I recently ran two tests in R - one using glm() and one using lm() with the goal being to test the relationship between a binary response and binary predictor. I ran glm() first and got an estimate of -0.68 for the predictor coefficient which I thought was pretty good. P<.05 and AIC of 653.
When I ran lm() however I got an estimate of -.14, a multiple r-squared of .008, P<.05.
My understanding is that linear regression is usually a poor choice for a categorical response compared with logistic regression, but when is this not the case? I noticed in this post http://statisticalhorizons.com/linear-vs-logistic that the author states there's middle ground where it does make sense to use linear regression. Are there any common rules (or rules of thumb you personally use) that determine when to try out linear regression on a categorical response? Do any of these differ from the author's cases?