| Jeffrey Anderson
Indiana University Purdue University Indianapolis
Modeling resiliency in the prevention of special education identification
There is a growing body of research demonstrating that certain children are at greater risk for special education identification because of their membership in specific socio-demographic groups. The persistent problem with this conceptual approach is that researchers often use socio-demographic variables to predict undesirable outcomes and ignore within-group variance. Indeed, despite predictions of academic and social-emotional problems due to elevated risks, many of these children not only avoid special education identification, but also consistently excel academically, demonstrating what behavioral scientists call resiliency. We argue that individuals are not predetermined to the same levels of risk based solely on group membership. As such, risk and resilience models are more appropriate for examining how protective factors interact with risk factors to foster resiliency, thereby decreasing the likelihood of special education identification and increasing positive school outcomes. Using longitudinal data from the ECLS-K, we apply Hierarchical Linear Modeling to examine three questions: (1) what is the extent to which individual demographic risk factors influence special education identification within specific disability categories and across time; (2) what is the degree to which family factors influence special education identification and the extent to which they interact with individual risk factors to impact these patterns of identification; and (3) how do school-level demographic variables influence special education identification and interact with family factors and individual risk factors to influence patterns of identification. Thus, we are able to examine the interplay of risk and protective factors that may create or support resiliency. Such information will help researchers, policymakers, and practitioners understand how to move past the current focus on deficits and toward a more systematic emphasis on identifying and enhancing protective factors that can substantially influence learning. These findings also will provide a more complete understanding of social and educational policy issues related to children who are at-risk for special education identification and their families.
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