Marking of Tense and Agreement in Language Samples by Children With and Without Specific Language Impairment in African American English and Southern White English: Evaluation of Scoring Approaches and Cut Scores Across Structures.
Janna B Oetting, Andrew M Rivière, Jessica R Berry and 3 others
PMID 33472006WHAT IT FOUND
Dialect-specific scoring and cut scores improved the accuracy of identifying language impairment in African American English and Southern White English speaking kindergartners.
Past tense marking was the most accurate single structure for diagnosis, though probes outperformed language samples for clinical efficiency.
Key findings
01Using dialect-specific cut scores with strategic scoring increased classification accuracy for identifying Specific Language Impairment compared to universal cut scores.
02Past tense marking yielded the highest levels of classification accuracy for distinguishing children with SLI from typically developing peers across both dialects.
03Dialect-informed probes were more efficient and sensitive to clinical status than language samples, which required significantly more time to elicit and analyze.
STILL TO COME
How it was doneWhat they foundWhat it means for SLPs
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What it does not show
The study focused only on kindergartners from one community in the rural South, so results may not generalize to other regions or age groups. Language samples contained insufficient numbers of some T/A structures, limiting the analysis of individual structures. Classification accuracy did not reach the 90% gold standard, meaning T/A measures should not be used alone for diagnosis. The study compared samples to probes but did not test the probes' validity in this specific sample cohort, relying on previous data.
Declared interests
Funded by the National Institute on Deafness and Other Communication Disorders (NIH). No commercial conflicts of interest reported.
The easy way to misread this
Do not use universal cut scores for tense and agreement assessment in dialect-speaking children, as this leads to misclassification. Also, do not assume that a single language sample is sufficient for diagnosis, as past tense alone or combined structures did not reach the 90% accuracy threshold required for a standalone diagnostic tool.