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About: Commonality analysis     Goto   Sponge   NotDistinct   Permalink

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Commonality analysis is a statistical technique within multiple linear regression that decomposes a model's R2 statistic (i.e., explained variance) by all independent variables on a dependent variable in a multiple linear regression model into commonality coefficients. These coefficients are variance components that are uniquely explained by each independent variable (i.e., unique effects), and variance components that are shared in each possible combination of the independent variables (i.e., common effects). These commonality coefficients sum up to the total variance explained (model R2) of all the independent variables on the dependent variable. Commonality analysis produces 2k − 1 commonality coefficients, where k is the number of the independent variables.