Categorical Data Analysis: Fundamentals and Perspective Applications in Health Sciences YG01-YG04
Department of Statistics, Level 6, Health Sciences Library Building, MAHE, Manipal-576104, Karnataka, India.
This paper introduces the statistical methods for testing differences between paired categorical responses. Application of the independent sample tests while analysing paired data is observed among health science researchers. Four common tests are described in detail for identifying specific differences between pairs of groups. Situation to use each test is discussed in general and in comparison with others. Almost all statistical analysis techniques involve assumptions about the data to be analysed. The paired situation tests including paired t-test and repeated measures analysis of variance requires the distribution of the differences be approximately normal, on the other hand, the unpaired t-test requires an assumption of normality to hold separately for both groups of observations. The data analysis technique also requires an assumption regarding the data generation process. Categorical data analysis approaches provide a series of statistical methods that require limited assumptions on the data. The tests more commonly used are McNemar’s, and Cochran’s Q, while some are not so widely reported, like Stuart Maxwell McNemar’s, and Cochran Mantel Haenszel correlation method.