# 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.

Model Selection Loglinear Analysis Data Considerations

**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 Loglinear Analysis or Logit Loglinear Analysis.
You can use Autorecode to recode string variables. If a numeric variable
has empty categories, use Recode to create consecutive integer values.

Obtaining a Model Selection Loglinear Analysis

This feature requires Custom Tables and Advanced Statistics.

From the menus choose:

- Select two or more numeric categorical factors.
- Select one or more factor variables in the Factor(s) list, and click Define Range.
- Define the range of values for each factor variable.
- Select an option in the Model Building group.

Optionally, you can select a cell weight variable to specify structural zeros.

This procedure pastes HILOGLINEAR command syntax.