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Research Article
3 July 2024

Should We Stop Using Lexical Diversity Measures in Children's Language Sample Analysis?

Publication: American Journal of Speech-Language Pathology
Volume 33, Number 4
Pages 1986-2001

Abstract

Purpose:

Prior work has identified weaknesses in commonly used indices of lexical diversity in spoken language samples, such as type–token ratio (TTR) due to sample size and elicitation variation, we explored whether TTR and other diversity measures, such as number of different words/100 (NDW), vocabulary diversity (VocD), and the moving average TTR would be more sensitive to child age and clinical status (typically developing [TD] or developmental language disorder [DLD]) if samples were obtained from standardized prompts.

Method:

We utilized archival data from the norming samples of the Test of Narrative Language and the Edmonton Narrative Norms Instrument. We examined lexical diversity and other linguistic properties of the samples, from a total of 1,048 children, ages 4–11 years; 798 of these were considered TD, whereas 250 were categorized as having a language learning disorder.

Results:

TTR was the least sensitive to child age or diagnostic group, with good potential to misidentify children with DLD as TD and TD children as having DLD. Growth slopes of NDW were shallow and not very sensitive to diagnostic grouping. The strongest performing measure was VocD. Mean length of utterance, TNW, and verbs/utterance did show both good growth trajectories and ability to distinguish between clinical and typical samples.

Conclusions:

This study, the largest and best controlled to date, re-affirms that TTR should not be used in clinical decision making with children. A second popular measure, NDW, is not measurably stronger in terms of its psychometric properties. Because the most sensitive measure of lexical diversity, VocD, is unlikely to gain popularity because of reliance on computer-assisted analysis, we suggest alternatives for the appraisal of children's expressive vocabulary skill.

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Information & Authors

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Published In

American Journal of Speech-Language Pathology
Volume 33Number 4July 2024
Pages: 1986-2001
PubMed: 38838249

History

  • Received: Dec 7, 2023
  • Revised: Mar 9, 2024
  • Accepted: Apr 11, 2024
  • Published online: Jun 5, 2024
  • Published in issue: Jul 3, 2024

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Authors

Affiliations

Hearing and Speech Sciences, Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD
Author Contributions: Conceptualization, Data curation, Formal analysis, Writing – original draft, and Writing – review & editing.
Youngjin Han
Human Development and Quantitative Methodology, University of Maryland, University of Maryland, College Park, MD
Author Contributions: Data curation, Formal analysis, and Writing – review & editing.
Ji Seung Yang
Human Development and Quantitative Methodology, University of Maryland, University of Maryland, College Park, MD
Author Contributions: Conceptualization, Data curation, Formal analysis, and Writing – review & editing.

Notes

Disclosure: The first author is a consultant to federal grants that support the development of the freely distributed CLAN utilities used in data analysis reported here (see support). The other authors have declared that no competing financial or nonfinancial interests existed at the time of publication.
Correspondence to Nan Bernstein Ratner: [email protected]
Editor-in-Chief: Erinn H. Finke
Editor: Sean M. Redmond

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