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2 results

  • On Bayesian Estimation in Unrestricted Factor Analysis

  • Raymond F. Koopman
  • Journal:
    Psychometrika / Volume 43 / Issue 1 / March 1978
    Published online by Cambridge University Press:
    01 January 2025, pp. 109-110

  • It is shown that the common and unique variance estimates produced by Martin & McDonald’s Bayesian estimation procedure for the unrestricted common factor model have a predictable sum which is always greater than the maximum likelihood estimate of the total variance. This fact is used to justify a suggested simple alternative method of specifying the Bayesian parameters required by the procedure.

  • Determining the Number of Components from the Matrix of Partial Correlations

  • Wayne F. Velicer
  • Journal:
    Psychometrika / Volume 41 / Issue 3 / September 1976
    Published online by Cambridge University Press:
    01 January 2025, pp. 321-327

  • A common problem for both principal component analysis and image component analysis is determining how many components to retain. A number of solutions have been proposed, none of which is totally satisfactory. An alternative solution which employs a matrix of partial correlations is considered. No components are extracted after the average squared partial correlation reaches a minimum. This approach gives an exact stopping point, has a direct operational interpretation, and can be applied to any type of component analysis. The method is most appropriate when component analysis is employed as an alternative to, or a first-stage solution for, factor analysis.