Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect patterns that may have gone...

We describe how to employ machine learning (ML) methods in theory development. Compared to traditional causal inference methods, ML methods make far fewer a priori assumptions about the functional form of the underlying model that best represents the data. Given this, researchers could use such...

Though it is a pervasive problem, relatively little research focuses on rural poverty and leadership initiatives designed to alleviate rural poverty. Using a comparative case study approach, this article assesses community-level change in rural communities in Montana and Minnesota that...

Mortgage application denial rates have increased since 2002, but it is unclear to what extent this increase in denials has differentially affected “non-traditional” mortgage applicants that do not resemble “traditional” white, non-Hispanic opposite-sex couple applicants. This article...