Example 28.7 Repeated Measures, 4 Response Levels, 1 Population

This example illustrates a repeated measures analysis in which there are more than two levels of response. In this study, from Grizzle, Starmer, and Koch (1969, p. 493), 7,477 women aged 30–39 are tested for vision in both right and left eyes. Since there are four response levels for each dependent variable, the RESPONSE statement computes three marginal probabilities for each dependent variable, resulting in six response functions for analysis. Since the model contains a repeated measurement factor (Side) with two levels (Right, Left), PROC CATMOD groups the functions into sets of three (=6/2). Therefore, the Side effect has three degrees of freedom (one for each marginal probability), and it is the appropriate test of marginal homogeneity. The following statements produce Output 28.7.1:

   title 'Vision Symmetry';
   data vision;
      input Right Left count @@;
      datalines;
   1 1 1520    1 2  266    1 3  124    1 4  66
   2 1  234    2 2 1512    2 3  432    2 4  78
   3 1  117    3 2  362    3 3 1772    3 4 205
   4 1   36    4 2   82    4 3  179    4 4 492
   ;

   proc catmod data=vision;
      weight count;
      response marginals;
      model Right*Left=_response_ / freq design;
      repeated Side 2;
      title2 'Test of Marginal Homogeneity';
   quit;