Multicollinearity

This fact sheet explains multicollinearity in regression models and why it undermines the interpretation of individual predictors rather than the overall model fit. It describes common causes of multicollinearity, how it manifests in regression output, and how sample size influences its impact on coefficient stability and precision. The document provides practical guidance on diagnosing multicollinearity using correlations and variance inflation factors, and outlines evidence-based strategies for addressing it in explanatory analyses.