Lazarsfeld's latent class model assumes that an observed multi-way contingency table can be decomposed into a weighted sum of more basic contingency tables. Each of these basic tables exhibits statistical independence among the factors defining the table. CANDECOMP is a method for least squares fitting of a multilinear model, the CANDECOMP model, to general multi-way data tables. We show that the latent class model is a special case of the CANDECOMP model, so that the CANDECOMP procedure provides a method for least squares fitting of the latent class model. A program, called CANLAC, has been written for estimating the parameters of the latent class model using the CANDECOMP procedure. Applications of the program to both artificial and real data are described.
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