Date of Award

5-2019

Degree Type

Creative Project

Degree Name

Master of Science (MS)

Department

Communicative Disorders and Deaf Education

First Advisor

Sandra Gillam

Second Advisor

Michelle Wilson

Abstract

Guo & Schneider (2016) explored different approaches to identifying grammatical impairments in 128 children developing typically (TL) and those with language impairments (LI) between the ages of 6 and 8. The measures that they explored included: calculating the finite verb morphology composite (FVMC), the number of errors per C-unit (Errors/CU), and the percent of grammatical C-units (PGCUs) in narrative samples. They found that all three outcome measures provided acceptable diagnostic accuracy when applied to six-year-olds, but PCGUs were found to be the most accurate tool with eight-year-olds.

The purpose of the current study was to analyze the narrative samples of 260 typically developing children ages 4-7 to determine typical scores for the previously listed grammatical measures at each age level. This knowledge may provide more information on the performance of typical children and how to accurately identify students with LI. This study extends the literature by testing whether the findings remain the same with addition data from children aged 6-7 and whether the findings extend to children aged 4 and 5 years.

The participants in this study included 260 children aged 4-7. They were selected as part of the normative sample for the Test of Narrative Language-2. The children were asked to produce stories in three varying contexts: a story retell after a model story, sequenced pictures for another story retell, and produce a story based on a picture after a model.

These stories were transcribed using Systematic Analysis of Language Transcripts (SALT). The transcriptions were double coded for grammaticality by two separate research assistants. Reliability was calculated by a graduate research assistant. The utterances were designated as grammatical or ungrammatical. Ungrammatical utterances were coded for the type of grammatical error present based on the model used by Guo & Schneider (2016).

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