Commonality Analysis: Partitioning Variance to Facilitate Better Understanding of Data

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Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity (2012, methodology): “This method allows a predictor’s contribution to be related to other predictor variables in the model, providing a clear picture of the predictor’s role in the explanation by itself, as well as with the other predictors (Rowell, 1991, 1996; Thompson, 2006; Zientek and Thompson, 2006).”
Understanding the Results of Multiple Linear Regression (2013, background): “Note that negative values of commonality coefficients generally indicate that a predictor exerts a suppressor effect, where it is removing (partialing out) the irrelevant variance in the other predictor(s) to increase the latter’s contributions to the model R2 (Zientek & Thompson, 2006).”
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance (2012, background): “The suppressor removes the irrelevant variance in the other variable or variables in the common effect to increase the other variable(s)‘ variance contributions to the regression effect (DeVito, 1976; Zientek & Thompson, 2006).”
Matrix Summaries Improve Research Reports: Secondary Analyses Using Published Literature (2009, methodology): “Commonality analysis can be employed to supplement either univariate (Mood, 1969; Zientek & Thompson, 2006) or multivariate (Frederick, 1999) analyses.”