Evidence map›Paper›PMID 35814069›Full record

ArticleFrontiers in psychology2022

Measurement of Lexical Diversity in Children's Spoken Language: Computational and Conceptual Considerations.

Ji Seung Yang, Carly Rosvold, Nan Bernstein Ratner

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Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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10citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  7. Open Brain AI and language assessment.Frontiers in human neuroscience · 2024
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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ji Seung YangDepartment of Human Development and Quantitative Methodology, University of Maryland, College Park, College Park, MD, United States.
Carly RosvoldDepartment of Hearing and Speech Sciences, Program in Neuroscience and Cognitive Science, College Park, MD, United States.
Nan Bernstein RatnerDepartment of Hearing and Speech Sciences, Program in Neuroscience and Cognitive Science, College Park, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Type-Token Ratio (TTR), given its relatively simple hand computation, is one of the few LSA measures calculated by clinicians in everyday practice. However, it has significant well-documented shortcomings; these include instability as a function of sample size, and absence of clear developmental profiles over early childhood. A variety of alternative measures of lexical diversity have been proposed; some, such as Number of Different Words/100 (NDW) can also be computed by hand. However, others, such as Vocabulary Diversity (VocD) and the Moving Average Type Token Ratio (MATTR) rely on complex resampling algorithms that cannot be conducted by hand. To date, no large-scale study of all four measures has evaluated how well any capture typical developmental trends over early childhood, or whether any reliably distinguish typical from atypical profiles of expressive child language ability. Materials and Methods: We conducted linear and non-linear regression analyses for TTR, NDW, VocD, and MATTR scores for samples taken from 946 corpora from typically developing preschool children (ages 2-6 years), engaged in adult-child toy play, from the Child Language Data Exchange System (CHILDES). These were contrasted with 504 samples from children known to have delayed expressive language skills (total Results: Only VocD showed significantly different mean scores between the typically -developing children and delayed developing children group. Using TTR would actually misdiagnose typical children and miss children with known language impairment. However, computation of VocD as a function of toy interactions was significant and emerges as a further caution in use of lexical diversity as a valid proxy index of children's expressive vocabulary skill. Discussion: This large scale statistical comparison of computer-implemented algorithms for expressive lexical profiles in young children with traditional, hand-calculated measures showed that only VocD met criteria for evidence-based use in LSA. However, VocD was impacted by sample elicitation context, suggesting that non-linguistic factors, such as engagement with elicitation props, contaminate estimates of spoken lexical skill in young children. Implications and suggested directions are discussed.

Indexed as

child languagecomputer-assistedexpressivelanguage sample analysislexical

Identifiers

PMID35814069
PMCPMC9257278

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.