Abstract
It is shown that the scoring algorithm for maximum likelihood estimation in exploratory factor analysis can be developed in a way that is many times more efficient than a direct development based on information matrices and score vectors. The algorithm offers a simple alternative to current algorithms and when used in one-step mode provides the simplest and fastest method presently available for moving from consistent to efficient estimates. Perhaps of greater importance is its potential for extension to the confirmatory model. The algorithm is developed as a Gauss-Newton algorithm to facilitate its application to generalized least squares and to maximum likelihood estimation.
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This research was supported by NSF Grant MCS-8301587.
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Jennrich, R.I. A gauss-newton algorithm for exploratory factor analysis. Psychometrika 51, 277–284 (1986). https://doi.org/10.1007/BF02293985
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DOI: https://doi.org/10.1007/BF02293985