Title: Analysis of residuals
Course: UGQuants (SSPS) > Section: Logistic Regression

Analysis of residuals

While we have a possible answer to our question, we need to do some further checks to increase our confidence in the model.  Just as in linear regression, there are various diagnostic tests we can undertake to evaluate our model. The first is simply a classification plot of the model's predictions versus the observations in our data (you can ask SPSS for this in the ‘options…’ window).  This gives more information than the classification table we have used up until now, as in addition to showing how our model has classified cases, it shows the actual probabilities produced by the model (while the classification table simply divides them into two groups based on whether the probability is greater than or less than 0.5).

If our model is a good one, then we would like to see the predicted cases not only corresponding to the observed cases, but clustering towards the extremes of the classification plot, that is to say with predicted probabilities tending towards either zero or one.  If our model is less robust, and is less good at dividing up the cases into two groups, then the predicted probabilities will be closer to 0.5.  As the classification plot below shows, our model is not particularly good in this sense and, as the classification table suggested earlier, is better at predicting the cases where respondents did explicitly disagree with the statement of men having more rights to a job in economically difficult times.

Figure 3: Classification plot