The relation between information and variance analyses
Analysis of variance and uncertainty analysis are analogous techniques for partitioning variability. In both analyses negative interaction terms due to negative covariance terms that appear when non-orthogonal predictor variables are allowed may occur. Uncertainties can be estimated directly from variances if the form of distribution is assumed. The decision as to which of the techniques to use depends partly on the properties of the criterion variable. Only uncertainty analysis may be used with a non-metric criterion. Since uncertainties are dimensionless (using no metric), however, uncertainty analysis has a generality which may make it useful even when variances can be computed.
KeywordsCovariance Public Policy Predictor Variable Variance Analysis Statistical Theory
Unable to display preview. Download preview PDF.
- 1.Garner, W. R. and Hake, H. W. The amount of information in absolute judgments.Psychol. Rev., 1951,58, 446–459.Google Scholar
- 2.Garner, W. R. and McGill, W. J. Relation between uncertainty, variance, and correlation analyses. Mimeographed report, Department of Psychology, The Johns Hopkins University, Baltimore, Maryland.Google Scholar
- 3.McGill, W. J. Multivariate information transmission.Psychometrika, 1954,19, 97–116.Google Scholar
- 4.Miller, G. A. and Madow, W. G. On the maximum likelihood estimate of the Shannon-Wiener measure of information. Report AFCRC-TR-54-75. Operational Applications Laboratory, ARDC, August 1954.Google Scholar
- 5.Shannon, C. E. A mathematical theory of communication.Bell syst. tech. J., 1948,27, 379–423, 623–656.Google Scholar
- 6.Stevens, S. S. Mathematics, measurement, and psychophysics. In S. S. Stevens (Ed.), Handbook of experimental psychology. New York: Wiley, 1951, pp. 1–49.Google Scholar