Using Language Sample Databases 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 ... Clinical Forum
Clinical Forum  |   January 01, 2010
Using Language Sample Databases
 
Author Affiliations & Notes
  • John J. Heilmann
    East Carolina University, Greenville, NC
  • Jon F. Miller
    University of Wisconsin—Madison
  • Ann Nockerts
    University of Wisconsin—Madison
  • Contact author: John J. Heilmann, Department of Communication Sciences and Disorders, Mail Stop #668, East Carolina University, Greenville, NC 27858-4353. E-mail: heilmannj@ecu.edu.
Article Information
Language Disorders / Clinical Forum
Clinical Forum   |   January 01, 2010
Using Language Sample Databases
Language, Speech, and Hearing Services in Schools, January 2010, Vol. 41, 84-95. doi:10.1044/0161-1461(2009/08-0075)
History: Received July 3, 2008 , Revised December 22, 2008 , Accepted February 18, 2009
 
Language, Speech, and Hearing Services in Schools, January 2010, Vol. 41, 84-95. doi:10.1044/0161-1461(2009/08-0075)
History: Received July 3, 2008; Revised December 22, 2008; Accepted February 18, 2009
Web of Science® Times Cited: 26

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.

ACKNOWLEDGMENTS
The SALT databases and project described in this article would not have been possible without the assistance of numerous collaborators and funding agencies from around the globe. Much of the work in collecting these databases was unfunded and was made possible by the tireless efforts of administrators and clinicians in the public schools. We would like to thank members of the Madison Metropolitan Public Schools for their assistance with the original conversational and narrative databases, the additional narrative retell data, and the data from the children with LI described in this study. We would also like to thank the SLPs in the San Diego and El Cajon school districts and colleagues at the University of Canterbury in New Zealand for their assistance in developing the databases. The bilingual narrative databases were made possible through collaboration with Aquiles Iglesias of Temple University and by Grants HD39521 “Oracy/Literacy Development of Spanish-speaking Children” and R305U010001 “Biological and Behavioral Variation in the Language Development of Spanish-speaking Children,” both of which were jointly funded by the National Institute of Child Health and Human Development, National Institutes of Health, and the U.S. Department of Education’s Institute of Education Sciences. The SALT Software Project is monitored by the University of Wisconsin—Madison Campus Conflict of Interest Committee to ensure that research interests are not compromised by business interests. Finally, a special thank you to Karen Andriacchi and all of the past and present members of the Language Analysis Laboratory who ensured that the thousands of language samples processed in the lab were transcribed accurately and organized efficiently.
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