Visualizing Latent Structures in Grade Correspondence Cluster Analysis and Generalized Association Plots
The latent structure of psychological data set concerning superstitions is investigated by means of two recent exploratory methods: Grade Correspondence Cluster Analysis (GCCA) and Generalized Association Plots (GAP). The paper compares visualized results in GCCA and GAP. Moreover, it shows what differs both methodologies and what is their intrinsic similarity, according to which the revealed latent structures become equivalent whenever the data set is sufficiently regular. Therefore upon the basis of the real data set, were constructed two types of highly regular simulated data, of the same size and the same multivariate dependence index. These simulated data were then analyzed.
KeywordsLatent Structure Correlation Matrice Positive Dependence Pearson Correlation Matrix Uniform Discretization
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