1. Correlational Study of Community, School and Family Attachment on Illegal Behavior of Adolescents Introduction Being able to identify the environmental influences which contribute to illegal behavior in adolescents is of critical importance to reducing such behavior. Social learning theory asserts that models in one’s environment heavily influence the observer’s behavior (Bandura, 1977). For example, parents play a role in the child’s inclination toward or away from illegal behavior. (Coombs & Paulson, 1988). In addition, research has shown that a child’s perception of violence in their neighborhood is associated with an increase in similar antisocial behavior of their own (Lambert et al., 2004). Furthermore, school connectedness, or the feeling on the part of the student that the adults in their school care about their success, has been shown to significantly correlate with increased resilience against delinquency (Battistich & Horn, 1997). Recognizing which circumstances increase the risk of these behaviors and which role models best insulate youth from engaging in illegal behaviors can assist agencies in implementing effective intervention program designed to curtail the perpetration of illegal behaviors in adolescents. State and community agencies are increasingly under pressure to “do more with less” and thus have to be judicious regarding how and where they spend their limited resources. Thus, it is hoped that this study may inform some decisions regarding where interventions should be focused in order to have the best chance of decreasing adolescent illegal behavior. However, it is hypothesized that significant correlations will be found between reported illegal behavior and each of the three environmental modeling scales. Methods Data were collected from two public high schools and a middle school in Western North Carolina as part of a gang assessment in the region. The questionnaire used in this study originated from the Office of Juvenile Justice and Delinquency Prevention (OJJDP) Comprehensive Gang Model: A Guide to Assessing Your Community’s Youth Gang Problem. Data were collected via Qualtrics, a secure online software survey program and data collection platform. Students accessed the online survey questionnaire using computers in their school’s computer labs. Participants were monitored to ensure minimal conversation occurred between students during the procedure. The monitor did not, however, view individual student responses. The student survey took between 30 to 45 minutes to complete. No identifying information was requested from the participants. Letters requesting student participation were sent to parents/legal guardians. In addition, students provided their assent to participate after the purpose and nature of the study were explained. A breakdown of the 1070 students surveyed revealed that 330 reported having participated in illegal behavior (i.e. drug use, weapon possession, alcohol consumption), of those; 136 were female, 194 were male, the group having a median age of 15(SD=1.15), the range of grade level was between 7 th and 12 th, 10.5 percent claimed Hispanic race, 3.3 percent claimed African American,.6 percent claimed Native American, and 80.9 percent claimed Caucasian. The scales used in this study demonstrated considerable internal consistency; community measure yielded a Cronbach’s alpha of.805, school.813 and family.847, and possessed face validity. Nathan Miller, Alvin Malesky Jr., and Phyllis Robertson Western Carolina University References Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Cognitive Prentice-Hall. Battistich V. & Horn A. (1997) The relationship between students’ sense of their school as a community and their involvement in problem behaviors. American Journal of Public Health; 87:1997–2001. Coombs, Robert H. & Paulson, Morris J. (1988) Contrasting family patterns of adolescent drug users and nonusers. Journal of Chemical Dependency Treatment, Vol 1(2), 59-72 Lambert S.F., Brown T.L., Phillips C.M. & Ialongo N.S. (2004) The relationship between perceptions of neighborhood characteristics and substance use among urban African American adolescents. American Journal of Community Psychology, 34, pp. 205–218. Results and Discussion The overall multiple regression model was significant (R 2 =.33, F(3,272)=44.24, p<.001). All three scales in this study together (family modeling, community modeling, school modeling) significantly predicted engagement in illegal behavior. Specifically the more negative models one was exposed to in the family, community, and at school the more likely it was that he or she was going to engage in multiple acts of illegal behavior. In addition, each of the scales independently predicted engagement in illegal behavior. As exposure to negative models increased in the family (M=23.96, SD=5.72) so too did the likelihood that the adolescent would engage in illegal behavior (beta=.175, SE=.05, t(275)=3.52, p<.001). In addition, the more negative models the adolescent was exposed to in the community (M=22.02, SD=7.32) the more illegal behavior the participant perpetrated (beta=.20, SE=.04, t(275)=5.45, p<.001). Finally, the more negative models the participant was exposed to at school (M=31.86, SD=6.25) the more likely it was that he or she would engage in illegal behavior (beta=.17, SE=.04, t(275)=4.14, p<.001). The scales used in this study were adequate predictors of illegal behavior in the at-risk adolescent population. However, none of the three stood out above the others. This result supported our hypothesis, though did not help illuminate a specific factor toward which funds would be best appointed for the prevention of adolescent misbehavior. From the results of this study it seems apparent that in order to design an effective intervention in the lives of at-risk youth, it is necessary to approach the task from the three directions at once. The amalgamation of approaches would offer the best hope for intervening before the youth become entrenched. Adjustments could be made in future research which might clarify the relation between these environmental factors, namely an increase in population size, and the inclusion of sub-scales within each of the three measures.