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Predicting Intentional Communication in Preverbal Preschoolers with Autism Spectrum Disorder.

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Title: Predicting Intentional Communication in Preverbal Preschoolers with Autism Spectrum Disorder.
Authors: Sandbank, Micheal, Woynaroski, Tiffany, Watson, Linda, Gardner, Elizabeth, Keçeli Kaysili, Bahar, Yoder, Paul
Source: Journal of Autism & Developmental Disorders. Jun2017, Vol. 47 Issue 6, p1581-1594. 14p. 4 Charts.
Subjects: Autism, Communication, Language acquisition, Longitudinal method, Sign language
Abstract: Intentional communication has previously been identified as a value-added predictor of expressive language in preverbal preschoolers with autism spectrum disorder. In the present study, we sought to identify value-added predictors of intentional communication. Of five theoretically-motivated putative predictors of intentional communication measured early in the study (at study entry and 4 months after), three had significant zero-order correlations with later intentional communication (12 months after study entry) and were thus added to a linear model that predicted later intentional communication scores controlling for initial intentional communication scores at study entry. After controlling for initial intentional communication, early motor imitation was the only predictor that accounted for a significant amount of variance in children's later intentional communication. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Predicting Intentional Communication in Preverbal Preschoolers with Autism Spectrum Disorder.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Autism+%26+Developmental+Disorders%22">Journal of Autism & Developmental Disorders</searchLink>. Jun2017, Vol. 47 Issue 6, p1581-1594. 14p. 4 Charts.
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  Data: <searchLink fieldCode="DE" term="%22Autism%22">Autism</searchLink><br /><searchLink fieldCode="DE" term="%22Communication%22">Communication</searchLink><br /><searchLink fieldCode="DE" term="%22Language+acquisition%22">Language acquisition</searchLink><br /><searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Sign+language%22">Sign language</searchLink>
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  Label: Abstract
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  Data: Intentional communication has previously been identified as a value-added predictor of expressive language in preverbal preschoolers with autism spectrum disorder. In the present study, we sought to identify value-added predictors of intentional communication. Of five theoretically-motivated putative predictors of intentional communication measured early in the study (at study entry and 4 months after), three had significant zero-order correlations with later intentional communication (12 months after study entry) and were thus added to a linear model that predicted later intentional communication scores controlling for initial intentional communication scores at study entry. After controlling for initial intentional communication, early motor imitation was the only predictor that accounted for a significant amount of variance in children's later intentional communication. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Autism & Developmental Disorders is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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