Making sense of commonality analysis
What proportion of variance in intelligence (e.g., IQ) can be uniquely attributed to genetic factors, independent of socio-economic influences? How do neuroimaging data from different modalities (e.g., fMRI and M/EEG) relate to one another and to theoretical models?
These questions highlight the critical role of shared variance in understanding complex systems. Commonality analysis provides a valuable tool for addressing such questions by partitioning the explained variance (\(R^{2}\) ) in multiple regression into distinct components. It identifies how much variance is uniquely attributable to each predictor and how much arises from shared contributions among predictors. This approach helps to clarify multivariate relationships and assess the relative importance of each independent variable (Seibold & McPhee, 1979).
Disclaimer: This post reflects my personal effort to gain a better understanding of commonality analysis and should be interpreted with caution. I do not claim to have any special expertise on this topic.
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