Journal of Speech Language and Hearing Research · 2007 · 481 citations · 56 references
EducationSpecial EducationSpeech-language PathologyChild DevelopmentCognitive DevelopmentCommunication DisordersDevelopmental Language DisorderLanguage DisordersLanguage ImpairmentLanguage-based Learning DisabilitiesNeurodevelopmental DisordersWorking MemoryChild Language DisordersLanguage Battery AssessmentLanguage Development DisordersLatent Regression ModelsMultilevel Latent Variable ModelsNested ModelsProcessing Speed DeficitsSpeed
Children with language impairment often score lower than typically developing peers on processing speed and working memory measures. The study investigated whether processing speed and working memory account for language impairment. Using confirmatory factor analyses on a battery of processing speed and working memory tasks administered to 14‑year‑old children with LI and controls, the authors compared nested models, including one with a general speed factor. Speed and working memory emerged as separable factors, with both verbal and nonverbal processing contributing; regression analyses explained 62 % of language score variance, with verbal working memory having the largest effect, suggesting processing factors help explain language disorders.
Purpose Children with language impairment (LI) often perform below the level of typically developing peers on measures of both processing speed and working memory. This study examined the relationship between these 2 types of measures and attempted to determine whether such measures can account for the LI itself. Method Fourteen-year-old children with LI and their typically developing peers participated in a wide range of processing speed and working memory tasks and were administered a comprehensive language test battery. Confirmatory factor analyses were used to compare 3 nested models designed to examine the dimensionality of the speed and working memory measures. A model that included a general speed factor was also evaluated. Results The models meeting our evaluation criteria treated speed and working memory as separable factors. Furthermore, nonverbal as well as verbal processing factors emerged from these analyses. Latent variable regression analyses showed that each of the appropriate models accounted for 62% of the variance in the children’s concurrent composite language test scores, with verbal working memory making the largest contribution. Conclusions These findings shed light on the relationship among different types of processing and suggest that processing factors can contribute to the understanding of language disorders.
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