THE NUMBER-OF-FACTORS PROBLEM IN LEAST-SQUARES FACTOR ANALYSIS WITH A RANDOM LOADING MODEL

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Author(s)

Abstract

A random loading model is presented as a numerical model in a Monte Carlo experiment of factor analysis. Three methods for the number-of-factors problem, Guttman-Kaiser, likelihood ratio and AIC, and two least-squares estimators, one-step and final, were compared experimentally within each category. On the whole, AIC method using the final estimator fared best, whereas the one-step estimator behaved better than the final estimator in estimating the unique variances.

Journal

  • Journal of the Japanese Society of Computational Statistics

    Journal of the Japanese Society of Computational Statistics 1(1), 1-15, 1988-12

    Japanese Society of Computational Statistics

Codes

  • NII Article ID (NAID)
    110001235542
  • NII NACSIS-CAT ID (NCID)
    AA10823693
  • Text Lang
    ENG
  • ISSN
    09152350
  • Data Source
    NII-ELS 
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