Multivariate alternatives to regression analysis in the evaluation of salary equity-parity
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Abstract
The analysis of salary equity-parity in institutions of higher education typically involves the use of multiple regression analysis to determine predicted salary and the residual differences between predicted and actual salary. Multiple regression analysis forces the variable weights throughout the salary structure to be uniform, permits only one criterion or dependent variable to be examined at a time, restricts the coordinates to those provided by the variables as measured, and as customarily used, treats qualitative or discrete variables as if they were continuous, assuming ordinality of the numbers used. Two multivariate alternatives to regression analysis are presented, canonical analysis and multiple discriminant analysis, both of which define new coordinate systems for evaluation of dimensions underlying salary decisions.
Keywords
Regression Analysis Coordinate System High Education Multiple Regression Analysis Discriminant AnalysisPreview
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References
- Anderson, T. W.An Introduction to Multivariate Statistical Analysis. New York: John Wiley, 1958.Google Scholar
- Beaumont, M. S. Efficiency and equity: the single salary schedule in public higher education.AAUP Bulletin, March 1978, pp. 19–25.Google Scholar
- Brascamp, L. A., and Johnson, D. R. The use of a parity-equity model to evaluate faculty salary policies.Research in Higher Education 1978,8 57–66.Google Scholar
- Buford, J. A., Jr., and Norris, D. R. A salary equalization model: identifying and correcting sex-based salary differences.Employee Relations Law Journal 1980–81,6(3), 406–421.Google Scholar
- Danielson, J. L., and Smith, R. The application of regression analysis to equity and merit in personnel decisions.Public Personnel Management Record 1981,10(1), 126–131.Google Scholar
- Das, R. S. An application of factor and canonical analysis to multivariate data.British Journal of Mathematical and Statistical Psychology 1965,18 57–67.Google Scholar
- Dixon, W. J., and Brown, M. B. (Eds.).BMDP-79 Biomedical computer programs P-Series. Berkeley: University of California Press, 1979.Google Scholar
- Dixon, W. J., Brown, M. B., Engleman, L., Frane, J. W., Hill, M. A., Jennrich, R. I., and Toporek, J. D.BMDP Statistical Software 1981. Berkeley: University of California Press, 1981.Google Scholar
- Fisher, R. A. The use of multiple measurements in taxonomic problems.Annals of Eugenics 1936,7 179–188.Google Scholar
- Fisher, R. A. The statistical utilization of multiple measurements.Annals of Eugenics 1938,8 376–386.Google Scholar
- Hotelling, H. Relations between two sets of variates.Biometrika 1936,28 321–377.Google Scholar
- Mahalanobis, P. C. On tests and measures of group divergence.Journal and Proceedings of the Asiatic Society of Bengal 1930,26 541–548.Google Scholar
- Mahalanobis, P. C. On the generalized distance in statistics.Proceedings of the National Institute of Sciences of India 1936,2 49–55.Google Scholar
- Mahalanobis, P. C., Majumdar, D. N., and Rao, C. R. Anthropometric survey of the United Provinces, 1941.Sankhya 1949,9 90–324.Google Scholar
- Rao, C. R. Tests with discriminant functions in multivariate analysis.Sankhya 1946,7 407–414.Google Scholar
- Rao, C. R.Advanced Statistical Methods in Biometric Research. New York: John Wiley, 1952.Google Scholar
- Rao, C. R.Linear Statistical Inference and Its Applications. New York: John Wiley, 1965.Google Scholar
- Risher, H., and Cameron, M. Pay decisions: testing for discrimination.Employee Relations Law Journal 1981–82,7(3), 432–453.Google Scholar
- Tanur, J. M., and Coser, R. L. Pockets of “poverty” in the salaries of academic women.AAUP Bulletin, March 1978, pp. 26–30.Google Scholar