Classifying Home-Based Child Care Providers: Validating a Typology of Providers' Beliefs and Self-Reported Practices.

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Title: Classifying Home-Based Child Care Providers: Validating a Typology of Providers' Beliefs and Self-Reported Practices.
Authors: Hooper, Alison1 alhooper2@ua.edu
Source: Early Childhood Education Journal. May2019, Vol. 47 Issue 3, p275-285. 11p. 5 Charts, 1 Graph.
Subject Terms: *Early childhood education, *Professional education, *Child development, Child care services, Latent class analysis (Statistics)
Abstract: Although home-based child care is a widely-used form of non-parental child care, relatively little is known about the home-based provider workforce and their beliefs and practices related to children and families. This study validates a previously identified typology of listed home-based child care providers (Hooper and Hallam, Early Child Res Q 47:194–205, 2019). The typology categorizes providers based on their beliefs and self-reported practices, rather than by licensing status or other structural characteristics. The current study uses exploratory and confirmatory latent profile analysis (LPA) to categorize a statewide sample of home-based child care providers (n = 234). Results of the confirmatory LPA suggest that the original typology identified in previous analysis of a nationally representative sample—Educationally Focused, Educationally Aware, and Caregiver profiles—fit the data well. However, an additional profile of Highly Engaged providers emerged in the exploratory analysis. Using the results of the profile analyses, I discuss implications for designing and implementing quality improvement supports for home-based child care providers to support them in providing high-quality child care. [ABSTRACT FROM AUTHOR]
Copyright of Early Childhood Education Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Classifying Home-Based Child Care Providers: Validating a Typology of Providers' Beliefs and Self-Reported Practices.
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  Data: <searchLink fieldCode="JN" term="%22Early+Childhood+Education+Journal%22">Early Childhood Education Journal</searchLink>. May2019, Vol. 47 Issue 3, p275-285. 11p. 5 Charts, 1 Graph.
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  Data: Although home-based child care is a widely-used form of non-parental child care, relatively little is known about the home-based provider workforce and their beliefs and practices related to children and families. This study validates a previously identified typology of listed home-based child care providers (Hooper and Hallam, Early Child Res Q 47:194–205, 2019). The typology categorizes providers based on their beliefs and self-reported practices, rather than by licensing status or other structural characteristics. The current study uses exploratory and confirmatory latent profile analysis (LPA) to categorize a statewide sample of home-based child care providers (n = 234). Results of the confirmatory LPA suggest that the original typology identified in previous analysis of a nationally representative sample—Educationally Focused, Educationally Aware, and Caregiver profiles—fit the data well. However, an additional profile of Highly Engaged providers emerged in the exploratory analysis. Using the results of the profile analyses, I discuss implications for designing and implementing quality improvement supports for home-based child care providers to support them in providing high-quality child care. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Early Childhood Education Journal is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10643-019-00926-8
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      – SubjectFull: Child development
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