User manual SPSS CATEGORIES 14.0

[. . . ] SPSS Categories 14. 0 ® 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. The SOFTWARE and documentation are provided with RESTRICTED RIGHTS. [. . . ] In contrast to the regression coefficients, this measure defines the importance of the predictors additively--that is, the importance of a set of predictors is the sum of the individual importances of the predictors. Pratt's measure equals the product of the regression coefficient and the zero-order correlation for a predictor. These products add to R2, so they are divided by R2, yielding a sum of 1. The set of predictors Package design and Brand name, for example, have an importance of 0. 654. The largest importance corresponds to Package design, with Package design, Price, and Good Housekeeping seal accounting for 95% of the importance for this combination of predictors. Multicollinearity Large correlations between predictors will dramatically reduce a regression model's stability. Correlated predictors result in unstable parameter estimates. Tolerance reflects how much the independent variables are linearly related to one another. This measure is the proportion of a variable's variance not accounted for by other independent variables in the equation. If the other predictors can explain a large amount of a predictor's variance, that predictor is not needed in the model. A tolerance value near 1 indicates that the variable cannot be predicted very well from the other predictors. In contrast, a variable with a very low tolerance contributes little information to a model, and can cause computational problems. Moreover, large negative values of Pratt's importance measure indicate multicollinearity. All of the tolerance measures are very high. None of the predictors are predicted very well by the other predictors and multicollinearity is not present. 122 Chapter 8 Transformation Plots Plotting the original category values against their corresponding quantifications can reveal trends that might not be noticed in a list of the quantifications. Such plots are commonly referred to as transformation plots. Attention should be given to categories that receive similar quantifications. These categories affect the predicted response in the same manner. However, the transformation type dictates the basic appearance of the plot. Variables treated as numerical result in a linear relationship between the quantifications and the original categories, corresponding to a straight line in the transformation plot. The order and the difference between the original categories is preserved in the quantifications. The order of the quantifications for variables treated as ordinal correspond to the order of the original categories. However, the differences between the categories are not preserved. [. . . ] 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. [. . . ]