Abstract
Redundancy analysis (RDA) was developed originally for descriptive data analysis of two sets of quantitative (continuous) variables, one set being dependent on the other. Israels (1984) generalized it to the case of qualitative (categorical) variables. In this paper we suggest a procedure (QRDA), extending RDA to deal with data described in terms of both qualitative and quantitative variables. For its evaluation, we give a Monte Carlo study to examine the stability of data analysis by QRDA, and demonstrate its validity. Further we discuss properties of QRDA in comparison with Israels' procedure on the point of eigenvalue problems, stating that the two procedures give the same eigenvector under a special condition on the data matrix. Then we show, on the basis of simulation study, that QRDA is more advantageous than Israels' procedure even in analyzing data in terms of qualitative variables only.
