Estimation for the Multiple Factor Model when Data are Missing

Carl T. Finkbeiner

153 citations0/0 files storedPsychometrika

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Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Statistical and numerical algorithms

Abstract

A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with 5 heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation over the heuristic methods but is considerably more costly computationally.

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References

2007Multiple Factor Analysis 1972Factor Analysis as a Statistical Method.1956Statistical Inference in Factor Analysis 1965A Biometrics Invited Paper. Factor Analysis: An Introduction to Essentials I. The Purpose and Underlying Models 1974Analyzing psychological data by structural analysis of covariance matrices 1969Evaluation of Factor Analytic Research Procedures by means of Simulated Correlation Matrices 1968A Comparison of Factor Analytic Techniques 1971FACTOR ANALYSIS BY GENERALIZED LEAST SQUARES

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