School-Based Behavioral Assessment Within Problem-Solving Models: Current Status and Future Directions

Abstract

The timing for this special series affords an excellent opportunity to integrate a body of new syntheses relevant to school-based behavioral assessment. Burgeoning information regarding the extension of problem-solving logic into system-wide frameworks such as response to intervention (National Association of State Directors of Special Education, 2008) has facilitated knowledge of key constructs (e.g., data-based decision making, progress monitoring, universal screening) among service delivery providers. Recent collaborative publications by national agencies have highlighted staggering findings related to the mental, emotional, and behavioral needs of youth, suggesting both serious needs and promising service delivery options that often involve school-based settings (National Research Council and Institute of Medicine, 2009). This special series offers a timely and critical review of big ideas related to school-based behavioral assessment. Mental, emotional, and behavioral disorders often emerge during childhood and are predictive of lower school achievement, increased demands on the juvenile justice system, increased burden on the child welfare system, and substantial cost that extends into hundreds of billions of dollars each year (National Research Council and Institute of Medicine, 2009). It has been estimated that one in five school-aged children requires mental health care, yet the majority of them will not receive services (Hoagwood & Erwin, 1997; President's New Freedom Commission, 2003). These needs pose serious challenges for school providers (Romer & McIntosh, 2005). For example, educators report spending a significant amount of time responding to behavior challenges presented by a relatively small number of students (U.S. Department of Education, 2000), and one in three teachers has considered leaving the profession or knows someone who has left because of issues related to discipline and behavior (Public Agenda, 2004). Reports such as these substantiate a critical need to facilitate student success in behavioral domains in schools. Prevention and early intervention has long been touted as the route for promoting student success in school, and use of a problem-solving model has received support as a process for effectively addressing student needs (e.g., Tilly, 2008). Although problem-solving models incorporating response to intervention logic (e.g., School-Wide Positive Behavior Supports: www.pbis.org) can offer a useful framework that includes relevant components of behavioral success (e.g., teaching and reinforcing expected behavior), implementation cannot be fully realized without use of technically adequate and usable sources of data to drive decisions in regard to appropriate intervention supports. Calls have been made to establish defensible, data-based decision making tools for use in problem-solving models (e.g., National Center on Response to Intervention: www.rti4success.org). However, the emphasis has been primarily in the realm of basic academic skills; assessments for mental, emotional, and behavioral functioning have lagged far behind. Critical Features of Behavioral Assessment Methods Within Problem-Solving Models Although response to intervention may be viewed as a new initiative, the process of problem solving is not (Reschly & Bergstrom, 2009). The basic steps involve problem identification, problem analysis, plan development and implementation using evidence-based strategies, and plan evaluation (e.g., Tilly, 2008). As implied by these steps, important features of assessment involve accurate problem identification (i.e., screening) and the evaluation of progress toward expected outcomes (i.e., progress monitoring). Using guidelines offered by Christ, Riley-Tillman, and Chafouleas (2009), critical features of behavior assessments that have utility in a problem-solving model include the following: (a) defensibility (psychometric research that provides evidence of validity for interpretation and use), (b) flexibility (guides a variety of assessment situations), (c) efficiency (requires relatively few resources--both feasible and reasonable), and (d) repeatability (yields time series data). …

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