Evidence map›Paper›PMID 32402218›Full record

ArticleJournal of speech, language, and hearing research : JSLHR2020

Predicting Expressive Language From Early Vocalizations in Young Children With Autism Spectrum Disorder: Which Vocal Measure Is Best?

Jena McDaniel, Paul Yoder, Annette Estes, Sally J Rogers

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of speech, language, and hearing research : JSLHR, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. The use of Language ENvironment Analysis in autism research: A systematic review.Autism : the international journal of research and practice · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 4 institutions in 1 country.

Jena McDanielLife Span Institute, University of Kansas, Lawrence.
Paul YoderDepartment of Special Education, Vanderbilt University, Nashville, TN.
Annette EstesDepartment of Speech and Hearing Sciences, University of Washington, Seattle.
Sally J RogersDepartment of Psychiatry and Behavioral Sciences, University of California, Davis.
University of California, Davis · USUniversity of Kansas · USUniversity of Washington · USVanderbilt University · US

Funding

Intervention effects of intensity and delivery style for toddlers with ASDR01MH100030 · NIMH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI ROGERS, SALLY J · 2013 to 2017
$13.4M
Research Project: Sensory and Multisensory Contributions to AutismU54HD083211 · NICHD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CUTTING, LAURIE E · 2015 to 2019
$7.1M
NICHD NIH HHS U54 HD083211NIMH NIH HHS R01 MH100030
6 · The paper itself

Abstract

Purpose This study was designed to test the incremental validity of more expensive vocal development variables relative to less expensive variables for predicting later expressive language in children with autism spectrum disorder (ASD). We devote particular attention to the added value of coding the quality of vocalizations over the quantity of vocalizations because coding quality adds expense to the coding process. We are also interested in the added value of more costly human-coded vocal variables relative to those generated through automated analyses. Method Eighty-seven children with ASD aged 13-30 months at study initiation participated. For quantity of vocalizations, we derived one variable from human coding of brief communication samples and one from an automated process for daylong naturalistic audio samples. For quality of vocalizations, we derived four human-coded variables and one automated variable. A composite expressive language measure was derived at study entry, and 6 and 12 months later. The 12 months-centered intercept of a simple linear growth trajectory was used to quantify later expressive language. Results When statistically controlling for human-coded or automated quantity of vocalization variables, human-coded quality of vocalization variables exhibited incremental validity for predicting later expressive language skills. Human-coded vocal variables also predicted later expressive language skills when controlling for the analogous automated vocal variables. Conclusion In sum, these findings support devoting resources to human coding of the quality of vocalizations from communication samples to predict later expressive language skills in young children with ASD despite the greater costs of deriving these variables. Supplemental Material https://doi.org/10.23641/asha.12276458.

Indexed as

Autism Spectrum DisorderLanguage Development DisordersChildChild LanguageChild, PreschoolCommunicationHumansLanguageLanguage Development

Identifiers

PMID32402218
PMCPMC7842121
OpenAlexW3025744815

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.