In my research I am looking at the effect of 3 IV's on a binary DV that is measured repeatedly on the same sample after different treatments. Therefore, for every treatment, I have created 3 binary logistic regression models that each test the effect of a different IV together with a set of control variables. Thus:

Treatment 1: Model 1: IV1 + controls Model 2: IV2 + controls Model 3: IV3 + controls

This for every treatment. For methodology reasons interaction effects are not necessary/possible.

The results have been very interesting and useful until I got to a certain treatment, let's call it treatment X. For this treatment, none of the logistic regression models were significant. I have tried omitting the least significant control variables, but the model does not improve enough. None of the IV's has a significant effect on the DV in this treatment, so that is a conclusion in itself. However, how can I explain in the discussion why it was not possible to create any significant model for this condition?


1 Answer 1


The most likely reasons:

  • Your theory was wrong.
  • You didn't have enough power.
  • Your variables were poorly operationalized.
  • You had an omitted variable that was important.

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.