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http://dx.doi.org/10.14400/JDC.2018.16.6.085

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

Cho, Yeong Bin (Department of Business Administration, Division of International Business, Konkuk Univ.)
Publication Information
Journal of Digital Convergence / v.16, no.6, 2018 , pp. 85-93 More about this Journal
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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Times Cited By KSCI : 2  (Citation Analysis)
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