Posterior analysis of the factor model
作者:
D. J. Bartholomew,
期刊:
British Journal of Mathematical and Statistical Psychology
(WILEY Available online 1981)
卷期:
Volume 34,
issue 1
页码: 93-99
ISSN:0007-1102
年代: 1981
DOI:10.1111/j.2044-8317.1981.tb00620.x
出版商: Blackwell Publishing Ltd
数据来源: WILEY
摘要:
The term posterior analysis is used in this paper to refer to methods of drawing inferences about the latent variables in factor analysis after the model has been fitted. In particular with the problem of locating each individual in the latent space on the basis of the values of the observed variables. This problem has been traditionally treated by determining factor scores. It is argued here that, if all variables in the model are random, then Bayes' theorem provides the logical link between the data and the unobserved latent variables. Viewed in this perspective the indeterminacy of factor scores is simply an expression of the fact that the latent variables are still random variables after the manifest variables have been observed. The name, factor scores, can then reasonably be given to the location parameters of the posterior distributions. The paper is primarily expository and it contains no new mathematics. Its concern is with the logical framework within which the analysis should be carried out and interpreted.
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