Language is a universal human ability, acquired readily by young children, who otherwise struggle with many basics of survival. And yet, language ability is variable across individuals. Naturalistic and experimental observations suggest that children’s linguistic skills vary with factors like socioeconomic status and children’s gender. But which factors really influence children’s day-to-day language use? Here, we leverage speech technology in a big-data approach to report on a unique cross-cultural and diverse data set: >2,500 d-long, child-centered audio-recordings of 1,001 2- to 48-mo-olds from 12 countries spanning six continents across urban, farmer-forager, and subsistence-farming contexts. As expected, age and language-relevant clinical risks and diagnoses predicted how much speech (and speech-like vocalization) children produced. Critically, so too did adult talk in children’s environments: Children who heard more talk from adults produced more speech. In contrast to previous conclusions based on more limited sampling methods and a different set of language proxies, socioeconomic status (operationalized as maternal education) was not significantly associated with children’s productions over the first 4 y of life, and neither were gender or multilingualism. These findings from large-scale naturalistic data advance our understanding of which factors are robust predictors of variability in the speech behaviors of young learners in a wide range of everyday contexts.

Background: The lack of available measures that can reliably characterize early developmental skills in children with neurogenetic syndromes (NGS) poses a significant challenge for research on early development in these populations. Although syndrome-specific measures may sometimes be necessary, a more cost- and time-efficient solution would be to identify existing measures that are appropriate for use in special populations or optimize existing measures to be used in these groups. Reliability is an important metric of psychometric rigor to consider when auditing and optimizing assessment tools for NGS. In this study, we use Generalizability Theory, an extension of classical test theory, as a novel approach for more comprehensively characterizing the reliability of existing measures and making decisions about their use in the field of NGS research. Methods: We conducted generalizability analyses on a popular early social communication screener, the Communication and Symbolic Behavior Scales—Infant-Toddler Checklist (CSBS-ITC), collected on 172 children (41 Angelman syndrome, 30 Prader-Willi syndrome, 42 Williams syndrome, 59 low-risk controls). Results: Overall, the CSBS-ITC demonstrated at least adequate reliability in the NGS groups included in this study, particularly for the Prader-Willi and Williams syndrome groups. However, the sources of systematic error variance in the CSBS-ITC varied greatly between the low-risk control and NGS groups. Moreover, as unassessed in previous research, the CSBS-ITC demonstrated substantial differences in variance sources among the NGS groups. Reliability of CSBS-ITC scores was highest when averaging across all measurement points for a given child and was generally similar or better in the NGS groups compared to the low-risk control group. Conclusions: Our findings suggest that the CSBS-ITC communicates different information about the reliability of stability versus change, in low-risk control and NGS samples, respectively, and that psychometric approaches like Generalizability Theory can provide more complete information about the reliability of existing measures and inform decisions about how measures are used in research on early development in NGS.

Language is a universal human ability, acquired readily by young children who otherwise struggle with many basics of survival1,2. And yet, language is variable across individuals. Behavioral and experimental observations suggest that children’s linguistic skills vary with factors like socioeconomic status3, children’s gender4, and multilingualism5. But which factors really influence children’s day-to-day language use? Here we leverage speech technology in a big-data approach to report on a unique cross-cultural and diverse data set: >2,500 day-long, child-centered audio-recordings of 1,001 2- to 48-month-olds from 12 countries spanning 6 continents across urban, farmer-forager, and subsistence-farming contexts. As expected, age and language-relevant clinical risks and diagnoses6 strongly correlated with how much speech (and speech-like vocalization) children produced. Critically, so too did adult talk in children’s environments: Children who heard less talk from adults produced less speech. In contrast to previous conclusions based on more limited sampling methods and a different set of language proxies, socioeconomic status, child gender, and multilingualism were not associated with children’s productions over the first four years of life. These findings from large-scale naturalistic data advance our understanding of what factors are robust predictors of variability in language behaviors of young learners in a wide range of everyday contexts.