This question seems so basic I am almost embarrassed to ask it, but my need for clarity has finally exceeded my need to pretend like I know everything.
I am testing the effect of prior training on the ability of two groups to learn from an intervention. Learning is measured by correct response to a task, before and after an intervention (factor
baseline = 0,
followup = 1). Prior training is a two-level factor:
no trained = 0 vs
trained = 1.
I want to know how to obtain the estimated difference in log-odds between baseline and follow-up for the training group. Here is the output for the repeated-measures logistic regression.
Estimate Std.Err z-value p-value (Intercept) 0.4726 0.2701 1.7495 0.08020486 trainingtrained 1.6864 0.6563 2.5694 0.01018619 timefollowup 2.5595 0.7607 3.3648 0.00076599 trainingtrained:timefollowup -1.3892 1.2549 -1.1070 0.26829049
The simple effects of time, i.e. the difference in log-odds between baseline and followup in the no training group are easy enough, it is just the coefficient for
timefollowup = 2.5595.
What I am unsure about is: how to calculate the simple effects of time in the training group only?
My guess is that you add the
trainingtrained:timefollowup coefficients together, i.e. 1.6864 + -1.3892 = 0.2972, but I just wanted to check. A little voice is telling me that the intercept needs to be involved somehow but I don't know whether that voice is right or not.