Sorry, you're right, that's unclear. We chose to run a logistic regression on a subset of factors that were statistically significant (i.e. every factor in the graph is significant). In other words, we chose the factors that had the largest effect size and then plugged them into the regression.
so wait -- you ran the regression with lots of factors, and then dropped those you found not to be statistically significant?
I would suggest it's not good practice to drop variables, even if they aren't statistically significant (and what an argument that can become if you test things simultaneously). Particularly if there's any chance they are correlated with other variables. Read Pearl; causality (which is what you're really discussing) is a rat's nest.
Also, when you plot the value of the coefficients, I would suggest ordering them by abs(coef) * stddev(var). This may not matter much for you since it looks like most of your variables are indicator variables, but it's still good practice.
edit: oh, hi, you're the Aline that wrote this? Thanks for the interesting analysis.
I ran a number of different regressions in parallel with significance testing. Ultimately, I chose to publish the figures from the one that included the factors with the largest effect sizes (determined in parallel) for simplicity, but the takeaways didn't really differ much when more factors (even ones that weren't significant) were included.
Regardless, thanks for pointing me to Pearl. Linking here for others in case they're interested, too: http://bayes.cs.ucla.edu/BOOK-2K/
