Login / Signup

Using Nonword Repetition to Identify Language Impairment in Bilingual Children: A Meta-Analysis of Diagnostic Accuracy.

José A Ortiz
Published in: American journal of speech-language pathology (2021)
Purpose Nonword repetition has been endorsed as a less biased method of assessment for children from culturally and linguistically diverse backgrounds, but there are currently no systematic reviews or meta-analyses on its use with bilingual children. The purpose of this study was to evaluate diagnostic accuracy of nonword repetition in the identification of language impairment (LI) in bilingual children. Method Using a key word search of peer-reviewed literature from several large electronic databases, as well as ancestral and forward searches, 13 studies were identified that met the eligibility criteria. Studies were evaluated on the basis of quality of evidence, design characteristics, and reported diagnostic accuracy. A meta-regression analysis, based on study results, was conducted to identify task characteristics that may be associated with better classification accuracy. Results Diagnostic accuracy across studies ranged from poor to good. Bilingual children with LI performed with more difficulty on nonword repetition tasks than those with typical language. Quasi-universal tasks, which account for the phonotactic constraints of multiple languages, exhibited better diagnostic accuracy and resulted in less misidentification of children with typical language than language-specific tasks. Conclusions Evidence suggests that nonword repetition may be a useful tool in the assessment and screening of LI in bilingual children, though it should be used in conjunction with other measures. Quasi-universal tasks demonstrate the potential to further reduce assessment bias, but extant research is limited.
Keyphrases
  • young adults
  • systematic review
  • autism spectrum disorder
  • machine learning
  • meta analyses
  • deep learning
  • quality improvement
  • climate change
  • solid state