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Contributions: AbstractPLS path models with categorical indicators  a correspondence analytical approach
We present an estimation method for path models with categorical manifestvariables. The path model under observation is the usual path model withlinear dependencies between M blocks of manifest variables and correspondinglatent variables and linear relations between the latent variables themselves. For evaluation of the model the PLS approach is used. In the basic PLS algorithm the latent variables h_{m} (m = 1,...,M) will be estimated by an iteration algorithm of the form
If the manifest variables are categorical, the linear relations above are meaningless, assuming the latent variables are continuous.Therefore we use correspondence analytical tools for estimation of the latentvariables. The idea of Correspondence Analysis (CorA) is multidimensionalscaling of a set of categorical data, which is, roughly speaking, similar to principal components for categorical data. One main aspect of CorA is the transformation of the raw data matrix into anindicator matrix G and the analysis of a kind of correlation matrixfor G. If we describe by [(Q)\tilde] a suitabletransformation of G we obtain a decomposition like
