Nonmetric Common Factor Analysis: an Alternating Least Squares Method with Optimal Scaling Features
نویسندگان
چکیده
We describe a convergent procedure for fitting the common factor analysis model to multivariate data whose variables may be nominal, ordinal or interval. Any mixture of measurement levels is permitted. There may be any pattern of missing data. As distinguished from previous work, the nonmetric relations (nominal or ordinal) are assumed on the raw observations (not on the correlations), and the model fitted is the common factor analysis model (not the principal components model) which isolates common from unique factor variation. The computational algorithm, based on the alternating least squares principle, is monotonically convergent and efficient. An illustrative example is presented.
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