User manual SPSS REGRESSION MODELS 13.0

[. . . ] SPSS Regression Models 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. [. . . ] See the SPSS Command Syntax Reference for complete syntax information. Chapter Probit Analysis 4 This procedure measures the relationship between the strength of a stimulus and the proportion of cases exhibiting a certain response to the stimulus. It is useful for situations where you have a dichotomous output that is thought to be influenced or caused by levels of some independent variable(s) and is particularly well suited to experimental data. This procedure will allow you to estimate the strength of a stimulus required to induce a certain proportion of responses, such as the median effective dose. Example. How effective is a new pesticide at killing ants, and what is an appropriate concentration to use?You might perform an experiment in which you expose samples of ants to different concentrations of the pesticide and then record the number of ants killed and the number of ants exposed. Applying probit analysis to these data, you can determine the strength of the relationship between concentration and killing, and you can determine what the appropriate concentration of pesticide would be if you wanted to be sure to kill, say, 95% of exposed ants. Regression coefficients and standard errors, intercept and standard error, Pearson goodness-of-fit chi-square, observed and expected frequencies, and confidence intervals for effective levels of independent variable(s). This procedure uses the algorithms proposed and implemented in NPSOL® by Gill, Murray, Saunders & Wright to estimate the model parameters. Data. For each value of the independent variable (or each combination of values for multiple independent variables), your response variable should be a count of the number of cases with those values that show the response of interest, and the total observed variable should be a count of the total number of cases with those values for the independent variable. The factor variable should be categorical, coded as integers. 25 26 Chapter 4 Assumptions. If you have a large number of values for the independent variables relative to the number of observations, as you might in an observational study, the chi-square and goodness-of-fit statistics may not be valid. Related procedures. 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 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. Obtaining a Probit Analysis E From the menus choose: Analyze Regression Probit. . . Figure 4-1 Probit Analysis dialog box 27 Probit Analysis E 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. E Select a total observed variable. 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. If you do, click Define Range to define the groups. E 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 the Transform drop-down list. [. . . ] The first degree of freedom contains the linear effect across all categories; the second degree of freedom, the quadratic effect; the third degree of freedom, the cubic; and so on, for the higher-order effects. You can specify the spacing between levels of the treatment measured by the given categorical variable. Equal spacing, which is the default if you omit the metric, can be specified as consecutive integers from 1 to k, where k is the number of categories. If the variable drug has three categories, the subcommand /CONTRAST(DRUG)=POLYNOMIAL is the same as /CONTRAST(DRUG)=POLYNOMIAL(1, 2, 3) 53 Categorical Variable Coding Schemes Equal spacing is not always necessary, however. [. . . ]