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Regression with Qualitative and Quantitative Variables: An Alternating Least Squares Method with Optimal Scaling Features

Published online by Cambridge University Press:  01 January 2025

Forrest W. Young*
Affiliation:
University of North Carolina
Jan de Leeuw
Affiliation:
Rijksuniversiteit Te Leiden
Yoshio Takane
Affiliation:
University of North Carolina
*
Requests for reprints should be sent to Forrest W. Young, Psychometric Laboratory, University of North Carolina, Davie Hall 013 A, Chapel Hill, North Carolina 27514.

Abstract

A method is discussed which extends canonical regression analysis to the situation where the variables may be measured at a variety of levels (nominal, ordinal, or interval), and where they may be either continuous or discrete. There is no restriction on the mix of measurement characteristics (i.e., some variables may be discrete-ordinal, others continuous-nominal, and yet others discrete-interval). The method, which is purely descriptive, scales the observations on each variable, within the restriction imposed by the variable's measurement characteristics, so that the canonical correlation is maximal. The alternating least squares algorithm is discussed. Several examples are presented. It is concluded that the method is very robust. Inferential aspects of the method are not discussed.

Information

Type
Original Paper
Copyright
Copyright © 1976 The Psychometric Society

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