Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case

Jin, Shaobo, Moustaki, Irini ORCID: 0000-0001-8371-1251 and Yang-Wallentin, Fan (2018) Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case. Psychometrika, 83 (3). pp. 628-649. ISSN 0033-3123

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Abstract

The problem of penalized maximum likelihood (PML) for an exploratory factor analysis (EFA) model is studied in this paper. An EFA model is typically estimated using maximum likelihood and then the estimated loading matrix is rotated to obtain a sparse representation. Penalized maximum likelihood simultaneously fits the EFA model and produces a sparse loading matrix. To overcome some of the computational drawbacks of PML, an approximation to PML is proposed in this paper. It is further applied to an empirical dataset for illustration. A simulation study shows that the approximation naturally produces a sparse loading matrix and more accurately estimates the factor loadings and the covariance matrix, in the sense of having a lower mean squared error than factor rotations, under various conditions.

Item Type:	Article
Official URL:	https://link.springer.com/journal/11336
Additional Information:	© Springer International Publishing AG
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	30 May 2018 12:00
Last Modified:	11 Dec 2024 21:39
URI:	http://eprints.lse.ac.uk/id/eprint/88118

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