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Cited by 1,009

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A Meta-Analysis of Video Modeling and Video Self-Modeling Interventions for Children and Adolescents with Autism Spectrum Disorders

Bellini, Scott, Akullian, Jennifer·2007·Exceptional Children

Abstract

This meta-analysis examined the effectiveness of video modeling and video self-modeling (VSM) interventions for children and adolescents with autism spectrum disorders (ASD). Twenty-three single-subject design studies were included in the meta-analysis. Intervention, maintenance, and generalization effects were measured by computing the percentage of nonoverlapping data points (PND). Results suggest that video modeling and VSM are effective intervention strategies for addressing social-communication skills, functional skills, and behavioral functioning in children and adolescents with ASD. Results also indicate that these procedures promote skill acquisition and that skills acquired via video modeling and VSM are maintained over time and transferred across persons and settings. The results suggest that video modeling and VSM intervention strategies meet criteria for designation as an evidence-based practice.

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Metadata

Authors
Bellini, S., & Akullian, J.
Venue
Exceptional Children
Published
2007-04
Volume
73
Issue
3
Pages
264-287
ISSN
  • 0014-4029 (ISSN-L)
  • 2163-5560
OpenAlex
W2102620940
Cited by
1,009
References
3
FWCI
8.05
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Meta-Analysis of Video Modeling and Video Self-Modeling Interventions for Children and Adolescents with Autism Spectrum Disorders. https://app.cochat.ai/papers/10.1177/001440290707300301
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Bellini, S., & Akullian, J. (2007). A Meta-Analysis of Video Modeling and Video Self-Modeling Interventions for Children and Adolescents with Autism Spectrum Disorders. Exceptional Children. https://doi.org/10.1177/001440290707300301

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.