The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Analysis of internet addiction in Korean adolescents using sparse partial least-squares regression

희소 부분 최소 제곱법을 이용한 우리나라 청소년 인터넷 중독 자료 분석

  • Han, Jeongseop (Department of mathematics, Korea Military Academy) ;
  • Park, Soobin (Department of Statistics, Ewha Womans University) ;
  • Lee, onghwan (Department of Statistics, Ewha Womans University)
  • 한정섭 (육군사관학교 수학과) ;
  • 박수빈 (이화여자대학교 통계학과) ;
  • 이동환 (이화여자대학교 통계학과)
  • Received : 2018.01.30
  • Accepted : 2018.02.01
  • Published : 2018.04.30
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Abstract

Internet addiction in adolescents is an important social issue. In this study, sparse partial least-squares regression (SPLS) was applied to internet addiction data in Korean adolescent samples. The internet addiction score and various clinical and psychopathological features were collected and analyzed from self-reported questionnaires. We considered three PLS methods and compared the performance in terms of prediction and sparsity. We found that the SPLS method with the hierarchical likelihood penalty was the best; in addition, two aggression features, AQ and BSAS, are important to discriminate and explain latent features of the SPLS model.

본 연구에서는 우리나라 청소년의 인터넷 중독 자료를 희소 부분 최소제곱법을 적용 하여 분석하였다. 서울 성모병원에서 수집된 자가보고 방식의 인터넷 중독 측도와 다양한 임상 및 정신 병리학적 설문 문항들을 자료로 활용하였다. 표본의 개수보다 설문 문항의 수가 많은 고차원 자료이며, 각 세부문항끼리는 상관관계가 높아 부분 최소제곱법이 좋은 회귀분석 모형이다. 보다 높은 예측 성능과 해석력을 얻기위해 희소성 제약 조건이 가능한 희소 부분 최소 제곱법을 이용하였고, 2가지 다른 벌칙함수를 이용하여 가장 좋은 방법을 선택하였다. 또한, 제안된 최종 모형을 통해 인터넷 중독이 임상 및 정신 병리학적 측도들로 잘 설명됨을 보이고, 공격성과 관련된 다른 설문 문항이 설문 문항이 모형의 잠재성분을 구별하고 설명하는데 역할을 한다는 유의미한 결과를 도출하였다.

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