, Volume 31, Issue 4, pp 437–445 | Cite as

The computer revolution in psychometrics

  • Bert F. GreenJr.


We have said that psychometric methods involving algorithms are completely objective—at least they are if the algorithm is in the form of a program for a digital computer. These objective procedures need Monte Carlo and other computer runs to determine their properties, but so do many equation-oriented techniques. The objective algorithms are flexible but not flaccid. They offer a way of dealing with complexities that formerly seemed beyond our grasp. As the computer revolution continues in psychometrics, we can expect objective algorithmic methods to become the rule rather than the exception.


Public Policy Statistical Theory Digital Computer Algorithmic Method Psychometric Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Anderson, H. E., Jr. and Fruchter, B. Some multiple correlation and predictor selection methods.Psychometrika, 1960,25, 59–76.Google Scholar
  2. [2]
    Cattell, R. B. Some deeper significance of the computer for the practicing psychologist.Personnel and Guid. J., 1965,43, 160–166.Google Scholar
  3. [3]
    Cattell, R. B. and Muerle, J. L. The “Maxplane” program for factor rotation to oblique simple structure.Educ. psychol. Measmt., 1960,20, 569–590.Google Scholar
  4. [4]
    Charnes, A. and Cooper, W. W.Management Models and Industrial Applications of Linear Programming. Vol. I. New York: John Wiley & Sons, Inc., 1961.Google Scholar
  5. [5]
    Dwyer, P. S. Solution to the personnel classification problem with the method of optimal regions.Psychometrika, 1954,19, 11–26.Google Scholar
  6. [6]
    Eber, H. W. Toward oblique simple structure: Maxplane.Multivariate Behav. Res., 1966,1, 112–125.Google Scholar
  7. [7]
    Green, B. F., Jr. Computer models for cognitive processes.Psychometrika, 1961,26, 85–92.Google Scholar
  8. [8]
    Guttman, L. A basis for scaling qualitative data.Amer. Sociol. Rev., 1944,9, 139–150.Google Scholar
  9. [9]
    Guttman, L. A general nonmetric technique for finding the smallest Euclidean space for a configuration of points.Psychometrika, (forthcoming).Google Scholar
  10. [10]
    Harman, H. H.Modern Factor Analysis. Chicago: Univ. of Chicago Press, 1960.Google Scholar
  11. [11]
    Jones, L. V. Beyond Babbage.Psychometrika, 1963,28, 315–332.Google Scholar
  12. [12]
    Kaiser, H. F. The application of electronic computers to factor analysis.Educ. psychol. Measmt., 1960, 141–151.Google Scholar
  13. [13]
    Kaiser, H. F. The varimax criterion for analytic rotation in factor analysis.Psychometrika, 1958,23, 187–200.Google Scholar
  14. [14]
    Kaiser, H. F. and Caffrey, J. Alpha factor analysis.Psychometrika, 1965,30, 1–14.Google Scholar
  15. [15]
    Kruskal, J. B. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis.Psychometrika, 1964,29, 1–27.Google Scholar
  16. [16]
    Lawley, D. N. The estimation of factor loadings by the method of maximum likelihood.Proc. Roy. Soc. Edin., 1940,A40, 64–82.Google Scholar
  17. [17]
    Lingoes, J. C. An IBM-7090 program for Guttman-Lingoes smallest space analysis—I.Behavioral Science, 1965,10, 183–184.Google Scholar
  18. [18]
    Loevinger, J., Gleser, C., and DuBois, P. H. Maximizing the discriminating power of a multiple-score test.Psychometrika, 1953,18, 309–317.Google Scholar
  19. [19]
    McQuitty, L. L. Rank order typal analysis.Educ. psychol. Measmt., 1963,23, 55–61.Google Scholar
  20. [20]
    Rao, C. R. Estimation and tests of significance in factor analysis.Psychometrika, 1955,20, 93–111.Google Scholar
  21. [21]
    Shepard, R. N. The analysis of proximities: multidimensional scaling with an unknown distance function.Psychometrika, 1962,27, I, 125–140, II, 219–246.Google Scholar
  22. [22]
    Shepard, R. N. and Carroll, J. D. Parametric representation of nonlinear data structures. To appear in P. R. Krishnaiah (Ed.)Proc. International Symposium on Multivariate Analysis. New York: Academic Press (forthcoming).Google Scholar
  23. [23]
    Spang, H. A., III. A review of minimization techniques for nonlinear functions.SIAM Rev., 1962,4, 343–365.Google Scholar
  24. [24]
    Suchman, E. A. The scalogram board technique for scale analysis. In S. A. Stouffer,et al. (Eds.)Measurement and Prediction. Princeton, N. J.: Princeton Univ. Press, 1950.Google Scholar
  25. [25]
    Thurstone, L. L. An analytic method for simple structure.Psychometrika, 1954,19, 173–182.Google Scholar
  26. [26]
    Torgerson, W. S.Theory and Methods of Scaling. New York: John Wiley & Sons, Inc., 1958.Google Scholar
  27. [27]
    Torgerson, W. S. Multidimensional scaling of similarity.Psychometrika, 1965,30, 379–393.Google Scholar
  28. [28]
    Tryon, R. C. Cumulative communality cluster analysis.Educ. psychol. Measmt., 1958,18, 3–36.Google Scholar
  29. [29]
    Tucker, L. R. The objective definition of simple structure in linear factor analysis.Psychometrika, 1955,20, 209–225.Google Scholar
  30. [30]
    Webster, H. Maximizing test validity by item selection.Psychometrika, 1956,21, 153–164.Google Scholar

Copyright information

© Psychometric Society 1966

Authors and Affiliations

  • Bert F. GreenJr.
    • 1
  1. 1.Carnegie Institute of TechnologyUSA

Personalised recommendations