SPSS® 12.0 Command Syntax Reference

LOGLINEAR 817
multiway contingency tables, logit models, logistic regression on categorical variables,
and quasi-independence models.
LOGLINEAR models cell frequencies using the multinomial response model and produces
maximum likelihood estimates of parameters by means of the Newton-Raphson algorithm
(Haberman, 1978). HILOGLINEAR, which uses an iterative proportional-fitting algorithm, is
more efficient for hierarchical models, but it cannot produce parameter estimates for unsaturated
models, does not permit specification of contrasts for parameters, and does not display a
correlation matrix of the parameter estimates.
Comparison of the GENLOG and LOGLINEAR Commands
The General Loglinear Analysis and Logit Loglinear Analysis dialog boxes are both associated
with the GENLOG command. In previous releases of SPSS, these dialog boxes were
associated with the LOGLINEAR command. The LOGLINEAR command is now available only
as a syntax command. The differences are described below.
Distribution assumptions
• GENLOG can handle both Poisson and multinomial distribution assumptions for observed
cell counts.
• LOGLINEAR assumes only multinomial distribution.
Approach
• GENLOG uses a regression approach to parameterize a categorical variable in a design
matrix.
• LOGLINEAR uses contrasts to reparameterize a categorical variable. The major disadvantage
of the reparameterization approach is in the interpretation of the results when there
is a redundancy in the corresponding design matrix. Also, the reparameterization
approach may result in incorrect degrees of freedom for an incomplete table, leading to
incorrect analysis results.
Contrasts and generalized log-odds ratios (GLOR)
• GENLOG doesn’t provide contrasts to reparameterize the categories of a factor. However,
it offers generalized log-odds ratios (GLOR) for cell combinations. Often, comparisons
among categories of factors can be derived from GLOR.
• LOGLINEAR offers contrasts to reparameterize the categories of a factor.
Deviance residual
• GENLOG calculates and displays the deviance residual and its normal probability plot, in
addition to the other residuals.
• LOGLINEAR does not calculate the deviance residual.
Factor-by-covariate design
• When there is a factor-by-covariate term in the design, GENLOG generates one regression
coefficient of the covariate for each combination of factor values. The estimates of these
regression coefficients are calculated and displayed.
• LOGLINEAR estimates and displays the contrasts of these regression coefficients.