User manual SPSS REGRESSION MODELS 11.0

[. . . ] SPSS Regression Models 11. 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. [. . . ] Probit analysis is closely related to logistic regression; in fact, if you choose the logit transformation, this procedure will essentially compute a logistic regression. In general, probit analysis is appropriate for designed experiments, whereas logistic regression is more appropriate for observational studies. The differences in output 17 18 Chapter 4 reflect these different emphases. The probit analysis procedure reports estimates of effective values for various rates of response (including median effective dose), while the logistic regression procedure reports estimates of odds ratios for independent variables. To Obtain a Probit Analysis From the menus choose: Analyze Regression Probit. . . Figure 4. 1 Probit Analysis dialog box Select a response frequency variable. This variable indicates the number of cases exhibiting a response to the test stimulus. The values of this variable cannot be negative. This variable indicates the number of cases to which the stimulus was applied. The values of this variable cannot be negative and cannot be less than the values of the response frequency variable for each case. Optionally, you can select a factor variable. If you do, click Define Range to define the groups. Select one or more covariate(s). This variable contains the level of the stimulus applied to each observation. If you want to transform the covariate, select a transformation from Probit Analysis 19 the Transform drop-down list. If no transformation is applied, and there is a control group, then the control group is included in the analysis. Select either Probit or Logit model. Probit Analysis Define Range Figure 4. 2 Probit Analysis Define Range dialog box This allows you to specify the levels of the factor variable that will be analyzed. The factor levels must be coded as consecutive integers, and all levels in the range you specify will be analyzed. Probit Analysis Options Figure 4. 3 Probit Analysis Options dialog box 20 Chapter 4 You can specify options for your probit analysis: Statistics. Allows you to request the following optional statistics: Frequencies, Relative median potency, Parallelism test, and Fiducial confidence intervals. Fiducial confidence intervals and Relative median potency are unavailable if you have selected more than one covariate. Relative median potency and Parallelism test are available only if you have selected a factor variable. Allows you to indicate a natural response rate even in the absence of the stimulus. Available alternatives are None, Calculate from data, or Value. Allows you to control parameters of the iterative parameter-estimation algorithm. You can override the defaults for maximum iterations, step limit, and optimality tolerance. PROBIT Command Additional Features The SPSS command language also allows you to: · Request an analysis on both the probit and logit models. [. . . ] If the dosage administered to the second group is twice that to the first group and the dosage administered to the third group is three times that to the first group, the treatment categories are equally spaced, and an appropriate metric for this situation consists of consecutive integers: /CONTRAST(DRUG)=POLYNOMIAL(1, 2, 3) If, however, the dosage administered to the second group is four times that given the first group, and the dosage given the third group is seven times that to the first, an appropriate metric is /CONTRAST(DRUG)=POLYNOMIAL(1, 4, 7) In either case, the result of the contrast specification is that the first degree of freedom for drug contains the linear effect of the dosage levels and the second degree of freedom contains the quadratic effect. Polynomial contrasts are especially useful in tests of trends and for investigating the nature of response surfaces. You can also use polynomial contrasts to perform nonlinear curve fitting, such as curvilinear regression. Repeated Compares adjacent levels of an independent variable. The general matrix form is mean df(1) df(2) . [. . . ]