Psychometrika

, Volume 53, Issue 3, pp 417–423 | Cite as

The adjusted rand statistic: A SAS macro

  • Dennis G. Fisher
  • Paul Hoffman
Computational Psychometrics

Abstract

A macro for calculating the Hubert and Arabie (1985) adjusted Rand statistic is presented. The adjusted Rand statistic gives a measure of classification agreement between two partitions of the same set of objects. The macro is written in the SAS macro language and makes extensive use of SAS/IML software (SAS Institute, 1985a, 1985b). The macro uses two different methods of handling missing values. The default method assumes that each object that has a missing value for the classification category is in its own separate category or cluster for that classification. The optional method places all objects with a missing value for the classification category into the same category for that classification.

Keywords

Public Policy Statistical Theory Optional Method Separate Category Classification Category 

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References

  1. Berzins, J. I., Ross, W. F., English, G. E., & Haley, J. V. (1974). Subgroups among opiate addicts: A typological investigation.Journal of Abnormal Psychology, 83(1), 65–73.Google Scholar
  2. Cohen, J. (1960). A coefficient of agreement for nominal scales.Educational and Psychological Measurement, 20(1), 37–46.Google Scholar
  3. Edelbrock, C. (1979). Mixture model tests of hierarchical clustering algorithms: The problem of classifying everybody.Multivariate Behavioral Research, 14, 367–384.Google Scholar
  4. Filstead, W. J., Drachman, D. A., Rossi, J. J., & Getsinger, S. H. (1983). The relationship of MMPI subtype membership to demographic variables and treatment outcome among substance misusers.Journal of Studies on Alcohol, 44(5), 917–922.Google Scholar
  5. Fisher, D. G., Anglin, M. D., Weisman, C. P., & Pulliam, L. (1988).Replication problems of substance abuser MMPIcluster types. Unpublished manuscript.Google Scholar
  6. Hubert, L., & Arabie, P. (1985). Comparing partitions.Journal of Classification, 2, 193–218.Google Scholar
  7. Lorr, M., & Radhakrishnan, B. K. (1967). A comparison of two methods of cluster analysis.Educational and Psychological Measurement, 27, 47–53.Google Scholar
  8. Milligan, G. W., & Cooper, M. C. (1986). A study of the comparability of external criteria for hierarchical cluster analysis.Multivariate Behavioral Research, 21, 441–458.Google Scholar
  9. Morey, L. C., & Agresti, A. (1984). The measurement of classification agreement: An adjustment to the Rand statistic for chance agreement.Educational and Psychological Measurement, 44, 33–37.Google Scholar
  10. Ramsay, J. O. (1986). A PROC MATRIX program for preference-dissimilarity multidimensional scaling.Psychometrika, 51, 163–170.Google Scholar
  11. Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods.Journal of the American Statistical Association, 66(336), 846–850.Google Scholar
  12. SAS Institute. (1979).SAS user's guide: 1979 edition. Cary, NC: Author.Google Scholar
  13. SAS Institute. (1985a).SAS/IML user's guide: Version 5 edition. Cary, NC: Author.Google Scholar
  14. SAS Institute. (1985b).SAS user's guide: Basics, Version 5 edition. Cary, NC: Author.Google Scholar
  15. Wishart, D. (1982).CLUST'AN user manual (Third edition). Edinburgh: Program Library Unit, Edinburgh University.Google Scholar

Copyright information

© The Psychometric Society 1988

Authors and Affiliations

  • Dennis G. Fisher
    • 1
  • Paul Hoffman
    • 2
  1. 1.Center for Alcohol and Addiction StudiesUniversity of Alaska AnchorageAnchorage
  2. 2.Office of Academic ComputingUniversity of CaliforniaLos Angeles

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