Open AccessEFFICIENT ESTIMATION IN IMAGE FACTOR ANALYSIS 1
Author(s) -
Jöreskog K. G.
Publication year - 1967
Publication title -
ets research bulletin series
Language(s) - English
Resource type - Journals
eISSN - 2333-8504
pISSN - 0424-6144
DOI - 10.1002/j.2333-8504.1967.tb00550.x
Subject(s) - guttman scale , invariant (physics) , image (mathematics) , factor (programming language) , maximum likelihood , mathematics , artificial intelligence , computer science , algorithm , pattern recognition (psychology) , statistics , mathematical physics , programming language
The image factor analytic model (IFA), as related to Guttman's image theory, is considered as an alternative to the traditional factor analytic model (TFA). One advantage with IFA, as compared with TFA, is that more factors can be extracted without yielding a perfect fit to the observed data. Several theorems concerning the structural properties of IFA are proved and an iterative procedure for finding the maximum likelihood estimates of the parameters of the IFA‐model is given. Substantial experience with this method verifies that Heywood cases never occur. Results of an artificial experiment suggest that IFA may be more factorially invariant than TFA under selection of tests from a large battery.