You using the Nominal Logistic Fit platform. You say that the response has five levels. If that is true, then you would have a different report. Does the column with the response really have five levels (1-5)? Is it using character or numeric data type? Is it using the ordinal modeling type? Such a response is ordinal in nature so you can take advantage of the extra information about rankings as well as differences. This arrangement should automatically lead to the Ordinal Logistic Fit platform (starting with Analyze > Fit Model).
The parameter estimate is coefficient of your linear predictor. So it represents the change in the response if you have a particular level of a categorical predictor or a change of 1 unit for a continuous predictor. It means the same thing as in a multiple regression analysis with a continuous response.
The difference here is the response. It is continuous through a transformation: the logit. You are fitting the linear predictor to the log( odds ). The ordinal response uses the cumulative logit, so you are fitting the linear predictor to the log( Pr( level i or below ) / Pr( level i+1 or higher ). In your case you will have four logits (the number of logits is always one less than the number of response levels). In effect, you have four sets of parameter estimates but they use a common intercept in the case of an ordinal response.
You can observe the explicit form of the ordinal logistic regression model by selecting Save Columns > Prediction Formula and then examining the data table. You will have one column for the linear predictor and then the back transformation for the probability of each level. Finally, you will have the prediction, which is the level with the highest probability.
