Convergence of estimates of unique variances in factor analysis, based on the inverse sample covariance matrix

9Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

If the ratio m/p tends to zero, where m is the number of factors m and p the number of observable variables, then the inverse diagonal element of the inverted observable covariance matrix (σ pjj) -1 tends to the corresponding unique variance ψ jj for almost all of these (Guttman, 1956). If the smallest singular value of the loadings matrix from Common Factor Analysis tends to infinity as p increases, then m/p tends to zero. The same condition is necessary and sufficient for (σ pjj) -1 to tend to ψ jj for all of these. Several related conditions are discussed. © 2006 The Psychometric Society.

Cite

CITATION STYLE

APA

Krijnen, W. P. (2006). Convergence of estimates of unique variances in factor analysis, based on the inverse sample covariance matrix. Psychometrika, 71(1), 193–199. https://doi.org/10.1007/s11336-000-1142-9

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free