Which method is commonly used to analyze multicollinearity in a dataset?

The method that is commonly used to analyze multicollinearity in a dataset involves both the correlation matrix and variance inflation factors (VIF). The correlation matrix allows researchers to observe the pairwise relationships between independent variables, identifying any strong correlations that may signal multicollinearity. A higher correlation between two or more predictor variables indicates potential multicollinearity issues.

Variance inflation factors provide a numerical measure of how much the variance of the estimated regression coefficients increases when your predictors are correlated. Specifically, a VIF value greater than 10 is often taken as an indication that multicollinearity may be present and affecting the reliability of the coefficient estimates.

In combination, these two approaches effectively highlight the presence and extent of multicollinearity, allowing analysts to make more informed decisions about model specifications and variable selection. Other methods mentioned, such as regression diagnostics, principal component analysis, and residual plots, do not address the analysis of multicollinearity as comprehensively or directly as the correlation matrix and VIF.