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A Heuristic Method for Estimating the Relative Weight of Predictor Variables in Multiple Regression
Abstract
The relative weight of predictor variables in multiple regression is difficult to determine because of non-zero predictor intercorrelations. Relative weight (also called relative importance by some researchers) is defined here as the proportionate contribution each predictor makes to R2, considering both its unique contribution and its contribution when combined with other variables. Although there are no unambiguous measures of relative weight when variables are correlated, some measures have been shown to provide meaningful results (Budescu, 1993; Lindeman, Merenda, & Gold, 1980). These measures are very difficult to implement, however, when the number of predictors is greater than about five. A method is proposed that is computationally efficient with any number of predictors, and is shown to produce results that are very similar to those produced by more complex methods. Recommendations are made for when this procedure may be applied and in what situations it is not appropriate.
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Research works printed but not linked: 29
- Afifi, A. A. & Clarke, V. (1990). Computer aided multivariate analysis (2nd ed.). New York: Van Nostrand Reinhold.
- 10.1037//0021-9010.80.1.168 · DOI
- Bring J., 1994, American Statistician, 48, 209, 10.1080/00031305.1994.10476059 · DOI
- 10.1037//0033-2909.114.3.542 · DOI · Paper page
- Darlington R. B., 1968, Psychological Bulletin, 69, 161, 10.1037/h0025471 · DOI · Paper page
- Darlington, R. B. (1990). Regression and linear models. New York: McGraw-Hill.
- Gibson W. A., 1962, Psychological Reports, 11, 32, 10.2466/pr0.1962.11.1.32 · DOI
- Goldberger, A. S. (1964). Econometric theory. New York: John Wiley & Sons, Inc.
- Green P. E., 1978, Journal of Marketing Research, 15, 356, 10.2307/3150583 · DOI
- Green P. E., 1980, Journal of Marketing Research, 17, 116, 10.2307/3151126 · DOI
- Green, P. E. & Tull, D. S. (1975). Research for marketing decisions (3rd ed.). Englewood Cliffs, NJ: Prentice-Hall, Inc.
- Grisaffe, D. (1993, February). Appropriate use of regression in customer satisfaction analyses: A response to William McLauchlan. Quirk's Marketing Research Review, pp. 11-17.
- Healy M. J. R., 1990, Statistics in Medicine, 9, 633, 10.1002/sim.4780090609 · DOI
- Heeler R. M., 1979, Journal of Marketing Research, 16, 60, 10.2307/3150875 · DOI
- Hobson C. J., 1983, Academy of Management Review, 8, 640, 10.2307/258265 · DOI
- Hobson C. J., 1981, Organizational Behavior and Human Performance, 28, 164, 10.1016/0030-5073(81)90021-0 · DOI
- Hoffman P. J., 1960, Psychological Bulletin, 57, 116, 10.1037/h0047807 · DOI · Paper page
- Hoffman P. J., 1962, Psychological Bulletin, 59, 77, 10.1037/h0044373 · DOI
- 10.1086/208530 · DOI
- Jackson B. B., 1980, Journal of Marketing Research, 17, 113, 10.2307/3151125 · DOI
- Johnson R. M., 1966, Psychometrika, 31, 61, 10.1007/BF02289457 · DOI
- Kruskal W., 1987, American Statistician, 41, 6, 10.1080/00031305.1987.10475432 · DOI
- Kruskal W., 1989, American Statistician, 43, 2, 10.1080/00031305.1989.10475596 · DOI
- Neter, J., Wasserman, W. & Kutner, M. H. (1985). Applied linear statistical models (2nd ed.). Homewood, IL: Irwin.
- Pratt, J. W. (1987). Dividing the indivisible: Using simple symmetry to partition variance explained. In T. Pukilla & S. Duntaneu (Eds.), Proceedings of Second Tampere Conference in Statistics (pp. 245-260). University of Tampere, Finland.
- SAS Institute (1990). SAS/STAT user's guide, Version 6 (4th ed., Vol. 2). Cary, NC: SAS Institute, Inc.
- 10.1016/0167-7152(87)90082-4 · DOI
- Williams, E. J. (1978). Linear hypotheses: Regression. In W. H. Kruskal & J. M. Tanur (Eds.), International encyclopedia of statistics (pp. 523-541). New York: Free Press.
- Zedeck S., 1977, Organizational Behavior and Human Performance, 18, 269, 10.1016/0030-5073(77)90031-9 · DOI
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Metadata
- Authors
- Johnson, J. W.
- Venue
- Multivariate Behavioral Research
- Publisher
- Informa UK Limited
- Published
- 2000-01
- Volume
- 35
- Issue
- 1
- Pages
- 1-19
- ISSN
- 0027-3171 (ISSN-L)
- 1532-7906
- OpenAlex
- W2115218557
- Cited by
- 1,135
- References
- 0
- FWCI
- 5.25
- Type
- journal article
- Language
- en
- Open access
- closed
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