Ratio
Variable        [beta]    SE    Odds     Lower   Upper      p
                              Ratio

Classroom        .248   .208    1.28      .845    1.94   .238
Behavior
Scale

Hyperactivity   -.047   .208    .954      .732    1.25   .726
Scale
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