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Rehabilitation Dataset Directory: Dataset Profile
Dataset: Requests for Assistance with Adaptive Switches from Individuals with Severe Communication Impairments, 2007 to 2012 ()
|Dataset Full Name||Requests for Assistance with Adaptive Switches from Individuals with Severe Communication Impairments, 2007 to 2012|
The Requests for Assistance with Adaptive Switches from Individuals with Severe Communication Impairments study is one of the three projects in the Communication of People with Mental Retardation (MR) , 2006 to 2012 Series, which focuses on identifying participant variables that predict success in increasing communication skills of individual with intellectual disabilities. The study data were collected to show how children and adults with profound multiple impairments can learn to request assistance with adaptive switches and examine the change from pre-intentional to intentional symbolic communication.
The study examined 77 respondents, ages 2 through 71, and administered three assessments:
Assistive technology (AT), Communication disability, Multiple disability
United States Department of Health and Human Services:
National Institutes of Health
Eunice Kennedy Shriver National Institute of Child
Health and Human Development
|Health Conditions/Disability Measures|
Criteria for participant inclusion in study: Communication impairment, Visual disability, Intellectual disability, Special equipment use/assistive technology
|Measures/Outcomes of Interest|
Communication, Assistive technology
Respondents were convenience sampled through their participation in a requesting-via adaptive-switch intervention. Respondents were between the ages 2 to 71 years with severe and multiple disabilities and suspected vision impairments.
77 respondents, ages 2 through 71
|Unit of Observation||
California, Kansas, and Washington
|Data Collection Mode||
|Data Collection Frequency||
The Communication Complexity Scale (CCS) was administered up to three points in time:
|Strengths and Limitations|
Up to three measurements of the Communication Complexity Scale (CCS) were administered over time.
Unique sample of individuals with severe multiple disabilities and vision impairments.
Sample of convenience may not be representative of the population as a whole.
|Data Access Requirements||
Public Use Dataset (de-identified)
Data Use agreement, No cost (Restricted use version-contains original demographic information)
Codebook contains raw frequencies:
This study is one of the three projects in the Communication of People with MR, 2006 to 2012 Series. The other two studies in this series are:
ICPSR related publications list:
Saunders, Muriel D., Saunders, Richard R.. Teaching individuals to signal for assistance in a timely manner. Behavioral Interventions.27, (4), 193-206.2012.
Saunders, Muriel D., Saunders, Richard R.. Innovation of a reinforcer preference assessment with the difficult to test. Research in Developmental Disabilities.32, (5), 1572-1579.2011.
For more information on the Communication Complexity Scale see:
Brady, N., Fleming, K., Thiemann-Bourque, K., Olswang, L., Dowden, P., Saunders, M., and Marquis, J. (2012). Development of the Communication Complexity Scale. American Journal of Speech-Language Pathology, 21(1), 16-28.
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The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).
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Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.
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