Psychometrika

, Volume 28, Issue 1, pp 69–80 | Cite as

A factor analytic method for investigating differences between groups of individual learning curves

  • R. A. Weitzman
Article

Abstract

In this method of analyzing learning data, entire learning curves are described quantitatively by single numbers which are used in a statistical test to determine whether two or more groups of learning curves are significantly different. The method has some logical advantages over prevailing methods in that it avoids the use of average learning curves and of arbitrary measures of slope and asymptote. Its disadvantage is computational. Since it involves the use of factor analytic procedures, it may be tedious to apply unless computation is carried out on a high-speed computer.

Keywords

Public Policy Analytic Procedure Learning Curve Statistical Theory Individual Learning 
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.

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References

  1. [1]
    Bush, R. R. and Wilson, T. R. Two-choice behavior of the paradise fish.J. exp. Psychol., 1956,51, 315–322.Google Scholar
  2. [2]
    Day, B. B. and Sandomire, M. M. Use of the discriminant function for more than two groups.J. Amer. statist. Ass., 1942,37, 461–472.Google Scholar
  3. [3]
    Eckart, C. and Young, G. The approximation of one matrix by another of lower rank.Psychometrika, 1936,1, 211–218.Google Scholar
  4. [4]
    Fisher, R. A. The use of multiple measurements in taxonomic problems.Ann. Eugenics, 1936,7, 179–188.Google Scholar
  5. [5]
    Gardner, R. A. Probability learning in two and three choice situations.Dissertation Abstr., 1954,14, 1820–1821.Google Scholar
  6. [6]
    Gulliksen, H. Mathematical solutions for psychological problems. Princeton: Princeton Univ. and Educ. Test. Serv., 1958.Google Scholar
  7. [7]
    Hilgard, E. R. A summary and evaluation of alternative procedures for the construction of Vincent curves.Psychol. Bull., 1938,35, 282–297.Google Scholar
  8. [8]
    Hoel, P. G.Introduction to mathematical statistics. New York: Wiley, 1947.Google Scholar
  9. [9]
    Householder, A. S. and Young, G. Matrix approximation and latent roots.Amer. math. Monthly, 1938,45, 165–171.Google Scholar
  10. [10]
    Kendall, M. G.A course in multivariate analysis. New York: Hafner, 1957.Google Scholar
  11. [11]
    Merrell, M. The relationship of individual growth to average growth.Hum. Biology, 1931,3, 37–40.Google Scholar
  12. [12]
    Rao, C. R. Some statistical methods for comparison of growth curves.Biometrics, 1958,14, 1–17.Google Scholar
  13. [13]
    Siegel, S.Nonparametric statistics for the behavioral sciences. New York: McGraw-Hill, 1956.Google Scholar
  14. [14]
    Thurstone, L. L.Multiple-factor analysis. Chicago: Univ. Chicago Press, 1947.Google Scholar
  15. [15]
    Tucker, L. R. Determination of parameters of a functional relation by factor analysis.Psychometrika, 1958,23, 19–23.Google Scholar
  16. [16]
    Tucker, L. R. Determination of generalized learning curves by factor analysis. Princeton: Princeton Univ. and Educ. Test. Serv., 1960.Google Scholar
  17. [17]
    Weitzman, R. A. A comparison of the performance of rats and fish on a probabilistic, discriminative learning problem. Princeton: Princeton Univ. and Educ. Test. Serv., 1959.Google Scholar

Copyright information

© Psychometric Society 1963

Authors and Affiliations

  • R. A. Weitzman
    • 1
  1. 1.Bar-Ilan UniversityIsrael

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