A Methodology for Improving fitness of the Latent Growth Modeling using Association Rule Mining

연관규칙을 이용한 잠재성장모형의 개선방법론

Cho, Yeong Bin;Jun, Jae-Hoon;Choi, Byungwoo

  • Received : 2018.11.04
  • Accepted : 2019.02.20
  • Published : 2019.02.28


The Latent Growth Modeling(LGM) is known as the typical analysis method of longitudinal data and it could be classified into unconditional model and conditional model. It is common to assume that the growth trajectory of unconditional model of LGM is linear. In the case of quasi-linear, the methodology for improving the model fitness using Sequential Pattern of Association Rule Mining is suggested. To do this, we divide longitudinal data into quintiles and extract periodic changes of the longitudinal data in each quintiles and make sequential pattern based on this periodic changes. To evaluate the effectiveness, the LGM module in SPSS AMOS was used and the dataset of the Youth Panel from 2001 to 2006 of Korea Employment Information Service. Our methodology was able to increase the fitness of the model compared to the simple linear growth trajectory.


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