Determining predictor importance in multiple regression under varied correlational and distributional conditions

Journal of Modern Applied Statistical Methods

Volumn 1, Issue 2, 2002, Pages 354-366

  Whittaker, Tiffany A ^a   Fouladi, Rachel T ^b,c   Williams, Natasha J ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^b UNIVERSITY OF TEXAS   (United States)

^c UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

Author keywords

Multicollinearity; Multiple Regression; Predictor Importance; Relative Importance

Indexed keywords

EID: 18444400196     PISSN: 15389472     EISSN: None     Source Type: Journal    
DOI: 10.22237/jmasm/1036110360     Document Type: Article

References (19)

1. Bring, J. (1994). How to standardize regression coefficients. The American Statistician, 48(3), 209-213.
2. Budescu, D. V. (1993). Dominance analysis: A new approach to the problem of relative importance of predictors in multiple regression. Psychological Bulletin, 114(3), 542-551.
3. Cohen, J., & Cohen, P. (1975). Applied multiple regression/ correlation analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.
4. Cortés, L. L., Montgomery, E. W., Morrow, K. A., & Monroe, D. M. (December, 2000). A statewide assessment of quality of care, quality of life and consumer satisfaction in Texas Medicaid nursing facilities. Austin, TX: Texas Department of Human Services, Long Term Care Office of Programs, Medical Quality Assurance.
5. Darlington, R. B. (1968). Multiple regression in psychological research and practice. Psychological Bulletin, 69(3), 161-182.
6. Fouladi, R. T. (January, 2001). Texas Department of Human Services Rider 26 longitudinal study of nursing home quality of care. Austin, TX: Texas Department of Human Services, Long Term Care Office of Programs, Medical Quality Assurance.
7. Gibson, W. A. (1962). Orthogonal predictors: A possible resolution of the Hoffman-Ward controversy. Psychological Reports, 11, 32-34.
8. Green, P. E., Carroll, J. D., & DeSarbo, W. S. (1978). A new measure of predictor importance in multiple regression. Journal of Marketing Research, 15, 356-360.
9. Hamilton, L. C. (1992). Regression with graphics. Belmont, CA: Duxbury Press.
10. Headrick, T. C. & Sawilowsky, S. S. (1999). Simulating correlated multivariate nonnormal distribution: Extending the Fleishman power method. Psychometrika, 64(1), 25-35.
11. Healy, M. J. R. (1990). Measuring importance. Statistics in medicine, 9, 633-637.
12. Johnson, J. W. (2000). A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivariate Behavioral Research, 35(1), 1-19.
13. Johnson, R. M. (1966). The minimal transformation to orthonormality. Psychometrika, 31, 61-66.
14. Kruskal, W. (1984). Concepts of relative importance. Qüestiió , 8(1), 39-45.
15. Lindeman, R. H., Merenda, P. F., & Gold, R. Z. (1980). Introduction to bivariate and multivariate analysis. Glenview, IL: Scott, Foresman and Company.
16. Micceri, T. (1989). The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin, 105(1), 156-166.
17. rd ed.). Chicago, IL: Richard D. Irwin, Inc.
18. Pratt, J. W. (1987). Dividing the indivisible: Using simple symmetry to partition variance explained. In T. Pukkila & S. Puntanen (Eds.), Proceedings of Second International Tampere Conference in Statistics (pp. 245-260). University of Tampere, Finland.
19. Vale, C. D., & Maurelli, V. A. (1983). Simulating multivariate nonnormal distributions. Psychometrika, 48, 465-471.