Diagnostic Accuracy of the Sampling Utterances and Grammatical Analysis Revised (SUGAR) Measures for Identifying...

Purpose The purpose of this study was twofold: (a) to determine the diagnostic accuracy of the four Sampling Utterances and Grammatical Analysis Revised (SUGAR) metrics, including total number of words, mean length of utterance_SUGAR, words per sentence, and clauses per sentence in differentiating children with language impairment (LI) from those with typical language development, and (b) to compare the average time to collect, transcribe, and analyze 50-utterance language samples for children with LI to those with typical language development. Method Participants were 306 children (LI, 36; typical language development, 270) who ranged in age from 3;0 (years;months) to 7;11. Fifty-utterance conversational language samples were obtained using a conversational protocol. The four SUGAR metrics were calculated from the samples. Results Cut scores of -1 SD for mean length of utterance_SUGAR and -1.25 cut score for clauses per sentence resulted in sensitivity of 97.22%, specificity of 82.96%, a positive likelihood ratio of 5.71, and a negative likelihood ratio of 0.03. On average, it took a total time of 20:20 min ( SD = 4:37, range: 13:11-30:25) to collect, transcribe, and analyze language samples for children with LI. Children with LI took significantly less time to produce 50 utterances, when compared to their typically developing peers. There were no significant differences in the time to transcribe and analyze language samples of children with LI compared to their typically developing peers. Conclusions The SUGAR metrics, in combination with other data sources (e.g., standardized testing, dynamic assessment, observation), can be used to identify preschool- and early elementary-aged children with LI. Furthermore, for children with LI, language sampling and analysis using the SUGAR method can be completed in approximately 20 min. The results of this study indicated the SUGAR measures can effectively and efficiently help in identifying LI. Supplemental Material https://doi.org/10.23641/asha.7728638.