Linear model methods typified by the Grizzle, Starmer, and Koch (1969) approach make a very clear distinction between independent and dependent variables. The emphasis of these methods is estimation and hypothesis testing of the model parameters. Therefore, it is easy to test for differences among probabilities, perform repeated measures analysis, and test for marginal homogeneity, but it is difficult to test for independence and generalized independence. These methods are a natural extension of the usual ANOVA approach for continuous data.
In contrast, log-linear model methods typified by the Bishop, Fienberg, and Holland (1975) approach do not make an a priori distinction between independent and dependent variables, although model specifications that allow for the distinction can be made. The emphasis of these methods is on model building, goodness-of-fit tests, and estimation of cell frequencies or probabilities for the underlying contingency table. With these methods, it is easy to test independence and generalized independence, but it is difficult to test for differences among probabilities, do repeated measures analysis, and test for marginal homogeneity.