Test of Parallel Lines

Figure 1. Test of parallel lines
Test of parallel lines table showing -2 log-likelihood, chi-square, degrees of freedom, and significance. The significance value is less than .0005.

For location-only models, the test of parallel lines can help you assess whether the assumption that the parameters are the same for all categories is reasonable. This test compares the estimated model with one set of coefficients for all categories to a model with a separate set of coefficients for each category. You can see that the general model (with separate parameters for each category) gives a significant improvement in the model fit. This can be due to several things, including use of an incorrect link function or using the wrong model.

It is also possible that the poor model fit is due to the chosen ordering of the categories of the dependent variable. An ordering that places No debt history as a greater credit risk may have a better fit. It would also be interesting to examine this data file using the Multinomial Logistic Regression procedure, since it allows you to avoid the ordering issues and also allows different effects of predictors.

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