Taking Language Samples Home: Feasibility, Reliability, and Validity of Child Language Samples Conducted Remotely With Video Chat Versus In-Person - Manning, Brittany L.; Harpole, Alexandra; Harriott, Emily M.; Postolowicz, Kamila; Norton, Elizabeth S. (Journal of speech, language, and hearing research, 2020)
Purpose: There has been increased interest in using telepractice for involving more diverse children in research and clinical services, as well as when in-person assessment is challenging, such as during COVID-19. Little is known, however, about the feasibility, reliability, and validity of language samples when conducted via telepractice. Method: Child language samples from parent-child play were recorded either in person in the laboratory or via video chat at home, using parents' preferred commercially available software on their own device. Samples were transcribed and analyzed using Systematic Analysis of Language Transcripts software. Analyses compared measures between-subjects for 46 dyads who completed video chat language samples versus 16 who completed in-person samples; within-subjects analyses were conducted for a subset of 13 dyads who completed both types. Groups did not differ significantly on child age, sex, or socioeconomic status. Results: The number of usable samples and percent of utterances with intelligible audio signal did not differ significantly for in-person versus video chat language samples. Child speech and language characteristics (including mean length of utterance, type-token ratio, number of different words, grammatical errors/omissions, and child speech intelligibility) did not differ significantly between in-person and video chat methods. This was the case for between-group analyses and within-child comparisons. Furthermore, transcription reliability (conducted on a subset of samples) was high and did not differ between in-person and video chat methods. Conclusions: This study demonstrates that child language samples collected via video chat are largely comparable to in-person samples in terms of key speech and language measures. Best practices for maximizing data quality for using video chat language samples are provided.