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.


38th Annual Convention; Seattle, WA; 2012

Event Details

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Symposium #53
Video-based Modeling: An Examination of the Evidence Based on Quality, Implementation, and Participant Outcomes
Saturday, May 26, 2012
2:30 PM–3:50 PM
LL05 (TCC)
Area: DDA/AUT; Domain: Applied Research
Chair: Rose A. Mason (Texas A&M University)
Discussant: Richard L. Simpson (University of Kansas)

Video-based modeling offers a practical method for capitalizing on the principles of observational learning as the method allows for exposure to a variety of examples. Given this, video-based modeling has been a prevalent topic in peer reviewed research addressing interventions for individuals with disabilities and has been labeled as an evidence-based practice for individuals with autism. However, video-based modeling is an umbrella term used to identify a variety of intervention protocols targeting distinct outcomes for a heterogeneous group of participants. The purpose of this symposium is to provide clarity regarding the most effective treatment protocol as well as identify for whom and under what circumstances video-based modeling yields the greatest results. Meta-analytical methods were utilized to aggregate the results of peer-reviewed video-based modeling studies for participants with a variety of disabilities and to address an assortment of targeted outcomes. The included presentations will identify protocol, participant, and outcome variables that yield differential effects. Further, the quality of the video-based modeling research methodology will be evaluated.

Keyword(s): autism, developmental disabilities, evidence-based practice, video-modeling

Video Based Modeling: A Quality of Research Evaluation

HEATHER S. DAVIS (Texas A&M University), Siglia P. H. Camargo (Texas A&M University)

Evidence-based practice requires empirical studiesthat implement rigorous methodological standards in addition to achieving desired outcomes on targeted skills. Examination of the quality of research for a given intervention allows for more definitive statements regarding functional relationships between the intervention and targeted outcomes. Video-based modeling is an emerging treatment for individuals with disabilities warranting an evaluation of the quality of the existing literature base. This study evaluates peer-reviewed video-based modeling research utilizing a 4-point Likert scale based on seven indicators of quality single-case research. A total of 56 single-case studies implementing video-based modeling with individuals with disabilities were evaluated. Of those studies, only 18 studies met minimal criteria for quality research with an overall average rating of 3.70. Issues related to delivery of the independent variable, particularly measures of fidelity, was a common omission across the studies. Quality research standards will be discussed as they relate to video-based modeling, including an overview of the strengths and weaknesses. Additionally, implications regarding the efficacy of video-based modeling and areas of future research will be addressed.


Video-Based Modeling: The Model Does Matter

ROSE A. MASON (Texas A&M University), Jennifer Ganz (Texas A&M University), Margot Boles (Texas A&M University), Leslie Neely (Texas A&M University), Heather S. Davis (Texas A&M University)

Transfer of research into practice requires explicit description of the intervention protocol to allow for replication in practical settings. However, video-modeling with other as model and self as model are2 distinct interventions frequently discussed in the literature without clear delineation of the differences. Additionally, specification of whether or not the video-based modeling intervention was delivered alone or as a component of an intervention package rarely occurs. Such grouping inhibits the transfer of research into practice and fails to identify differential effects that are likely to occur when delivery methods differ. Through a meta-analysis of improvement rate differences this study proposes to quantitatively analyze the video-based modeling literature base to provide further specificity regarding the implementation factors that yield the greatest magnitude of change. Preliminary results indicate a large overall effect size of .81 CI.834 [.80, .82] with a range from -.26 to .96. Statistically significant differences (p = .05) occurring based on type of model as moderator were obtained. Quantitative differences as it relates to implications for practice and future research will be discussed.


The Efficacy of Video-based Interventions for Secondary and Postsecondary Individuals With Dsabilities: A Meta-analysis

MARGOT BOLES (Texas A&M University), Rose A. Mason (Texas A&M University)

The differing needs and required skill sets of older populations, when compared to elementary populations, necessitates research explicitly designed for this population. However, a limited number of evidence-based practices have been identified for adolescents and adults with developmental disabilities. Video-based modeling interventions, consisting of other, self, and point of view, have shown promise across a variety of skills and for a broad array of participant characteristics. Additionally, many studies have been conducted implementing video-based modeling with older participants. The purpose of this presentation is to establish the efficacy of video-based modeling with secondary and postsecondary individuals with disabilities using meta-analysis of improvement rate differences across participants. Additionally, differential effects based on type of video-based modeling implemented are explored. Results indicate overall large effects (.71, CI.84 .69, .72). Effects based on type of video-based modeling indicate statistically significant discrepancies (p =.05) between type of video-based modeling with highest effects on targeted outcomes occurring with the implementation of video self-modeling (.86, CI.84 .82, .90).




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