The State of Natural Language Sampling in Autism Research: A Scoping Review
Natural language sampling is popular in autism research, but wide method gaps and missing minimally speaking kids keep the data fractured.
01Research in Context
What this study did
Plate (2025) read 59 autism papers that used natural language sampling. The team wanted to see how researchers collect and report real-talk samples from autistic kids.
They counted who was studied, what settings were used, and how the talk was taped and scored.
What they found
Use of natural language sampling is growing fast, but every lab does it differently. Most studies leave out minimally or nonspeaking school-age children.
Papers rarely explain the exact same steps, so you cannot line up results across labs.
How this fits with other research
Feldman et al. (1999) warned that autism diagnosis reports were messy 25 years ago. Plate shows the same chaos now lives inside language sampling, so the field still needs standard checklists.
Pane et al. (2025) also published a 2025 scoping review on toy-play studies in autism. Both reviews found the same holes: young kids with limited speech are missing and methods vary too much.
Oates et al. (2023) found that autistic girls often look “better” on language tests than boys, but only when you use male norms. Plate adds that we rarely sample girls’ natural talk at all, so we may be double-blind to their real needs.
Why it matters
If you sample language in your practice, write down every step: where you sat, what toys were out, how long you waited. Share the script so the next BCBA can copy it. Push your team to include nonspeaking clients and to compare their talk with same-age peers, not just test norms. Small fixes like these turn scattered data into useful benchmarks for our whole field.
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02At a glance
03Original abstract
Caregiver reports and standardized assessments have been the primary methods used to study language development in autism. However, these forms of measurement are often coarse, complicated by floor effects and reporter bias, and limited by the fact that they only capture how children can use language at a single moment in time, rather than how children actually use language during everyday interactions. These limitations have led to recent calls for the use of natural language sampling (NLS) as a fine-grained, developmentally appropriate, and contextually relevant measure of everyday communication. The number of studies using NLS to study language in autism has increased substantially in the last 15 years, resulting in a wide array of sampling methods and measures. Given both the increasing prevalence of NLS methods in the autism literature and the variability in sampling approaches and measures, this scoping review addresses the following questions: 1. What populations have been studied using NLS?2. Which data collection methods are most prevalent in NLS research?3. How are measures of language derived from NLS? 1. What populations have been studied using NLS? 2. Which data collection methods are most prevalent in NLS research? 3. How are measures of language derived from NLS? Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a search for studies published in the last 15 years across three databases was conducted. After removing duplicates, 4,671 titles and abstracts were screened and 59 papers met inclusion criteria. Sample characteristics, natural language collection methods, and derived measures were extracted and tabled for each study. The most prevalent NLS methods and measures in autism language research are reviewed and the benefits and drawbacks of various methods are discussed. This scoping review highlights subgroups of the autistic population that have been underrepresented in NLS studies—in particular, minimally/nonspeaking school-aged autistic children. This article also examines means to collect a “naturalistic” sample of language. Notably, studies did not address whether autistic children exhibit different social communication skills when talking to different types of social partners. Broadly, research has underreported key methodological details, making comparisons across studies difficult. This review highlights the appropriate use of NLS across development in autism and makes recommendations for NLS future research. Additional work is needed to address the gaps described in this article and replicate previous findings to identify patterns of natural language across the literature.
Autism & Developmental Language Impairments, 2025 · doi:10.1177/23969415251341247