Predicting language performance from narrative language samples

Murphy, KImberley and Springle, Alisha and Sultani, Mollee and McIlraith, Autumn (2022) Predicting language performance from narrative language samples. Journal of Speech, Language, and Hearing Research, 65 (2). pp. 775-784. ISSN 1092-4388

Abstract

Purpose: Analysis of narrative language samples is a recommended clinical practice in the assessment of children's language skills, but we know little about how results from such analyses relate to overall oral language ability across the early school years. We examined the relations between language sample metrics from a short narrative retell, collected in kindergarten, and an oral language factor in grades kindergarten through 3. Our specific questions were to determine the extent to which metrics from narrative language sample analysis are concurrently related to language in kindergarten and predict language through Grade 3. Method: Participants were a sample of 284 children who were administered a narrative retell task in kindergarten and a battery of vocabulary and grammar measures in kindergarten through Grade 3. Language samples were analyzed for number of different words, mean length of utterance, and a relatively new metric, percent grammatical utterances (PGUs). Structural equation models were used to estimate the concurrent and longitudinal relationships. Results: The narrative language sample metrics were consistently correlated with the individual vocabulary and grammar measures as well as the language factor in each grade, and also consistently and uniquely predicted the language factor in each grade. Standardized path estimates in the structural equation models ranged from 0.20 to 0.39. Conclusions: This study found narrative language sample metrics to be predictive, concurrently and longitudinally, of a latent factor of language from kindergarten through Grade 3. These results further validate the importance of collecting and analyzing narrative language samples, to include PGU along with more traditional metrics, and point to directions for future research.