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Socioeconomic status correlates with measures of Language Environment Analysis (LENA) system: a meta-analysis

Published online by Cambridge University Press:  25 June 2021

Leonardo PIOT ,
Naomi HAVRON Open the ORCID record for Naomi HAVRON [Opens in a new window]  and
Alejandrina CRISTIA
Leonardo PIOT*
Affiliation:
Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, France
Naomi HAVRON
Affiliation:
Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, France University of Haifa, Israel
Alejandrina CRISTIA
Affiliation:
Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, France
*
Address for correspondence: Leonardo Piot, 29 rue d'Ulm, 75005, Paris, France. E-mail: [email protected]
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Abstract

Using a meta-analytic approach, we evaluate the association between socioeconomic status (SES) and children's experiences measured with the Language Environment Analysis (LENA) system. Our final analysis included 22 independent samples, representing data from 1583 children. A model controlling for LENATM measures, age and publication type revealed an effect size of rz= .186, indicating a small effect of SES on children's language experiences. The type of LENA metric measured emerged as a significant moderator, indicating stronger effects for adult word counts than child vocalization counts. These results provide important evidence for the strength of association between SES and children's everyday language experiences as measured with an unobtrusive recording analyzed automatically in a standardized fashion.

Keywords

SESLENAexperiencesproductioninputlanguage environmentmeta-analysis
Type
Brief Research Report
Information
Journal of Child Language , Volume 49 , Issue 5 , September 2022 , pp. 1037 - 1051
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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References

Adams, K. A., Marchman, V. A., Loi, E. C., Ashland, M. D., Fernald, A., & Feldman, H. M. (2018). Caregiver talk and medical risk as predictors of language outcomes in full term and preterm toddlers. Child Development, 89(5), 1674–1690.CrossRefGoogle ScholarPubMed
Beecher, C. C., & Van Pay, C. K. (2019). Small talk: A community research collaboration to increase parental provision of language to children. Child & youth care forum, 1–26. Springer.Google Scholar
Bergelson, E. (2017). Bergelson seedlings home bank corpus.Google Scholar
Brushe, M. E., Lynch, J. W., Reilly, S., Melhuish, E., & Brinkman, S. A. (2020). How many words are australian children hearing in the first year of life? BMC Pediatrics, 20(1), 1–9.CrossRefGoogle ScholarPubMed
Caselli, M. C., Bates, E., Casadio, P., Fenson, J., Fenson, L., Sanderl, L., & Weir, J. (1995). A cross-linguistic study of early lexical development. Cognitive Development, 10(2), 159–199.CrossRefGoogle Scholar
Christakis, D. A., Lowry, S. J., Goldberg, G., Violette, H., & Garrison, M. M. (2019). Assessment of a parent-child interaction intervention for language development in children. JAMA Network Open, 2(6), e195738–e195738.CrossRefGoogle ScholarPubMed
Cristia, A., Bulgarelli, F., & Bergelson, E. (2020). Accuracy of the language environment analysis system segmentation and metrics: A systematic review. Journal of Speech, Language, and Hearing Research, 63(4), 1093–1105.CrossRefGoogle ScholarPubMed
Dailey, S., & Bergelson, E. (2021). Language input to infants of different socioeconomic statuses: A quantitative meta-analysis. Developmental Science, Registered report.Google ScholarPubMed
d'Apice, K., Latham, R. M., & Stumm, S. von. (2019). A naturalistic home observational approach to children's language, cognition, and behavior. Developmental Psychology, 55(7), 1414.CrossRefGoogle ScholarPubMed
Dudley-Marling, C., & Lucas, K. (2009). Pathologizing the language and culture of poor children. Language Arts, 86(5), 362–370.Google Scholar
Dupas, P., Duflo, E., & Kremer, M. (2016). Estimating the impact and cost-effectiveness of expanding secondary education in ghana.CrossRefGoogle Scholar
Dwyer, A., Jones, C., Davis, C., Kitamura, C., & Ching, T. Y. (2019). Maternal education influences australian infants’ language experience from six months. Infancy, 24(1), 90–100.CrossRefGoogle ScholarPubMed
Ferjan-Ramírez, N., Lytle, S. R., & Kuhl, P. K. (2020). Parent coaching increases conversational turns and advances infant language development. Proceedings of the National Academy of Sciences, 117(7), 3484–3491.CrossRefGoogle ScholarPubMed
Flood, M. M. (2015). Quality versus quantity: An investigation of the impact of home language and maternal education level on young children's vocabulary size. UNIVERSITY OF WISCONSIN-MADISON.Google Scholar
Ganek, H., Smyth, R., Nixon, S., & Eriks-Brophy, A. (2018). Using the language environment analysis (lena) system to investigate cultural differences in conversational turn count. Journal of Speech, Language, and Hearing Research, 61(9), 2246–2258.CrossRefGoogle ScholarPubMed
Gilkerson, J., & Richards, J. A. (2008). The lena natural language study. Boulder, CO: LENA Foundation.Google Scholar
Gilkerson, J., Richards, J. A., Warren, S. F., Montgomery, J. K., Greenwood, C. R., Kimbrough Oller, D., Hansen, J. H. L., & Paul, T. D. (2017). Mapping the early language environment using all-day recordings and automated analysis. American Journal of Speech-Language Pathology, 26(2), 248–265.CrossRefGoogle ScholarPubMed
Glick, P. C. (1976). Living arrangements of children and young adults. Journal of Comparative Family Studies, 7(2), 321–333.CrossRefGoogle Scholar
Golinkoff, R. M., Hoff, E., Rowe, M. L., Tamis-LeMonda, C. S., & Hirsh-Pasek, K. (2019). Language matters: Denying the existence of the 30-million-word gap has serious consequences. Child Development, 90(3), 985–992.CrossRefGoogle ScholarPubMed
Hart, B., & Risley, T. R. (1995). Meaningful differences in the everyday experience of young american children. Paul H Brookes Publishing.Google Scholar
Hoff, E. (2003). The specificity of environmental influence: Socioeconomic status affects early vocabulary development via maternal speech. Child Development, 74(5), 1368–1378.CrossRefGoogle ScholarPubMed
Huttenlocher, J., Waterfall, H., Vasilyeva, M., Vevea, J., & Hedges, L. V. (2010). Sources of variability in children's language growth. Cognitive Psychology, 61(4), 343–365.CrossRefGoogle ScholarPubMed
Law, J., Charlton, J., & Rush, R. (2018). Home-start early speech and language study: Phase 1 evaluation report. Home-Start.Google Scholar
Lease-Johnson, E. (2018). Redefining the word gap from a cumulative risk perspective (PhD thesis). UNIVERSITY OF MINNESOTA.Google Scholar
Leung, C. Y., Hernandez, M. W., & Suskind, D. L. (2020). Enriching home language environment among families from low-ses backgrounds: A randomized controlled trial of a home visiting curriculum. Early Childhood Research Quarterly, 50, 24–35.CrossRefGoogle Scholar
McGillion, M., Pine, J. M., Herbert, J. S., & Matthews, D. (2017). A randomised controlled trial to test the effect of promoting caregiver contingent talk on language development in infants from diverse socioeconomic status backgrounds. Journal of Child Psychology and Psychiatry, 58(10), 1122–1131.CrossRefGoogle ScholarPubMed
Merz, E. C., Maskus, E. A., Melvin, S. A., He, X., & Noble, K. G. (2020). Socioeconomic disparities in language input are associated with children's language-related brain structure and reading skills. Child Development, 91(3), 846–860.CrossRefGoogle ScholarPubMed
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The prisma statement. PLOS Medicine, 6(7), 1–6. https://doi.org/10.1371/journal.pmed.1000097CrossRefGoogle ScholarPubMed
Orena, A. J., Byers-Heinlein, K., & Polka, L. (2019). Reliability of the language environment analysis recording system in analyzing french–english bilingual speech. Journal of Speech, Language, and Hearing Research, 62(7), 2491–2500.CrossRefGoogle ScholarPubMed
Oller, D. K., Eilers, R., Basinger, D., Steffens, M., & Urbano, R. (1995). Poverty and speech precursor development. First Language, 15(2), 167–187.CrossRefGoogle Scholar
Piot, L., Havron, N., & Cristia, A. (2020). Socioeconomic status correlates with measures of Language ENvironment Analysis (LENA) system: a meta-analysis. Retrieved from https://osf.io/rw6ve/?view_only=eddcd33627d940c8ab51f5b7ef8e252dGoogle Scholar
R Core Team (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/Google Scholar
Romeo, R. R., Leonard, J. A., Mackey, A. P., Rowe, M. L., & Gabrieli, J. D. E. (2019). Neural correlates of the “word gap”: Children's language experience is associated with brain structure and function. SRCD.Google Scholar
Romeo, R. R., Leonard, J. A., Robinson, S. T., West, M. R., Mackey, A. P., Rowe, M. L., & Gabrieli, J. D. (2018). Beyond the 30-million-word gap: Children's conversational exposure is associated with language-related brain function. Psychological Science, 29(5), 700–710.CrossRefGoogle ScholarPubMed
Rothstein, H. R., Sutton, A. J., & Borenstein, M. (2005). Publication bias in meta-analysis. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments, 1–7.Google Scholar
Scaff, C. (2019). Beyond WEIRD: An interdisciplinary approach to language acquisition (PhD thesis).Google Scholar
Sperry, D. E., Sperry, L. L., & Miller, P. J. (2019). Reexamining the verbal environments of children from different socioeconomic backgrounds. Child Development, 90(4), 1303–1318.CrossRefGoogle ScholarPubMed
Sultana, N., Wong, L. L., & Purdy, S. C. (2020). Natural language input: Maternal education, socioeconomic deprivation, and language outcomes in typically developing children. Language, Speech, and Hearing Services in Schools, 51(4), 1049–1070.CrossRefGoogle ScholarPubMed
Swanson, M. R., Donovan, K., Paterson, S., Wolff, J. J., Parish-Morris, J., Meera, S. S., Watson, L. R., Estes, A. M., Marrus, N., Elison, J. T., Shen, M. D., McNeilly, H. B., MacIntyre, L., Zwaigenbaum, L., St John, T., Botteron, K., Dager, S., Piven, J., & IBIS Network (2019). Early language exposure supports later language skills in infants with and without autism. Autism Research, 12(12), 1784–1795. https://doi.org/10.1002/aur.2163CrossRefGoogle ScholarPubMed
VanDam, M. (2018). Cougar homebank corpus.Google Scholar
Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. Retrieved from https://www.jstatsoft.org/v36/i03/CrossRefGoogle Scholar
Wang, Y., Williams, R., Dilley, L., & Houston, D. M. (2020). A meta-analysis of the predictability of lena™ automated measures for child language development. Developmental Review, 57, 100921.CrossRefGoogle ScholarPubMed
Warlaumont, A. S., Pretzer, G. M., Walle, E., Mendoza, S., & Lopez, L. (2016). Warlaumont homebank corpus.Google Scholar
Weisleder, A., & Fernald, A. (2013). Talking to children matters: Early language experience strengthens processing and builds vocabulary. Psychological Science, 24(11), 2143–2152.CrossRefGoogle ScholarPubMed
Zegiob, L. E., Arnold, S., & Forehand, R. (1975). An examination of observer effects in parent-child interactions. Child Development, 46(2), 509–512. Retrieved from http://www.jstor.org/stable/1128149CrossRefGoogle Scholar