, Volume 58, Issue 2, pp 211–232 | Cite as

A simple Gauss-Newton procedure for covariance structure analysis with high-level computer languages

  • Robert Cudeck
  • Kelli J. Klebe
  • Susan J. Henly


An implementation of the Gauss-Newton algorithm for the analysis of covariance structures that is specifically adapted for high-level computer languages is reviewed. With this procedure one need only describe the structural form of the population covariance matrix, and provide a sample covariance matrix and initial values for the parameters. The gradient and approximate Hessian, which vary from model to model, are computed numerically. Using this approach, the entire method can be operationalized in a comparatively small program. A large class of models can be estimated, including many that utilize functional relationships among the parameters that are not possible in most available computer programs. Some examples are provided to illustrate how the algorithm can be used.

Key words

covariance structures Gauss-Newton method simplex models second order factor analysis dichotomized variables 


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Copyright information

© The Psychometric Society 1993

Authors and Affiliations

  • Robert Cudeck
    • 1
  • Kelli J. Klebe
    • 2
  • Susan J. Henly
    • 3
  1. 1.Department of PsychologyUniversity of MinnesotaMinneapolis
  2. 2.University of ColoradoColorado Springs
  3. 3.College of NursingUniversity of North DakotaUSA

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