인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Socio-emotional development in early childhood serves as critical foundations for later psychological well-being, academic achievement, and social adjustment. While previous studies have identified various factors that influence these developmental outcomes, there remains a need for data-driven approaches that simultaneously capture both categorical profiles and continuous variation in socio-emotional development. Accordingly, the present study draws on longitudinal data from the Panel Study on Korean Children to identify key predictors of socio-emotional development in early childhood. Specifically, independent variables measured between ages two and five were used to predict socio-emotional outcomes at age six. The analysis focused on three domains of socio-emotional development: problem behavior, self-esteem, and social competence. Initially, latent profiles were derived through principal component analysis and K-means clustering, and classification methods were applied to identify predictors of profile membership. In parallel, Ridge regression models with Shapley Additive Explanations (SHAP) were employed to estimate the relative contribution of each predictor to individual outcomes. The classification model achieved an accuracy of 86.7% in predicting latent socio-emotional profiles, and Ridge regression models explained 25.1% to 36.2% of the variance in the three outcome variables. SHAP-based interpretation across both models revealed that peer interaction, maternal warmth, and early sociability functioned as protective factors, whereas disruptive behavior and parenting stress were associated with negative adjustment. These findings grounded in statistically robust model performance and interpretable machine learning analysis, highlight the practical utility of this approach for early screening and intervention
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-094346580