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Early childhood risk and later adaptation: A person-centered approach using latent profiles

Risk factors in early childhood tend to co-occur and accumulate over time in complex patterns. Person-centered methods enable nuanced understanding of developmental processes of risk and resilience. With longitudinal data on 3398 children from the Fragile Families study, we utilized latent class ana...

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Bibliographic Details
Published in:Journal of applied developmental psychology 2019-05, Vol.62, p.66-76
Main Authors: Herbers, Janette E., Cutuli, J.J., Jacobs, Emily L., Tabachnick, Alexandra R., Kichline, Tiffany
Format: Article
Language:English
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Summary:Risk factors in early childhood tend to co-occur and accumulate over time in complex patterns. Person-centered methods enable nuanced understanding of developmental processes of risk and resilience. With longitudinal data on 3398 children from the Fragile Families study, we utilized latent class analysis to identify profiles of psychosocial risk in early childhood in relation to profiles of middle childhood functioning. Results revealed five classes of risk, including one class of low risk, two classes characterized by resource-related risks, one class characterized by family stress, and one class involving both resource and family risks. Patterns of later functioning involved five profiles. Two profiles demonstrated competence, two were characterized by lower functioning in certain domains, and one involved maladaptive functioning across domains. Risk profiles characterized by resource-related risks corresponded with outcome profiles with low academics. Profiles characterized by family stress were associated with below average academic, conduct, and emotional well-being profiles. •Person-centered methods elucidate developmental processes of risk and adaptation.•Children differ in their profiles of early risk and later adaptive functioning.•Profiles of primarily resource-related risks predict profiles of academic struggles.•Profiles with family dysfunction and other risks predict profiles with poor conduct.
ISSN:0193-3973
1873-7900
DOI:10.1016/j.appdev.2019.01.003