Abstract

The use of linear regression is ubiquitous across the social and behavioral sciences, and yet researchers rarely hold that the variables in their target systems are in fact linearly related. This raises the question of how to interpret linear regression coefficients when there is ‘functional misspecification’ and the target system exhibits nonlinearity. Here, recent methodological discussions among practitioners have mirrored philosophical debates over scientific realism. In this paper, I frame the prevailing views in terms of their stance on the scientific realism debate. I then present a novel realist interpretation of linear regression—the ‘secant interpretation’—based on a property I derive about the manner in which linear regression coefficients represent properties of non-linear systems of interest.