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. Use, duplication, or disclosure by the Government is subject to restrictions as set forth in subdivision (c) (1) (ii) of The Rights in Technical Data and Computer Software clause at 52. 227-7013. [. . . ] This group allows you to select the type of statistic used for testing hypotheses. You can choose between F, adjusted F, chi-square, and adjusted chi-square. This group gives you control over the sampling design degrees of freedom used to compute p values for all test statistics. If based on the sampling design, the value is the difference between the number of primary sampling units and the number of strata in the first stage of sampling. Alternatively, you can set a custom degrees of freedom by specifying a positive integer. When performing many hypothesis tests, you run the risk of increased overall Type I error (the probability of incorrectly rejecting a null hypothesis). This group allows you to choose the method of adjusting the significance level. This method does not control the overall probability of rejecting the hypotheses that some linear contrasts are different from the null hypothesis values. Sequential Sidak. This is a sequentially step-down rejective Sidak procedure that is much less conservative in terms of rejecting individual hypotheses but maintains the same overall significance level. Sequential Bonferroni. This is a sequentially step-down rejective Bonferroni procedure that is much less conservative in terms of rejecting individual hypotheses but maintaining the same overall significance level. Sidak. This method adjusts the observed significance level for the fact that multiple contrasts are being tested. 68 Chapter 9 Complex Samples General Linear Model Estimated Means Figure 9-5 General Linear Model Estimated Means dialog box The Estimated Means dialog box allows you to display the model-estimated marginal means for levels of factors and factor interactions specified in the Model subdialog box. You can also request that the overall population mean be displayed. Term. Estimated means are computed for the selected factors and factor interactions. The contrast determines how hypothesis tests are set up to compare the estimated means. This type of contrast is useful when there is a control group. Deviation. Compares the mean of each level (except a reference category) to the mean of all of the levels (grand mean). Compares the mean of each level (except the first) to the mean of previous levels. Compares the mean of each level of the factor (except the last) to the mean of subsequent levels. 69 Complex Samples General Linear Model Repeated. Compares the mean of each level (except the last) to the mean of the subsequent level. Polynomial. Compares the linear effect, quadratic effect, cubic effect, and so on. The first degree of freedom contains the linear effect across all categories; the second degree of freedom, the quadratic effect; and so on. These contrasts are often used to estimate polynomial trends. Reference Category. The simple and deviation contrasts require a reference category, or factor level against which the others are compared. Complex Samples General Linear Model Save Figure 9-6 General Linear Model Save dialog box Save Variables. [. . . ] The meaning of a logistic regression coefficient is not as straightforward as that of a linear regression coefficient. 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. [. . . ]