Myself and a co-author have recently submitted a journal article assessing a particular criminological theory's adequacy in terms of predicting academic dishonesty. The main analysis involved a logistic regression predicting presence of academic dishonesty (yes/no) in a follow-up period, with the IV's being the subscales of an attitudinal test administered at time zero.

We chose to use the subscales (4 items each) of this particular test as predictors rather than the full scale due to very poor factorial fit for a one factor model, but good fit for a multifactorial model based on the test subscales. One of the reviewers highlighted the low-ish internal consistency reliability of these subscales (~0.6+), and suggested looking at a correction for attenuation in relationships due to measurement error.

I've read a little about corrections for measurement error in logistic regression, but have ended up a bit confused. Thoreson and Laake seem to suggest that maximum likelihood estimation is the optimum option to deal with this. As I understand it SPSS uses ML for the estimation of logistic regression models, but surely this is something a bit different? I can't see how the automatic SPSS estimation procedure could correct for measurement error.

Has anyone had any experience with measurement error corrections in logistic regression? Any suggestions for correction methods that are reasonably easy to execute?