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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Related Experiment Video

Updated: Sep 19, 2025

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
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Modeling Longitudinal Trajectories of Word Production With the CDI.

Trevor K M Day1,2,3, Arielle Borovsky4, Donna Thal5

  • 1Institute of Child Development, University of Minnesota, Minneapolis, Minnesota, USA.

Developmental Science
|June 16, 2025
PubMed
Summary

This study presents two methods for analyzing parent-reported language development data from the MacArthur-Bates Communicative Development Inventories (CDI). We adjusted scores and applied growth curves to better understand vocabulary acquisition in children, including those with language disorders.

Keywords:
CDIgrowth curvelanguage acquisitionword learning

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Area of Science:

  • Child language acquisition research
  • Developmental psychology
  • Speech and language pathology

Background:

  • The MacArthur-Bates Communicative Development Inventories (CDI) are parent-report tools for assessing child language development.
  • Analyzing CDI word-inventory data presents statistical challenges, particularly with out-of-norm range or irregularly timed longitudinal data.

Purpose of the Study:

  • To develop and validate statistical methods for modeling MacArthur-Bates Communicative Development Inventories (CDI) data.
  • To adjust out-of-age-normed-range 'Words & Gestures' scores to 'Words & Sentences' scores.
  • To apply Gompertz growth curves to longitudinal CDI data, including accelerated longitudinal designs, to model vocabulary development and compare growth rates across different child cohorts.

Main Methods:

  • Statistical modeling of parent-reported language data from the MacArthur-Bates Communicative Development Inventories (CDI).
  • Score adjustment techniques for 'Words & Gestures' data to align with 'Words & Sentences' metrics.
  • Application of Gompertz growth curve modeling to longitudinal CDI data, accommodating varied individual timepoints.

Main Results:

  • Parent-reported 'Words & Gestures' CDI data can be reliably adjusted to 'Words & Sentences' scores.
  • Gompertz growth curves effectively model longitudinal vocabulary development from CDI data, yielding parameters like growth rate and age at maximum growth.
  • Children with speech, language, or learning/reading disabilities exhibited significantly lower vocabulary growth rates compared to typically developing children.

Conclusions:

  • The proposed methods offer robust approaches for analyzing complex CDI datasets, enhancing our understanding of child language acquisition.
  • Gompertz growth curve parameters provide valuable insights into vocabulary development trajectories and can differentiate between typically developing children and those with developmental language challenges.
  • These analytical advancements can inform future research on language development using longitudinal CDI data.