How are confidence intervals for proportions computed under SPSS? I wonder whether any of you can help me. SPSS puts confidence intervals in graphs of frequencies and proportions,
but it clearly is not using a normal approximation, i.e. it is not using the formula $\text{CI} = m \pm 1.96 \sqrt{(p(1-p)/n}$.
apart from anything else the SPSS interval is not symmetrical around the value.
So what is it doing? I could not track down the formula anywhere, not even searching on the web (which usually
solves 99% of problems in life, plus or minus 1%).  :)
 A: Statistics graphics use Jeffreys binomial for the CIs.
A: If you paste the syntax from the Chart Builderfor an error bar chart, you can add labeling syntax to it.  For example, the error bars as pasted from the CB would be specified like this:
  ELEMENT: interval(position(region.spread.range(jobcat*(LOW+HIGH))), shape.interior(shape.ibeam))
Adding the label function as below will cause the values to be displayed.  You might need to increase the number of decimals displayed by using the CE.  That could be saved as a chart template.
ELEMENT: interval(position(region.spread.range(jobcat*(LOW+HIGH))), shape.interior(shape.ibeam),label(LOW),label(HIGH))
There is also an extension command, PROPOR, that will do all this in a tabular way.  However, it requires that the Python Essentials and the PROPOR command be installed from the SPSS Community website (www.ibm.com/developerworks/spssdevcentral)
You can get bootstrapped ci's using Bootstrap and Frequencies.  See the bootstrapping case study in help for details.
And you can get a Wald CI from GENLIN using an intercept-only model.
HTH,
Jon Peck
