The purpose of this post is to provide some guidelines to aid in inferring causation in social science research. At the moment, the post just defines confounding variables, distinguishes them from other third-variables (e.g. mediators and colliders), and provides some examples of statistical techniques that can be used to control for confounding variables. In the future, I hope to cover in more detail regression analysis, causal diagrams, multicollinearity, and other concepts important to understand to infer causation in the social sciences.
Interpreting Effect Sizes
Note: this post is a work in progress. The effect size is a statistic that quantifies the magnitude of the association between two variables. It is arguably the most important statistic reported in any study attempting to report the relationship between different variables. This purpose of this post is to aid in interpreting effect sizes, particularly from a social science perspective. There are several different kinds of statistics that are used to report effect sizes, […]