multikollineaarsusele

Multikollineaarsusel, also known in English as multicollinearity, describes a statistical phenomenon in which two or more explanatory variables in a regression model are highly linearly related. The term originates from the Latin roots “multi” and “collinearity,” and is commonly used in quantitative research across disciplines such as economics, psychology, and epidemiology. When multicollinearity is present, the estimated regression coefficients become sensitive to small changes in the data and the standard errors inflate, leading to unreliable inference about the relationships between variables.

Detection of multicollinearity is typically performed through diagnostic statistics. The variance inflation factor (VIF) is a popular measure; values above 5 or 10 are often taken as indicators of problematic collinearity. Correlation matrices and condition indices (obtained from eigenvalue decomposition of the predictor matrix) are also frequently employed. The tolerance statistic, the reciprocal of the VIF, provides another viewpoint on the relative contribution of each variable to the model.

Addressing multicollinearity may involve several strategies. Variable selection procedures, such as stepwise regression or penalization techniques like ridge regression and LASSO, can diminish redundant predictors. Principal component analysis (PCA) offers an alternative by transforming correlated variables into a smaller set of orthogonal components that retain most of the variance. In some cases, theoretical considerations suggest that highly correlated variables represent distinct constructs and should be retained; in these scenarios, careful interpretation of coefficients is necessary.

Although multicollinearity does not bias the overall fit of a regression model, it hampers the precision of coefficient estimates and complicates hypothesis testing. Researchers should therefore routinely assess multicollinearity during model building, especially when the goal is to attribute causal or substantive meaning to individual predictors.