Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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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
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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.

대표적인 종단자료 분석방법인 잠재성장모형(Latent Growth Modeling)은 무조건적 모델과 조건적 모델로 구분되는데, 이중 무조건적 모델은 초기값과 기울기를 추정하여 적합도가 높은 모델을 추정해야 한다. 그렇지만 기존 잠재성장모형에는 종단자료의 형태가 단순선형함수 등 특정 함수가 아닐 경우 기울기를 추정하는 체계적인 방법론이 없었다. 본 연구에서는 뮤조건적 모델의 기울기를 추정하는데 연관규칙(Association Rule Mining)의 순차패턴(Sequential Pattern)을 사용하였다. 데이터는 한국고용정보원의 2001년~2006년에 조사한 청년 패널 데이터를 사용하였다. 제안한 방법론은 기존 단순선형함수를 가정할 때와 비교하여 적합도가 상승하는 것을 확인할 수 있었으며, 기울기 추정 과정을 시각화할 수 있는 부수적인 장점이 있었다.

Keywords

References

  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. https://doi.org/10.15753/aje.2012.13.4.011
  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. https://doi.org/10.2307/1130295
  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. https://doi.org/10.9708/jksci.2010.15.12.227
  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. https://doi.org/10.15207/JKCS.2018.9.3.053
  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.