The Model

Logit loglinear models are "ANOVA-like" models for the logit-expected cell counts of crosstabulation tables. Logits are formed by the log-ratios of cell counts, where the cells in a given logit are correspond to pairs of values of the dependent variable, for a given cross-classification of factors.

Dependent Variables. Dependent variables are categorical responses whose values you want to model.

Factors. Factors are categorical predictor variables that help define the crosstabulation table.

Covariates. Scale predictors can be added as covariates in the model. Within cells of the crosstabulation table, the mean covariate values of cases in the cell are used to model the cell counts.

Cell Structure. The cell structure variable allows you exclude cells from the analysis. This can be helpful if you want to impose a particular structure on the crosstabulation table. See the General Loglinear Analysis case studies for further uses of the cell structure variable.

Contrasts. You can specify a set of contrast variables to test the differences between model effects.

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