Abstract
A method is discussed which extends principal components analysis to the situation where the variables may be measured at a variety of scale levels (nominal, ordinal or interval), and where they may be either continuous or discrete. There are no restrictions on the mix of measurement characteristics and there may be any pattern of missing observations. The method scales the observations on each variable within the restrictions imposed by the variable's measurement characteristics, so that the deviation from the principal components model for a specified number of components is minimized in the least squares sense. An alternating least squares algorithm is discussed. An illustrative example is given.
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Copies of this paper and of the associated PRINCIPALS program may be obtained by writing to Forrest W. Young, Psychometric Laboratory, Davie Hall 013-A, Chapel Hill, NC 27514.
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Young, F.W., Takane, Y. & de Leeuw, J. The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features. Psychometrika 43, 279–281 (1978). https://doi.org/10.1007/BF02293871
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DOI: https://doi.org/10.1007/BF02293871