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

• Okamoto Masashi

  School of Economics, Otemon University

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

Keywords

• AIC
• Guttman-Kaiser method
• Least-squares factor analysis
• Likelihood ratio method
• Number-of-factors problem
• One-step estimator
• Partial Gauss-Newton algorithm
• Random loading model

Details

• CRID: 1571135651907760256
• NII Article ID: 110001235542
• NII Book ID: AA10823693
• ISSN: 09152350
• Text Lang: en
• Data Source:
  • CiNii Articles