John J Heilmann1, Jon F Miller, Ann Nockerts. 1. Department of Communication Sciences and Disorders, Mail Stop #668, East Carolina University, Greenville, NC 27858-4353, USA. heilmannj@ecu.edu
Abstract
PURPOSE: Over the past 50 years, language sample analysis (LSA) has evolved from a powerful research tool that is used to document children's linguistic development into a powerful clinical tool that is used to identify and describe the language skills of children with language impairment. The The Systematic Analysis of Language Transcripts (SALT; J. F. Miller & A. Iglesias, 2008) Software Project has developed several databases of language samples from more than 6,000 typical speakers. This article presents an overview of the SALT databases and then demonstrates the power of these databases in classifying children with language impairment. METHOD: Conversational language samples were elicited from 244 children with language impairment who were between 3 and 13 years of age. Language production measures generated from these transcripts were compared to measures from 244 transcripts in the SALT conversational database. A series of discriminant function analyses were completed to document the sensitivity and specificity of the language sample measures. RESULTS: The language sample measures were effective in classifying children based on their language status, with correct identification of 78% of the children with language impairment and 85% of the children who were typically developing. CONCLUSION: The SALT databases provide a useful tool for the clinical management of children with language impairment.
PURPOSE: Over the past 50 years, language sample analysis (LSA) has evolved from a powerful research tool that is used to document children's linguistic development into a powerful clinical tool that is used to identify and describe the language skills of children with language impairment. The The Systematic Analysis of Language Transcripts (SALT; J. F. Miller & A. Iglesias, 2008) Software Project has developed several databases of language samples from more than 6,000 typical speakers. This article presents an overview of the SALT databases and then demonstrates the power of these databases in classifying children with language impairment. METHOD: Conversational language samples were elicited from 244 children with language impairment who were between 3 and 13 years of age. Language production measures generated from these transcripts were compared to measures from 244 transcripts in the SALT conversational database. A series of discriminant function analyses were completed to document the sensitivity and specificity of the language sample measures. RESULTS: The language sample measures were effective in classifying children based on their language status, with correct identification of 78% of the children with language impairment and 85% of the children who were typically developing. CONCLUSION: The SALT databases provide a useful tool for the clinical management of children with language impairment.
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