Comparison of FREQ and CATMOD Procedures

PROC FREQ is used primarily to investigate the relationship between two variables; any confounding variables are taken into account by stratification rather than by parameter estimation. PROC CATMOD is used to investigate the relationship among many variables , all of which are integrated into a parametric model.

When PROC CATMOD estimates the covariance matrix of the frequencies, it assumes that the frequencies were obtained by a stratified simple random sampling procedure. However, PROC CATMOD can also analyze input data that consist of a function vector and a covariance matrix. Therefore, if the sampling procedure is different, you can estimate the covariance matrix of the frequencies in the appropriate manner before submitting the data to PROC CATMOD.

For the FREQ procedure, Fisher's Exact Test and Cochran-Mantel-Haenszel statistics are based on the hypergeometric distribution, which corresponds to fixed marginal totals. However, by conditioning arguments, these tests are generally applicable to a wide range of sampling procedures. Similarly, the Pearson and likelihood -ratio chi-square statistics can be derived under a variety of sampling situations.

PROC FREQ can do some traditional nonparametric analysis (such as the Kruskal-Wallis test and Spearman's correlation) since it can generate rank scores internally. Fisher's Exact Test and the Cochran-Mantel-Haenszel statistics are also inherently nonparametric. However, the main vehicle for nonparametric analyses in the SAS System is the NPAR1WAY procedure.

A large sample size is required for the validity of the chi-square distributions, the standard errors, and the covariance matrices for both PROC FREQ and PROC CATMOD. If sample size is a problem, then PROC FREQ has the advantage with its CMH statistics because it does not use any degrees of freedom to estimate parameters for confounding variables. In addition, PROC FREQ can compute exact p -values for any two-way table, provided that the sample size is sufficiently small in relation to the size of the table. It can also produce exact p -values for many tests, including the test of binomial proportions , the Cochran-Armitage test for trend, and the Jonckheere-Terpstra test for ordered differences among classes.

See the chapters on the FREQ and CATMOD procedures for more information. In addition, some well-known texts that deal with analyzing categorical data are listed in the 'References' section of this chapter.