Univaiuate anu multivariate categorical data analysis for block designs

Analysis for univariate and multivariate categorical data in block designs is given and illustrated through examples. The univariate analysis compares the treatments on the basis of their pooled frequency distributions (pooled over blocks). The test statistic used is called Q after Cochran (1950). The large sample null distribution of Q is a chi-square. Analysis of p-variate categorical data (kth variable having ck classes, K=1,...,p) can be done by treating it as a univariate categorical problem with d] classes. Very often d] is large in relation to the size of the experiment. This makes the expected frequencies for some of the cells very small, making the univariate method inapplicable. In these circumstances it may be reasonable to compare the treatments on the basis of marginal distributions up to the mth dimension, 1d] , which is given in this paper. This method is also illustrated for missing observations