Correlational Study of Community, School and Family Attachment on Illegal
Behavior of Adolescents
Nathan Miller, Alvin Malesky Jr., and Phyllis Robertson
Western Carolina University
Results and Discussion
Introduction
Methods
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.
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 7th and 12th, 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.
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.
The overall multiple regression model was significant (R2 =.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.