An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and a practical example.

@article{citeulike:2925158, abstract = {Multiple regression is a widely used technique for data analysis in social and behavioral research. The complexity of interpreting such results increases when correlated predictor variables are involved. Commonality analysis provides a method of determining the variance accounted for by respective predictor variables and is especially useful in the presence of correlated predictors. However, computing commonality coefficients is laborious. To make commonality analysis accessible to more researchers, a program was developed to automate the calculation of unique and common elements in commonality analysis, using the statistical package R. The program is described, and a heuristic example using data from the Holzinger and Swineford (1939) study, readily available in the MBESS R package, is presented.}, address = {Southern Methodist University, Dallas, Texas, USA. kim.nimon@gmail.com}, author = {Nimon, K. and Lewis, M. and Kane, R. and Haynes, R. M. }, citeulike-article-id = {2925158}, issn = {1554-351X}, journal = {Behavior research methods}, keywords = {collinearity, r, regression}, month = {May}, number = {2}, pages = {457--466}, posted-at = {2008-06-25 07:35:36}, priority = {2}, title = {An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and a practical example.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/18522056}, volume = {40}, year = {2008} }

TY - JOUR ID - citeulike:2925158 L3 - citeulike-article-id:2925158 TI - An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and a practical example. JF - Behavior research methods VL - 40 IS - 2 SP - 457 EP - 466 AD - Southern Methodist University, Dallas, Texas, USA. kim.nimon@gmail.com SN - 1554-351X N2 - Multiple regression is a widely used technique for data analysis in social and behavioral research. The complexity of interpreting such results increases when correlated predictor variables are involved. Commonality analysis provides a method of determining the variance accounted for by respective predictor variables and is especially useful in the presence of correlated predictors. However, computing commonality coefficients is laborious. To make commonality analysis accessible to more researchers, a program was developed to automate the calculation of unique and common elements in commonality analysis, using the statistical package R. The program is described, and a heuristic example using data from the Holzinger and Swineford (1939) study, readily available in the MBESS R package, is presented. KW - collinearity KW - r KW - regression AU - Nimon, K AU - Lewis, M AU - Kane, R AU - Haynes, RM PY - 2008/05// UR - http://view.ncbi.nlm.nih.gov/pubmed/18522056 ER -