Fuzzy Clusterwise Generalized Structured Component Analysis With Higher-Order Factor Model

• In Event: Methods for Mixture Modeling and Classification

Abstract

In generalized structured component analysis (GSCA), an approach of component-based structural equation modeling, fuzzy clustering was applied to classify a heterogeneity of a population, denoted as fuzzy clusterwise GSCA. Although higher-order latent models are widely used, the application of higher order model has not been used in fuzzy clusterwise GSCA. Accordingly, this study utilizes fuzzy clustering GSCA in a higher-order latent model by demonstrating an empirical data analysis with a cognitive measure. The results showed that the 2-solution model worked with the empirical data. Two classes in the 2-solution model were distinguished as low and high ability in Number Puzzles.

Authors

• Seongeun Kim, University of North Carolina at Greensboro

• Seohee Park, American Board of Internal Medicine

• Ji Hoon Ryoo, Yonsei University