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Semin Speech Lang 2016; 37(02): 106-116
DOI: 10.1055/s-0036-1580740
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Using Language Sample Analysis in Clinical Practice: Measures of Grammatical Accuracy for Identifying Language Impairment in Preschool and School-Aged Children

Sarita Eisenberg
1   Department of Communication Sciences and Disorders, Montclair State University, Montclair, New Jersey
,
Ling-Yu Guo
2   University at Buffalo–The State University of New York, Buffalo, New York
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Publikationsdatum:
25. April 2016 (online)

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Abstract

This article reviews the existing literature on the diagnostic accuracy of two grammatical accuracy measures for differentiating children with and without language impairment (LI) at preschool and early school age based on language samples. The first measure, the finite verb morphology composite (FVMC), is a narrow grammatical measure that computes children's overall accuracy of four verb tense morphemes. The second measure, percent grammatical utterances (PGU), is a broader grammatical measure that computes children's accuracy in producing grammatical utterances. The extant studies show that FVMC demonstrates acceptable (i.e., 80 to 89% accurate) to good (i.e., 90% accurate or higher) diagnostic accuracy for children between 4;0 (years;months) and 6;11 in conversational or narrative samples. In contrast, PGU yields acceptable to good diagnostic accuracy for children between 3;0 and 8;11 regardless of sample types. Given the diagnostic accuracy shown in the literature, we suggest that FVMC and PGU can be used as one piece of evidence for identifying children with LI in assessment when appropriate. However, FVMC or PGU should not be used as therapy goals directly. Instead, when children are low in FVMC or PGU, we suggest that follow-up analyses should be conducted to determine the verb tense morphemes or grammatical structures that children have difficulty with.

Keywords

Finite verb morphology composite - percent grammatical utterances - diagnostic accuracy - language sample analysis
 

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