Caregiver–child verbal interactions in child care: A buffer against poor language outcomes when maternal language input is less

https://doi.org/10.1016/j.ecresq.2013.08.002Get rights and content

Highlights

  • Examined the language by mother in a picturebook task with her child in the home.

  • Examined caregiver–child language in the child care setting.

  • Caregiver–child language interactions buffered children against poor language when mothers used lower levels of language.

Abstract

Recent research has suggested that high quality child care can buffer young children against poorer cognitive and language outcomes when they are at risk for poorer language and readiness skills. Most of this research measured the quality of parenting and the quality of the child care with global observational measures or rating scales that did not specify the exact maternal or caregiver behaviors that might be causally implicated in the buffering of these children from poor outcomes. The current study examined the actual language by the mother to her child in the home and the verbal interactions between the caregiver and child in the child care setting that might be implicated in the buffering effect of high quality childcare. The sample included 433 rural children from the Family Life Project who were in child care at 36 months of age. Even after controlling for a variety of covariates, including maternal education, income, race, child previous skill, child care type, the overall quality of the home and quality of the child care environment; observed positive caregiver–child verbal interactions in the child care setting interacted with the maternal language complexity and diversity in predicting children's language development. Caregiver–child positive verbal interactions appeared to buffer children from poor language outcomes concurrently and two years later if children came from homes where observed maternal language complexity and diversity during a picture book task was less.

Section snippets

Maternal language input and children's language development

In this paper, we examined two aspects of language in the home environment through maternal language input during a wordless picture book task: language diversity and language complexity. Language diversity included measures of the number of different words that were used and the kind of different words used that might foster interaction during the shared book task. Language complexity included the grammatical complexity of maternal language, including her mean length of utterance (MLU) in

Maternal–child book sharing and child language development

One context that has often been used to measure maternal language diversity and complexity as well as child language has been the shared book situation (Ninio & Bruner, 1978). The language scaffolding that mothers use in this situation has been linked to children's later language and literacy skills (Payne, Whitehurst, & Angell, 1994; Sénéchal, 2006). Researchers have demonstrated that maternal book reading interactions enhance children's vocabularies and other foundational skills in literacy (

Caregiver–child verbal interactions and children's language development

While contributions of specific maternal language inputs in the home are known to benefit children's language development, understanding the role of language inputs in the child care setting remains less clear, which is particularly true in studies that have also examined the child's home environment. The studies of NICHD ECCRN, 2000, NICHD ECCRN, 2003, NICHD ECCRN, 2005 suggested that the early predictors of children's language at 36 months of age were highly related to the quality of the home

The buffering effect of high-quality child care

Although the direct effects of language experiences at home and child care are important in understanding children's development, it is also important to understand if caregiver–child language interactions might buffer children against poorer language when they receive less language input from their mothers at home. Although no previous studies have specifically looked at caregiver–child verbal interactions as a buffer for children who receive less language input in the home, some recent work

Design and sample

The data were drawn from the Family Life Project, a longitudinal project designed to study families who live in two rural geographic areas with high child poverty rates east of the Mississippi (Dill, 1999). Our definition of rural at the county level included Beale Codes 3, 4 and 6, where rural was synonymous with counties that contained mid-size and small towns somewhat distant from urban centers (Butler & Beale, 1994). We only included counties where there was no town with a population of

Preliminary analyses

To account for missing data within the subsample (ranging from 1–15% of covariate data to 1–8% of outcome data), multiple imputation was conducted in SAS 9.2, using PROC MI to create 30 imputed data sets. Multiple imputation serves to reduce bias due to missing data in longitudinal research designs, with researchers recommending imputation with at least 25 data sets (Spratt et al., 2010). Descriptive findings (Table 1, Table 2, Table 3) are presented using non-imputed data while all regression

Discussion

The findings from this study underscore the importance of both the specific language input by mothers at home and the language by caregivers in child care in predicting children's current and later language. Caregiver language interactions in child care were especially important for children whose mothers provided less language in the home. For most young children in the United States, the home and child care are the major ecological settings during the preschool years. Thus, much of the

Acknowledgment

Support for this research was provided by the National Institute of Child Health and Human Development (PO1-HD-39667), with co-funding from the National Institute on Drug Abuse. Please direct correspondence to Lynne Vernon-Feagans.

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    The Family Life Project (FLP) Key Investigators include Lynne Vernon Feagans, The University of North Carolina; Martha Cox, The University of North Carolina; Clancy Blair, The Pennsylvania State University; Peg Burchinal, The University of North Carolina; Linda Burton, Duke University; Keith Crnic, The Arizona State University; Ann Crouter, The Pennsylvania State University; Patricia Garrett-Peters, The University of North Carolina; Mark Greenberg, The Pennsylvania State University; Stephanie Lanza, The Pennsylvania State University; Roger Mills-Koonce, The University of North Carolina; Emily Werner, The Pennsylvania State University and Michael Willoughby, The University of North Carolina.

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