Recently, I came across a social science paper that had used linear probability regression. I had never heard of linear probability models (LPM), but it seems just an application of ordinary least squares regression but to a binomial dependent variable.
According to some, LPM is a commonly used alternative for logistic regression, which is what I was learned to use when the outcome is binary.
Potentially because of my own social science background (HRM), using linear regression without a link transformation on binary data just seems very unintuitive and error-prone to me. Hence, I sought for more information.
I particularly liked this article by Jake Westfall, which he dubbed “Logistic regression is not fucked”, following a series of blogs in which he talks about methods that are fucked and not useful.
Jake explains the classification problem and both methods inner workings in a very straightforward way, using great visual aids. He shows how LMP would differ from logistic models, and why its proposed benefits are actually not so beneficial. Maybe I’m in my bubble, but Jake’s arguments resonated.
Read his article yourself:
Here’s the summary:
Arguments against the use of logistic regression due to problems with “unobserved heterogeneity” proceed from two distinct sets of premises. The first argument points out that if the binary outcome arises from a latent continuous outcome and a threshold, then observed effects also reflect latent heteroskedasticity. This is true, but only relevant in cases where we actually care about an underlying continuous variable, which is not usually the case. The second argument points out that logistic regression coefficients are not collapsible over uncorrelated covariates, and claims that this precludes any substantive interpretation. On the contrary, we can interpret logistic regression coefficients perfectly well in the face of non-collapsibility by thinking clearly about the conditional probabilities they refer to.