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 the Advanced Statistics option.
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.