There are several methods to detect multicollinearity. The most common ones are:
1. Variance Inflation Factor (VIF): VIF quantifies how much the variance of a regression coefficient is inflated due to multicollinearity. A VIF value above 10 is often considered indicative of high multicollinearity.
2. Correlation Matrix: By examining the correlation coefficients between pairs of predictor variables, researchers can identify highly correlated pairs (usually, a correlation coefficient above 0.8 suggests multicollinearity).
3. Condition Index: This method involves computing the condition number from the eigenvalues of the predictors' correlation matrix. A condition number above 30 indicates potential multicollinearity issues.
