binary logistic regression models with correction on over-dispersion When using Generalized Lienar Models in SPSS (dist: binomial, link: logit, r/n) how to overdispersion correction? In otherwords how to apply binary logistic regression models with correction on over-dispersion using SPSS?
 A: Example 37.1 Logistic Regression
In an experiment comparing the effects of five different drugs, each drug is tested on a number of different subjects. The outcome of each experiment is the presence or absence of a positive response in a subject. The following artificial data represent the number of responses r in the n subjects for the five different drugs, labeled A through E. The response is measured for different levels of a continuous covariate x for each drug. The drug type and the continuous covariate x are explanatory variables in this experiment. The number of responses r is modeled as a binomial random variable for each combination of the explanatory variable values, with the binomial number of trials parameter equal to the number of subjects n and the binomial probability equal to the probability of a response.
The following DATA step creates the data set:
   data drug;
      input drug$ x r n @@;
      datalines;
   A  .1   1  10   A  .23  2  12   A  .67  1   9
   B  .2   3  13   B  .3   4  15   B  .45  5  16   B  .78  5  13
   C  .04  0  10   C  .15  0  11   C  .56  1  12   C  .7   2  12
   D  .34  5  10   D  .6   5   9   D  .7   8  10
   E  .2  12  20   E  .34 15  20   E  .56 13  15   E  .8  17  20
   ;
   run;

A logistic regression for these data is a generalized linear model with response equal to the binomial proportion r/n. The probability distribution is binomial, and the link function is logit. For these data, drug and x are explanatory variables. The probit and the complementary log-log link functions are also appropriate for binomial data.
PROC GENMOD performs a logistic regression on the data in the following SAS statements:
   proc genmod data=drug;
      class drug;
      model r/n = x drug / dist = bin
                           link = logit
                           lrci;
   run;

A: I don't see anything related to over dispersion in your problem. Even though I don't work with SPSS, I assume that the logistic regression dialog requires 4 types of variables:


*

*dependent - the count of positive outcomes

*independent continuous - covariate "X"

*independent categorical - drug type

*count variable -  number of observation in each category


I don't see need or room for any correction.
