One approach to the analysis of categorical data that we have not discussed is the method of weighted least squares, as developed by Grizzle, Starmer, and Koch ( 1969). In this methodology, a transformation of the cell probabilities is made so that the "usual" linear model, as applied to continuous data, holds, approximately, for this transformed data. This methodology has the advantage of being very flexible but has some shortcomings in comparison with maximum likelihood techniques.
We have not talked at all about defining or making inferences about measures of association in contingency tables. The series of papers by Goodman and Kruskal ( 1954, 1959, 1963, 1972) should be referred to here. For material on the related topic of measures of agreement see Cohen ( 1960) or Lin ( 1974). We have not discussed tables where there is an ordering of the categories of certain variables. Haberman ( 1974a) and Williams and Grizzle ( 1972) discuss this from the log-linear and weighted least squares approaches, respectively. There has recently been interest in the analysis of contingency tables arising from cluster or stratified sampling schemes. Altham ( 1976), Brier ( 1979), and Cohen ( 1976) discuss some aspects of this problem.
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