User manual SPSS CATEGORIES 13.0

[. . . ] SPSS Categories 13. 0 ® Jacqueline J. Heiser SPSS Inc For more information about SPSS® software products, please visit our Web site at http://www. spss. com or contact SPSS Inc. 233 South Wacker Drive, 11th Floor Chicago, IL 60606-6412 Tel: (312) 651-3000 Fax: (312) 651-3668 SPSS is a registered trademark and the other product names are the trademarks of SPSS Inc. No material describing such software may be produced or distributed without the written permission of the owners of the trademark and license rights in the software and the copyrights in the published materials. [. . . ] E Click Save in the Categorical Regression dialog box. Figure 8-15 Save dialog box E Select Transformed variables and Residuals. 121 Categorical Regression E Click Continue. E Click Plots in the Categorical Regression dialog box. Figure 8-16 Plots dialog box E Choose to create transformation plots for package and price. E Click OK in the Categorical Regression dialog box. Intercorrelations The intercorrelations among the predictors are useful for identifying multicollinearity in the regression. Variables that are highly correlated will lead to unstable regression estimates. However, due to their high correlation, omitting one of them from the model only minimally affects prediction. The variance in the response that can be explained by the omitted variable is still explained by the remaining correlated variable. However, zero-order correlations are sensitive to outliers and also cannot identify multicollinearity due to a high correlation between a predictor and a combination of other predictors. 122 Chapter 8 Figure 8-17 Original predictor correlations Figure 8-18 Transformed predictor correlations The intercorrelations of the predictors for both the untransformed and transformed predictors are displayed. All values are near 0, indicating that multicollinearity between individual variables is not a concern. Notice that the only correlations that change involve Package design. Because all other predictors are treated numerically, the differences between the categories and the order of the categories are preserved for these variables. Consequently, the correlations cannot change. Model Fit and Coefficients The Categorical Regression procedure yields an R2 of 0. 948, indicating that almost 95% of the variance in the transformed preference rankings is explained by the regression on the optimally transformed predictors. Transforming the predictors improves the fit over the standard approach. 123 Categorical Regression Figure 8-19 Model summary for categorical regression The following table shows the standardized regression coefficients. Categorical regression standardizes the variables, so only standardized coefficients are reported. These values are divided by their corresponding standard errors, yielding an F test for each variable. However, the test for each variable is contingent upon the other predictors being in the model. In other words, the test determines if omission of a predictor variable from the model with all other predictors present significantly worsens the predictive capabilities of the model. These values should not be used to omit several variables at one time for a subsequent model. Moreover, alternating least squares optimizes the quantifications, implying that these tests must be interpreted conservatively. Figure 8-20 Standardized coefficients for transformed predictors The largest coefficient occurs for Package design. A one standard deviation increase in Package design yields a 0. 748 standard deviation decrease in predicted preference ranking. However, Package design is treated nominally, so an increase in the quantifications need not correspond to an increase in the original category codes. [. . . ] Changes can be studied when the measurement instrument is different at different time points. Health Services and Outcomes Research Methodology, 4, 109­126. Intra-individual and inter-individual multidimensionality. In: Psychological Scaling: Theory & Applications, H. [. . . ]