An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and a practical example.
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.
Nimon
Kim
K
Southern Methodist University, Dallas, Texas, USA. kim.nimon@gmail.com
Lewis
Mitzi
M
Kane
Richard
R
Haynes
R Michael
RM
D000465:Algorithms; D001525:Behavioral Sciences / Q000379:methods*; D032882:Comprehension; D003627:Data Interpretation, Statistical; D006801:Humans; D015999:Multivariate Analysis; D011381:Programming Languages; D012044:Regression Analysis; D012942:Social Sciences / Q000379:methods; D012984:Software; D014705:Verbal Behavior