Abstract
Three potential applications of stepwise procedures in canonical analysis and several alternative stepping decision rules are described. A stepdown procedure using smallest interest multiple correlation as the stepping criterion was applied to data on the Minnesota Importance Questionnaire and the Minnesota Vocational Interest Inventory for two random halves of a group of 500 males in a double cross-validation design. Results indicated that up to 75 percent of variables could be dropped from either set with little drop in the R[SUBc]. Cross-validation coefficients were usually higher after dropping several variables than for the full sets.
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