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The relation between information and variance analyses

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Abstract

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.

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References

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Authors and Affiliations

  1. The Johns Hopkins University, USA

    W. R. Garner

  2. Massachusetts Institute of Technology, USA

    William J. McGill

Authors
  1. W. R. Garner
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  2. William J. McGill
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Additional information

The work of the senior author was supported by Contract N5ori-166, Task Order 1, between the U.S. Office of Naval Research and The Johns Hopkins University. This is Report No. 166-I-192, Project Designation No. NR 145-089, under that contract.

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Garner, W.R., McGill, W.J. The relation between information and variance analyses. Psychometrika 21, 219–228 (1956). https://doi.org/10.1007/BF02289132

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  • DOI: https://doi.org/10.1007/BF02289132

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