• The Journal of Korean Society for School & Community Health Education
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• v.20 no.1
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• pp.69-84
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• 2019

Objectives: The purpose of this study is to examine change in adolescent depression across time and to determine the relation between individual and neighborhood socioeconomic status (SES) and depression. Methods: This study employed multilevel latent growth analysis using longitudinal data from Korea Children and Youth Panel Survey. A sample of this study consists of 2,351 adolescents who were in first grade of middle school in 2010. Results: Results showed that both initial level and downward trajectory of depression varied significantly across individuals as well as across neighborhoods. On the individual level, self-rated economic condition(b=-0.203, p<.001) was related to the initial level of depression. Adolescents whose father had a high educational level(b=0.028, p<.001) or whose mother had a low educational level(b=-0.022, p=.011) had lower rates of decline in adolescent depression. On the neighborhood level, neighborhood deprivation index (b=0.003, p=.019) and gini coefficient(b=0.124, p=.040) were associated with lower rates of decline in depression. Conclusions: Low SES in adolescence is correlated with worse mental health, especially depression. Social disparities in depression likely originate before adulthood. The findings argue for the importance of understanding depression in adolescence from a multilevel or ecological framework.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.100-106
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• 2022

A longitudinal study refers to a research method based on longitudinal data repeatedly measured on the same object. Most of the longitudinal analysis methods are suitable for prediction or inference, and are often not suitable for use in exploratory study. In this study, an exploratory method to analyze longitudinal data is presented, which is to find the longitudinal trajectory after determining the best number of clusters by clustering longitudinal data using self-organizing map technique. The proposed methodology was applied to the longitudinal data of the Employment Information Service, and a total of 2,610 samples were analyzed. As a result of applying the methodology to the actual data applied, time-series clustering results were obtained for each panel. This indicates that it is more effective to cluster longitudinal data in advance and perform multilevel longitudinal analysis.