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An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and
Kim Nimon1, Mitzi Lewis, Richard Kane
1Southern Methodist University, Dallas, Texas, USA. kim.nimon@gmail.com
Behavior Research Methods
|June 5, 2008
Summary
This study introduces an R program to simplify commonality analysis for researchers. It automates calculations for correlated predictors in multiple regression, making complex data interpretation more accessible.
Area of Science:
- Social and behavioral sciences
- Statistical analysis
- Psychometrics
Background:
- Multiple regression is a common statistical technique.
- Interpreting regression with correlated predictors is complex.
- Commonality analysis quantifies variance explained by predictors.
Purpose of the Study:
- To develop an automated program for commonality analysis.
- To simplify the computation of commonality coefficients.
- To enhance accessibility of commonality analysis for researchers.
Main Methods:
- Developed a program using the R statistical package.
- Automated the calculation of unique and common variance elements.
- Utilized a heuristic example from the Holzinger and Swineford (1939) study.
Main Results:
- The program successfully automates commonality analysis.
- Provides accessible computation of commonality coefficients.
- Demonstrates utility with a real-world dataset.
Conclusions:
- The R program facilitates complex regression interpretation.
- Increases the practical application of commonality analysis.
- Supports researchers in understanding predictor variance.
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