Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.


36th Annual Convention; San Antonio, TX; 2010

Event Details

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Paper Session #394
Conceptual and Practical Issues in Selecting and Analyzing School-Based Interventions
Monday, May 31, 2010
10:30 AM–11:50 AM
Texas Ballroom Salon D (Grand Hyatt)
Area: EDC
Chair: Gary L. Cates (Illinois State University)
Toward a Behavior Analytic Method of Identifying Effective Instructional Reading Interventions
Domain: Service Delivery
GARY L. CATES (Illinois State University), Kristin N. Johnson-Gros (Eastern Illinois University)
Abstract: Although the analysis procedures (e.g. comparing experimental conditions in a multi-element design) and the purposes between the experimental analyses of aberrant behavior and the experimental analyses of academic responding are similar (i.e. facilitating intervention development/selection), the mediating factors are distinct. Experimental analysis of aberrant behavior has focused on testing mediating hypotheses related to behavioral function whereas experimental analysis of academic responding has focused primarily on comparing evidence based strategies with little emphasis on hypothesis testing. Although researchers have proposed hypothesized reasons for academic skill deficiency, few studies have utilized this theoretical framework (e.g. Daly & Martens; 1994). Moreover, Haring and Eaton (1978) have provided an instructional hierarchy that when considered in conjunction with response topographies may facilitate the linking of analysis of academic responding with a theory driven approach to the selection of interventions being compared. By blending a hypothesis testing approach with an instructional model that emphasizes learning as hierarchical process, educators may be better equipped in prescribing effective interventions. The proposed presentation will provide clinical data of students with reading difficulties that highlight a process of blending experimental analysis of academic responding with an instructional hierarchy framework.
Examining Models for Academic Interventions: Emphasis on Academic Response Patterns
Domain: Service Delivery
KRISTIN N. JOHNSON-GROS (Eastern Illinois University), Gary L. Cates (Illinois State University)
Abstract: Two models of examining potential academic interventions that have been proposed are the Instructional Hierarchy (IH) by Haring and Eaton (1978), which suggests a linear model of learning beginning with an Acquisition stage to Generalization and Maintenance stages. Another model proposed by Daly and colleagues suggest that interventions are prescribed by the least to most intrusive for teachers (e.g., can’t versus won’t). This model typically employs Brief experimental analysis (BEA) which is a single subject methodology in which potential interventions are implemented alternatively to identify the most effective intervention. Although the effects of BEA have shown to be effective (e.g., Daly & Martens, 1994; Eckert, Ardoin, Daisey, & Scarola, 2000), in most of the studies, researchers addressed reading fluency with much less emphasis on mathematics. The proposed presentation will examine both models from a conceptual standpoint. In addition, the presenters will demonstrate studies that highlight each model with mathematics. The presenters will synthesize both models to aid in the conceptualization of future research but also on how the assessment of students should be analyzed by response patterns to better highlight specificity of interventions.
Enhancing School-Wide Positive Behavior Support Through Structured Direct Observation of the Classroom Ecology
Domain: Applied Behavior Analysis
PHILIP L. CONCORS (ABC Consultants, LLC), Karen M. Zeltman (ABC Consultants, LLC), Vincent Winterling (Winterling Consultants), Karen Woods (ABC Consultants, LLC)
Abstract: School-wide positive behavior support (SWPBS) programs often rely on office discipline reports (ODR) as the primary metric through which to evaluate outcomes. Although the use of direct observation measures are advocated in the SWPBS conceptual literature as a more rigorous method of determining program efficacy, relatively few studies employ such measures. Ecobehavioral assessment within the classroom environment has been empirically supported both as a means to inform intervention (e.g. teacher training) and to evaluate program outcomes (e.g. class-wide behavior plans). Presented in this case example, a taxonomy of instructor behavior, student behavior, and instructor-student interactions was developed from the relevant research literature in order to supplement the ODR data collected for a large school district in the mid-Atlantic region. Active-engagement, opportunity-to-respond, praise-to-correction ratio, and teacher-directed instruction were some of the measures utilized to refine the evaluation of an SWPBS program, and also to inform consultative focus in regard to identified areas of need.
Examining Discrepancies in Applied Data
Domain: Applied Behavior Analysis
KIM KILLU (University of Michigan - Dearborn), Kimberly P. Weber (Gonzaga University)
Abstract: A basic component of behavior analysis involves the collection of quantitative data used to determine program needs and make modifications based on the interpretation of evidence. The data obtained maybe derived from a variety of sources and the results may be in direct conflict with one another for a number of reasons. The variance obtained when collecting large amounts of data can interfere with the data's interpretation and evaluation. Such variance in data, however, is inevitable and simply an inherent characteristic of organisms and environments. Rather than viewing variability and discrepancies as a hindrance to program development, they should be embraced as an expected occurrence and a source for further investigation. This paper will examine common discrepancies in data, the reasons for variance in data, and provide recommendations for integrating data discrepancies with intervention planning.



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