Predicting Longitudinal Change in Language Production and Comprehension in Individuals With Down Syndrome Hierarchical Linear Modeling Research Article
Research Article  |   October 01, 2002
Predicting Longitudinal Change in Language Production and Comprehension in Individuals With Down Syndrome
 
Author Affiliations & Notes
  • Robin S. Chapman, PhD
    Waisman Center University of Wisconsin-Madison
  • Linda J. Hesketh
    Waisman Center University of Wisconsin-Madison
  • Doris J. Kistler
    Waisman Center University of Wisconsin-Madison
  • Contact author: Robin Chapman, PhD, Waisman Center, 1500 Highland Avenue, Madison, WI 53705. E-mail: chapman@waisman.wisc.edu
  • * Currently affiliated with the University of Oregon-Eugene
    Currently affiliated with the University of Oregon-Eugene×
Article Information
Special Populations / Genetic & Congenital Disorders / Research Issues, Methods & Evidence-Based Practice / Attention, Memory & Executive Functions / Language / Research Articles
Research Article   |   October 01, 2002
Predicting Longitudinal Change in Language Production and Comprehension in Individuals With Down Syndrome
Journal of Speech, Language, and Hearing Research, October 2002, Vol. 45, 902-915. doi:10.1044/1092-4388(2002/073)
History: Received September 25, 2001 , Accepted April 9, 2002
 
Journal of Speech, Language, and Hearing Research, October 2002, Vol. 45, 902-915. doi:10.1044/1092-4388(2002/073)
History: Received September 25, 2001; Accepted April 9, 2002
Web of Science® Times Cited: 68

Longitudinal change in syntax comprehension and production skill, measured four times across a 6-year period, was modeled in 31 individuals with Down syndrome who were between the ages of 5 and 20 years at the start of the study. Hierarchical Linear Modeling was used to fit individual linear growth curves to the measures of syntax comprehension (TACL-R) and mean length of spontaneous utterances obtained in 12-min narrative tasks (MLU-S), yielding two parameters for each participant's comprehension and production: performance at study start and growth trajectory. Predictor variables were obtained by fitting linear growth curves to each individual's concurrent measures of nonverbal visual cognition (Pattern Analysis subtest of the Stanford-Binet), visual short-term memory (Bead Memory subtest), and auditory short-term memory (digit span), yielding two individual predictor parameters for each measure: performance at study start and growth trajectory. Chronological age at study start (grand-mean centered), sex, and hearing status were also taken as predictors. The best-fitting HLM model of the comprehension parameters uses age at study start, visual short-term memory, and auditory short-term memory as predictors of initial status and age at study start as a predictor of growth trajectory. The model accounted for 90% of the variance in intercept parameters, 79% of the variance in slope parameters, and 24% of the variance at level 1. The same predictors were significant predictors of initial status in the best model for production, with no measures predicting slope. The model accounted for 81% of the intercept variance and 43% of the level 1 variance. When comprehension parameters are added to the predictor set, the best model, accounting for 94% of the intercept and 22% of the slope variance, uses only comprehension at study start as a predictor of initial status and comprehension slope as a predictor of production slope. These results reflect the fact that expressive language acquisition continues in adolescence and is predicted by syntax comprehension and its growth trajectory.

Acknowledgments
This research was supported by NIH R01 HD23353 to Robin S. Chapman, co-funded by the National Institute of Child Health and Human Development and the National Down Syndrome Society. We thank the participants and parents. A preliminary version of this work was presented at the Gatlinburg Research Conference on Mental Retardation, March 2000, Charleston, SC.
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