Categorical Data Analysis
A casual perusal of the codebook and variable descriptions for most public-use survey data sets quickly leads to the observation that the responses to the majority of survey questions in the social sciences, public health, and related fields of research are measured as a binary choice, a selection from multinomial response categories, a choice from an ordinal scale or possibly a discrete count of events. This chapter covers procedures for simple univariate, bivariate, and selected multivariate analyses for such categorical survey responses, focusing on the adaptation of established analytic techniques to complex sample survey data. Basic methods for analyzing a single categorical variable are described, including estimation of category proportions, goodness of fit (GOF) tests to compare the sample estimates with hypothesized population values, and graphical display of the estimated population distribution across the K categories of the single variable. The chapter discusses two techniques for multivariate categorical data: the Cochran–Mantel–Haenszel (CMH) test and simple log-linear models for crosstabulated data.