Name: Dana Princiotta Organization: University of Arizona Email: Danap@email.arizona.edu

1. Comparing Female Versus Male Juvenile Delinquents: Predicting Adjudication for a School-Based Sample of Arrested Youth D.K. Princiotta & R.J. Morris University of Arizona, Tucson, AZ 85721 RESULTS INTRODUCTION DISCUSSION AND CONCLUSIONS Abstract Little research has been published on female involvement in juvenile delinquency. In addition, few studies have been published comparing female versus male juvenile delinquents on various school-related variables. The current study used a prediction model to determine school-based variables that predict adjudication status of female and male youth who have been arrested. Problem statement The primary objective of this study was to examine the possible causal relationship between sex and the following school-related variables for middle and high school juvenile offenders: IDEA classification, Learning Disability, Emotionally Disability, and socioeconomic status in predicting which youths will be adjudicated. It is hypothesized that the model will identify school-related variables that differentially predict whether males orfemales have been adjudicated. In comparing males and females for the final model, the model fits males better than it does females. However, it must be noted that many more males were arrested in this sample than females. Creating a prediction model for adjudication status presents many issues as the balance between males and females is typically unbalanced. The final model includes the independent variables of GPA and IDEA status (0,1). School-related variables that were excluded through various analyses included: SES, ED, LD, and AIMS (Arizona Instrument to Measure Standards) scores. This model was superior to other models for overall fit. Many models were attempted, however this model captures the research question most closely, making certain variables of less interest (e.g., drug offense, ethnicity). As might be expected, the relationship between GPA and adjudication status was a negative one. IDEA status has more negative significance with adjudication status than GPA. GPA was the best predictor of adjudication. Less expected, SES was not a good predictor in this model, as previously hypothesized. For future analyses, it could be beneficial to examine the students that the model did not fit and find out what variable differentiates these students from those in which the model did fit. Once discovered, groups can be created based on that variable, with less emphasis on gender, to build a stronger prediction model. Table 1. Results of Logistic Regression Dependent Variable=Adjudication Status (0,1) ______________________________________________ B S.E . Wald Sig. Exp(B) ______________________________________________ GPA -.464 .117 15.828 .000 .629 IDEA -.764 .210 13.254 .000 .466 Constant -.471 .242 3.785 .591 .624 *Includes males and females Table 2. Model Summary ______________________________________________ Nagelkerke’s ______________________________________________ Males .050 Females .075 Both sexes .060 ______________________________________________ The overall model is significant at the 0.05 level according to the Model chi-square statistic. The model predicts 67% of the adjudication cases correctly. In comparing males and females, the prediction was better for males than for females. The results from the final model indicate that the coefficients on IDEA and GPA are negative and statistically significant at the p<.05 level. Children with IDEA status are 12% more likely to be adjudicated. Children who decrease GPA scores by one point increase their odds of adjudication by approximately 50%. METHODS AND MATERIALS • Participants • 4686 students; • Public school district in the SW region of the U.S. • Enrolled in middle schools and high schools.  • Data • Coded by the school district and juvenile court--the researchers were not involved in the coding of data. • Design and Analysis • Logistic regression was utilized to predict the probability of adjudication by fitting the model to the data. • Procedure • Dataset acquired through an Intergovernmental Agreement (IGA) between the school district, the University of Arizona, and the juvenile court center via a CD-ROM with the already cleansed data.  • This excel file was exported into SPSS for data analysis. ACKNOWLEDGEMENTS This research was supported by the Children’s Policy and Research Project at the University of Arizona. REFERENCES Chesney-Lind, M. & Belknap, J. (2004). Trends in delinquent girls’ aggression and violent behavior: A review of the evidence. In M. Putallaz and K.L. Bierman (Eds), Aggression, antisocial behavior, and violence among girls (pp.203-222). New York: Guilford Press. Foley, A. (2008). The current state of gender-specific delinquency programming. Journal of Criminal Justice, 36, 262-269. Gorman-Smith, D., & Loeber, R.F. (2005). Are developmental pathways in disruptive behaviors the same for girls and boys? Journal of Child and Family Studies, 14, 15-27. Mullis, R.L., Cornille, T.A., Mullis, A.K., & Huber, J. (2004). Female juvenile offending: A review of characteristics and contexts. Journal of Child and Family Studies, 13, 205-218. CONTACT Name: Dana Princiotta Organization: University of Arizona Email: Danap@email.arizona.edu