The Korean Journal of Human Development

Article

Developmental Profiles of School-aged Children using Korean Child Community Centers and Developmental Environments as Predictors

Author
이보람(Lee, Boram),박혜준(Park, Hye Jun),김수정(Kim, Soo jung),김부열(Kim, Booyuel),김태종(Kim, Tae Jong)
Information
pp. 153~180 / 2018 Vol.25 No.3
e-ISSN2982-6845
p-ISSN1226-4474
Metrics Views 0 / Downloads 0
PDF

Abstract

The aims of this study were (1) to examine the characteristics and profiles of the cognitive, language and socio-emotional development of children using community child center, (2) to identify the influence of environmental factors. For theses purposes, developmental tests and questionnaires were conducted on 113 pairs of children and caregivers. To solve the research questions, latent profile analysis was used for identifying distinct group and binomial logistic analysis was employed for finding the environmental predictors influencing developmental profiles. The results showed that the degree of child’s development showed a large individual difference and subgroups were classified into two groups, ‘underachievement of socio-emotional development’ and ‘normal achievement of socio-emotional development’. The difference between two groups was found to be only in a socio-emotional development. Finally, the results showed that as the depression level of the caregiver was lower, the followign effects were found: higher perceived social support, the children s greater ability to develop school friendship longer partronage of the other, and higher probability of belonging to the ‘normal achievement of socio-emotional development’ group. Based on these results, it is important to focus on socio-emotional development and the ecological environment-including family, school, and community-to go beyond addressing the absence of care and also strengthen children’ developmental potential.

Keywords

지역아동센터 학령초기아동 발달유형 발달환경 잠재프로파일분석 Korean Child Community Center early school-aged children developmental profiles developmental environment latent profile analysis