That is all we need to change in this example so click Continue to close the submenu and then OK on the main menu to run the analysis. This will save, for each case in the data file, the predicted probability of achieving each outcome category, in this case the estimated probabilities of the student achieving each of the levels (3, 4, 5, 6 and 7). Put a tick in the Estimated response probabilities box. This can be particularly useful during model diagnostics. You also see here options to save new variables (see under the ‘Saved Variables’ heading) back to your SPSS data file. This is essential as it will ask SPSS to perform a test of the proportional odds (or parallel lines) assumption underlying the ordinal model (see. Also place a tick in the Test of parallel lines box. However, this is not recommended for models with many factors or for models with continuous covariates, since such models typically result in very large tables which are often of limited value in evaluating the model because they are so extensive (they are so extensive, in fact, that they are likely to cause severe mental distress). For relatively simple models with a few factors this can help in evaluating the model.

Move English level ( k3en) to the ‘Dependent’ box and gender to the ‘Factor(s)’ box.

You access the menu via: Analyses > Regression > Ordinal. It is, however, slightly fiddly and annoying!

if we wanted boys to be the reference category we could recode gender so girls=0 and boys=1). Again this is not a huge problem because if we want to we can simply RECODE our variables to force a particular category as the reference category (e.g. So for our gender variable (scored boys=0, girls=1) girls will be the reference category and the coefficients will be for boys. 11 ) as SPSS ordinal automatically takes the LAST category as the reference category. Second, for categorical ( nominal or ordinal) explanatory variables, unlike logistic regression, we do not have the option to directly specify the reference category (LAST or FIRST, see. Don’t worry this will be clear in the example. However this makes little practical difference to the calculation, we just have to be careful how we interpret the direction of the resulting coefficients for our explanatory variables. This differs from our example above and what we do for logistic regression. First, for the dependent (outcome) variable, SPSS actually models the probability of achieving each level or below (rather than each level or above).

So let’s see how to complete an ordinal regression in SPSS, using our example of NC English levels as the outcome and looking at gender as an explanatory variable.īefore we get started, a couple of quick notes on how the SPSS ordinal regression procedure works with the data, because it differs from logistic regression.

Spss 22 tutorial ppt