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


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.


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


  1. J. S. Lee & S. Y. Kim. (2017). An Exploration of Nonlinear Latent Growth Model Using Exponential Function: As an Alternative to Quadratic LGM, J. of Educational Evaluation, 30(4), 791-816.
  2. B. W. Jun, Y. W. Joo & B. H. Lim. (2011). A Study of Demographic Characteristics and Products Buying Behavior of Online Consumer Survey Participants Compared to Face-to-Face Survey, Korean Coporation Management Review, 21(3), 93-108.
  3. E. J. Lee & C. H. Cho. (2013). A Longitudinal Study on the Effects of Franchise's Factors and Perform ance - Disclosure Agreement, Korean J. of Business Administration, 26(8), 2185-2209.
  4. E. J. Lee &, C. H. Cho. (2014). A Logitudinal Study on the KS-SQI for Improving Service Quality, Korean J. of Business Administration, 27(4), 561-574.
  5. H. J. Lim & J. S. Cho. (2012). The Effect of Ownership Concentration on Firm Performance : Static and Dynamic Panel Data Analysis, Korean J. of Business Administration, 25(8), 3265-3291.
  6. Y. B. Cho, S. K. Lee & K. H. Ro. (2015). A Methodology for Analyzing the Longitudinal Data using SOM Technique, Korean J. of Business Administration, 28(1), 93-102.
  7. K. S. Kim. (2009). AMOS and LISREL, Han Academy.
  8. S. W. Menard. (2002). Longitudinal research (2nd. ed.). London: Sage Publications Inc.
  9. S. S. Yeo & S. H. Park. (2012). An Appliation of Latent Growth Modeling: Use of Curriculum-Based Measurement as longitudinal Data, Asian J. of Education, 13(4), 247-273.
  10. R Agrawal, T. Imielinski & A. Swami. (1993). Mining association rules between sets of items in large databases, Proceedings of the ACM SIGMOD Conference on Management of Data, 207-216.
  11. J. D. Singer & J. B. Willett. (2006). Longitudinal data analysis: present status; future prospects, Presentation at the 45th Congress of the German Pcycological Association, Nurnberg, Germany, 17-21.
  12. Toon Taris. (1999). A Primer in Longitudinal Data Analysis, SAGE Publications Inc.
  13. G. M. Fitzmaurice, N. M. Laird & J. H. Ware. (2012). Applied Longitudinal Analysis, 2nded. JohnWiley&Sons, Hoboken, New Jersey.
  14. K. L. McArdle & D. B. Epstein. (1987). Latent Growth curves within development structural equation models, Child Development, 58, 110-133.
  15. B. M. Byrne. (2010). Structural Equation modeling with AMOS: Basic concepts, applications and programming. Mahwah, N.J:Lawrence Erlbaum Associates.
  16. R. B. Kline. (2004). Principles and practice of structural equation modeling. New York: Guilford.
  17. K. A. Bollen & P. J. Curran. (2006). Latent curve models a structural equation perspective. Hoboken, NJ: Wiley-Interscience.
  18. B. W. Jin, Y. S. Cho & K. H. Ryu. (2010). Personalized e-Commerce Recommendation System using RFM method and Association Rules, J. of the Korea Society of Computer and Information, 15(12), 227-235.
  19. J. C. Kim, H. I. Jung, H. Yoo & K. Y. Chung. (2018). Sequence Mining based Manufacturing Process using Decision Model in Cognitive Factory, Journal of the Korea Convergence Society, 9(3), 53-59.
  20. Y. J. Shin & M. S. Yim. (2012). A Study of the Relatioship Analysis between Mobile Application by Using An Association Rules, Journal of the Korea Convergence Society, 3(2), 19-25.