User Guide

Ordinal Regression

 Summary statistics. Cox and Snell’s, Nagelkerke’s, and McFadden’s statistics.

 Parameter estimates. Parameter estimates, standard errors, and confidence intervals.

 Asymptotic correlation of parameter estimates. Matrix of parameter estimate

correlations.

 Asymptotic covariance of parameter estimates. Matrix of parameter estimate

covariances.

 Cell information. Observed and expected frequencies and cumulative frequencies,

Pearson residuals for frequencies and cumulative frequencies, observed and

expected probabilities, and observed and expected cumulative probabilities of each

response category by covariate pattern. Note that for models with many covariate

patterns (for example, models with continuous covariates), this option can generate

a very large, unwieldy table.

 Test of parallel lines. Test of the hypothesis that the location parameters are

equivalent across the levels of the dependent variable. This is available only for the

location-only model.

Saved variables. Saves the following variables to the working file:

 Estimated response probabilities. Model-estimated probabilities of classifying a

factor/covariate pattern into the response categories. There are as many

probabilities as the number of response categories.

 Predicted category. The response category that has the maximum estimated

probability for a factor/covariate pattern.

 Predicted category probability. Estimated probability of classifying a

factor/covariate pattern into the predicted category. This probability is also the

maximum of the estimated probabilities of the factor/covariate pattern.

 Actual category probability. Estimated probability of classifying a factor/covariate

pattern into the actual category.

Print log-likelihood. Controls the display of the log-likelihood. Including multinomial

constant

gives you the full value of the likelihood. To compare your results across

products that do not include the constant, you can choose to exclude it.