User Guide

57
Chapter
5
Model Selection
Loglinear Analysis
The Model Selection Loglinear Analysis procedure analyzes multiway
crosstabulations (contingency tables). It fits hierarchical loglinear models to
multidimensional crosstabulations using an iterative proportional-fitting algorithm.
This procedure helps you find out which categorical variables are associated. To build
models, forced entry and backward elimination methods are available. For saturated
models, you can request parameter estimates and tests of partial association. A
saturated model adds 0.5 to all cells.
Example. In a study of user preference for one of two laundry detergents, researchers
counted people in each group, combining various categories of water softness (soft,
medium, or hard), previous use of one of the brands, and washing temperature (cold
or hot). They found how temperature is related to water softness and also to brand
preference.
Statistics. Frequencies, residuals, parameter estimates, standard errors, confidence
intervals, and tests of partial association. For custom models, plots of residuals and
normal probability plots.
Data. Factor variables are categorical. All variables to be analyzed must be numeric.
Categorical string variables can be recoded to numeric variables before starting the
model selection analysis.
Avoid specifying many variables with many levels. Such specifications can lead to
a situation where many cells have small numbers of observations, and the chi-square
values may not be useful.
Related procedures. The Model Selection procedure can help identify the terms
needed in the model. Then you can continue to evaluate the model using General