For a categorical factor with more than 2 levels, the hypothesis for the coefficient is about whether that level of the factor is different from the reference level for the factor. To assess the statistical significance of the factor, use the test for terms with more than 1 degree of freedom. For more information on how to display this test, go to Select the results to display for Ordinal Logistic Regression.
To determine how well the model fits the data, examine the log-likelihood and the measures of association. Larger values of the log-likelihood indicate a better fit to the data. Because log-likelihood values are negative, the closer to 0, the larger the value. The log-likelihood depends on the sample data, so you cannot use the log-likelihood to compare models from different data sets.
The log-likelihood cannot decrease when you add terms to a model. For example, a model with 5 terms has higher log-likelihood than any of the 4-term models you can make with the same terms. Therefore, log-likelihood is most useful when you compare models of the same size. To make decisions about individual terms, you usually look at the p-values for the term in the different logits.
Larger values for Somers' D, Goodman-Kruskal gamma, and Kendall's tau-a indicate that the model has better predictive ability. Somers' D and Goodman-Kruskal gamma can be between -1 and 1. Kendall's tau-a can be between -2/3 and 2/3. Values close to the maximum indicate the model has good predictive ability. Values close to 0 indicate that the model does not have a predictive relationship with the response. Negative values are rare in practice because that performance is worse than when the model and the response are unrelated.
In this second set of results, the distance and the square of the distance are both predictors. You cannot use the log-likelihood to compare these models because they have different numbers of terms. The measures of association are higher for the second model, which indicates that the second model performs better than the first model.