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Sentence diversity (Hadley et al., 2018)

dataset
posted on 01.03.2018 by Pamela A. Hadley, Megan M. McKenna, Matthew Rispoli
Purpose: This clinical focus article describes how to assess and when to target diverse, simple sentences as part of early language intervention.
Method: The theoretical foundations and clinical motivations for assessing sentence diversity based on unique combinations of subjects and verbs are explained, followed by a description of how to compute the measure. Sentence diversity is then related to familiar developmental measures of lexical diversity, utterance length, and grammatical complexity in a sample of 40 typically developing toddlers at 30 months of age. Descriptive and correlational analyses are used to demonstrate how sentences become more diverse as utterances also become longer and more complex.
Conclusions: The ability to produce simple sentences with diverse subject–verb combinations is proposed as a general developmental expectation for toddlers at 30 months of age. All 40 children produced at least 10 different subject–verb combinations in 30 min of parent–toddler conversation. Sentence diversity is also associated with familiar developmental measures. Recommendations are provided for using the measure of sentence diversity to inform treatment planning and monitor progress for young children with language disorders.

Supplemental Material S1. Computing sentence diversity.

Supplemental Material S2. Subject diversity by four levels of mean length of utterance (MLU).

Supplemental Material S3. Index of Productive Syntax items and their relation to English sentence structure.

Hadley, P. A., McKenna, M. M., & Rispoli, M. (2018). Sentence diversity in early language development: Recommendations for target selection and progress monitoring. American Journal of Speech-Language Pathology, 27, 536–552. https://doi.org/10.1044/2017_AJSLP-17-0098

Funding

The database used in this article was collected as part of BCS-082251, NSF awarded to Matthew Rispoli. Portions of the data analysis were based on Megan McKenna’s master’s thesis, completed as a graduate student at the University of Illinois, and partially supported by a 2013 ASHA SPARC Award.

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