Title: Building a model - the effects of age?
Course: UGQuants (SSPS) > Section: Logistic Regression

Building a model - the effects of age?

How far are these correlations expressing an indirect effect of age?  Older people are far less likely to have surpassed the basic amount of school education (the expansion of higher education only started in the 1960s).  They are also more likely to be religious and conservative in their political orientation.

We could control for age using partial tables if age took only two or three values.  Indeed in situations where we have only nominal or ordinal variables, no more than three or four of these variables and none of the variables takes a large number of categories, it is generally preferable to use a three or four way contingency table using the methods we looked at in an earlier module.  Such tables are not only intuitively more understandable, they use the maximum possible information.  However, in our example here age is a ratio level variable with many categories (we have only simplified it in the table to make its effect easy to see), and some other variables also have a scale of ten values, so tables are not a practical option.  We cannot use linear regression, as our dependent variable has only two categories.  This is where we can use logistic regression to answer our question.

First of all, we can run a simple model which shows the effect of age.  To do this, in SPSS, (remember to weight the data with the variable dweight) we select analyze, then regression, and then binary logistic.  This brings up the logistic regression dialog box, which is similar to the dialog box for linear regression.  Put mnrgtjb_bin (the cleaned up binary version of the indicator) in the box for dependent variables and put agea in the covariates box and click on OK.  We can also ask SPSS to produce 95% confidence intervals for the values for the Exp(B) coefficients, by clicking on options and selecting confidence intervals: CI for exp(B).  This gives us the following results.

SPSS first runs a model with no independent variables, known as the null model (roughly equivalent to a linear regression equation with only the mean of the dependent variable as a constant).  This shows us what we might predict or guess in the absence of any information about our respondents.  Since 72% of our respondents disagree with 'male breadwinner ideology' if we predicted that they all did, we would be right 68.2% of the time: correct for all those who actually did disagree, and wrong for all of the 31.8% who did not.

SPSS then runs our simple model with age as an independent, ratio level variable.  We can predict about 70.3% of our cases correctly.  However, more useful still is the fact that we can now use this model to test the impact of our other variables of interest.  The following tables show the SPSS output from our logistic regression model.

What do these outputs mean?