User manual SPSS COMPLEX SAMPLES 13.0

[. . . ] SPSS Complex Samples 13. 0 TM 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. [. . . ] Alternatively, you can build a custom model that includes interaction effects and nested terms. 64 Chapter 9 Non-Nested Terms For the selected factors and covariates: Interaction. Creates the highest-level interaction term for all selected variables. Creates a main-effects term for each variable selected. Creates all possible two-way interactions of the selected variables. Creates all possible three-way interactions of the selected variables. Creates all possible four-way interactions of the selected variables. Creates all possible five-way interactions of the selected variables. Nested Terms You can build nested terms for your model in this procedure. Nested terms are useful for modeling the effect of a factor or covariate whose values do not interact with the levels of another factor. For example, a grocery store chain may follow the spending habits of its customers at several store locations. Since each customer frequents only one of these locations, the Customer effect can be said to be nested within the Store location effect. Additionally, you can include interaction effects or add multiple levels of nesting to the nested term. Limitations. Nested terms have the following restrictions: All factors within an interaction must be unique. Thus, if A is a factor, then specifying A*A is invalid. All factors within a nested effect must be unique. Thus, if A is a factor, then specifying A(A) is invalid. No effect can be nested within a covariate. Thus, if A is a factor and X is a covariate, then specifying A(X) is invalid. Intercept. The intercept is usually included in the model. If you can assume the data pass through the origin, you can exclude the intercept. Even if you include the intercept in the model, you can choose to suppress statistics related to it. 65 Complex Samples General Linear Model Complex Samples General Linear Model Statistics Figure 9-3 General Linear Model Statistics dialog box Model Parameters. This group allows you to control the display of statistics related to the model parameters. Estimate. [. . . ] While B is convenient for testing the model effects, Exp(B) is easier to interpret. Exp(B) represents the ratio change in the odds of the event of interest attributable to a one-unit increase in the predictor for predictors that are not part of interaction terms. For example, Exp(B) for employ is equal to 0. 798, which means that the odds of default for people who have been with their current employer for two years are 0. 798 times the odds of default for those who have been with their current employer for one year, all other things being equal. The design effects indicate that some of the standard errors computed for these parameter estimates are larger than those you would obtain if you assumed that these observations came from a simple random sample, while other are smaller. [. . . ]