Analysis of sample survey data involving categorical response variables: Methods and software - ARCHIVED

During the past 10 years or so, rapid progress has been made in the development of statistical methods of analysing survey data that take account of the complexity of survey design. This progress has been particularly evident in the analysis of cross-classified count data. Developments in this area have included weighted least squares estimation of generalized linear models and associated Wald tests of goodness of fit and subhypotheses, corrections to standard chi-squared or likelihood ratio tests under loglinear models or logistic regression models involving a binary response variable, and jackknifed chisquared tests. This paper illustrates the use of various extensions of these methods on data from complex surveys. The method of Scott, Rao and Thomas (1989) for weighted regression involving singular covariance matrices is applied to data from the Canada Health Survey (1978-79). Methods for logistic regression models are extended to Box-Cox models involving power transformations of cell odds ratios, and their use is illustrated on data from the Canadian Labour Force Survey. Methods for testing equality of parameters in two logistic regression models, corresponding to two time points, are applied to data from the Canadian Labour Force Survey. Finally, a general class of polytomous response models is studied, and corrected chi-squared tests are applied to data from the Canada Health Survey (1978-79). Software to implement these methods using the SAS facilities on a main frame computer is briefly described.