Efficient Estimation in Image Factor Analysis
- 1 March 1969
- journal article
- Published by Cambridge University Press (CUP) in Psychometrika
- Vol. 34 (1) , 51-75
- https://doi.org/10.1007/bf02290173
Abstract
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.Keywords
This publication has 15 references indexed in Scilit:
- Testing a Simple Structure Hypothesis in Factor AnalysisPsychometrika, 1966
- A Rapidly Convergent Descent Method for MinimizationThe Computer Journal, 1963
- On the Statistical Treatment of Residuals in Factor AnalysisPsychometrika, 1962
- Some Rao-Guttman RelationshipsPsychometrika, 1962
- “Best Possible” Systematic Estimates of CommunalitiesPsychometrika, 1956
- THE DETERMINACY OF FACTOR SCORE MATRICES WITH IMPLICATIONS FOR FIVE OTHER BASIC PROBLEMS OF COMMON‐FACTOR THEORY1British Journal of Statistical Psychology, 1955
- Some necessary conditions for common-factor analysisPsychometrika, 1954
- Image Theory for the Structure of Quantitative VariatesPsychometrika, 1953
- The Orthogonal Approximation of an Oblique Structure in Factor AnalysisPsychometrika, 1952
- VI.—The Estimation of Factor Loadings by the Method of Maximum LikelihoodProceedings of the Royal Society of Edinburgh, 1940