The Utility of Natural Language Samples for Assessing Communication and Language in Infants Referred with Early Signs of Autism

Abstract

Natural Language Sampling (NLS) offers clear potential for communication and language assessment, where other data might be difficult to interpret. We leveraged existing primary data for 18-month-olds showing early signs of autism, to examine the reliability and concurrent construct validity of NLS-derived measures coded from video—of child language, parent linguistic input, and dyadic balance of communicative interaction—against standardised assessment scores. Using Systematic Analysis of Language Transcripts (SALT) software and coding conventions, masked coders achieved good-to-excellent inter-rater agreement across all measures. Associations across concurrent measures of analogous constructs suggested strong validity of NLS applied to 6-min video clips. NLS offers benefits of feasibility and adaptability for validly quantifying emerging skills, and potential for standardisation for clinical use and rigorous research design.