The CATMOD Procedure
-
Overview
-
Getting Started
-
Syntax
-
Details
Missing Values Input Data Sets Ordering of Populations and Responses Specification of Effects Output Data Sets Logistic Analysis Log-Linear Model Analysis Repeated Measures Analysis Generation of the Design Matrix Cautions Computational Method Computational Formulas Memory and Time Requirements Displayed Output ODS Table Names
-
Examples
Linear Response Function, r=2 Responses Mean Score Response Function, r=3 Responses Logistic Regression, Standard Response Function Log-Linear Model, Three Dependent Variables Log-Linear Model, Structural and Sampling Zeros Repeated Measures, 2 Response Levels, 3 Populations Repeated Measures, 4 Response Levels, 1 Population Repeated Measures, Logistic Analysis of Growth Curve Repeated Measures, Two Repeated Measurement Factors Direct Input of Response Functions and Covariance Matrix Predicted Probabilities
- References
| Linear Models Contrasted with Log-Linear Models |
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.