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Approximated penalized maximum likelihood for exploratory factor analysis: An orthogonal case
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
London School of Economics and Political Science.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2018 (English)In: Psychometrika, ISSN 0033-3123, E-ISSN 1860-0980, Vol. 83, no 3, p. 628-649Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2018. Vol. 83, no 3, p. 628-649
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-360038DOI: 10.1007/s11336-018-9623-zISI: 000452377900006OAI: oai:DiVA.org:uu-360038DiVA, id: diva2:1246687
Funder
Swedish Research Council, 2017-01175Available from: 2018-09-10 Created: 2018-09-10 Last updated: 2019-01-21Bibliographically approved

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Jin, ShaoboYang-Wallentin, Fan

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