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A Data Based Methodology for Estimating the Unconditional Model of the Latent Growth Modeling

잠재성장모형의 무조건적 모델 추정을 위한 데이터 기반 방법론

  • Cho, Yeong Bin (Department of Business Administration, Division of International Business, Konkuk Univ.)
  • 조영빈 (건국대학교 국제비즈니스학부 경영학과)
  • Received : 2018.04.04
  • Accepted : 2018.06.20
  • Published : 2018.06.28

Abstract

The Latent Growth Modeling(LGM) is known as the arising analysis method of longitudinal data and it could be classified into unconditional model and conditional model. Unconditional model requires estimated value of intercept and slope to complete a model of fitness. However, the existing LGM is in absence of a structured methodology to estimate slope when longitudinal data is neither simple linear function nor the pre-defined function. This study used Sequential Pattern of Association Rule Mining to calculate slope of unconditional model. The applied dataset is 'the Youth Panel 2001-2006' from Korea Employment Information Service. The proposed methodology was able to identify increasing fitness of the model comparing to the existing simple linear function and visualizing process of slope estimation.

Keywords

Longitudinal Data Analysis;Latent Growth Modeling;Unconditional Model;Association Rule;Sequential Pattern

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