Approximated Penalized Maximum Likelihood for Exploratory Factor Analysis: An Orthogonal Case

Shaobo Jin (), Irini Moustaki () and Fan Yang-Wallentin ()
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Shaobo Jin: Uppsala University
Irini Moustaki: London School of Economics and Political Science
Fan Yang-Wallentin: Uppsala University

Psychometrika, 2018, vol. 83, issue 3, No 6, 628-649

Abstract: 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.

Keywords: factor rotation; LASSO; SCAD; MCP; sparsity; shrinkage (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11336-018-9623-z

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