Zig-zag exploratory factor analysis with more variables than observations

Unkel, Steffen and Trendafilov, Nickolay T. (2013). Zig-zag exploratory factor analysis with more variables than observations. Computational Statistics, 28(1) pp. 107–125.

DOI: https://doi.org/10.1007/s00180-011-0275-z

Abstract

In this paper, the problem of fitting the exploratory factor analysis (EFA) model to data matrices with more variables than observations is reconsidered. A new algorithm named ‘zig-zag EFA’ is introduced for the simultaneous least squares estimation of all EFA model unknowns. As in principal component analysis, zig-zag EFA is based on the singular value decomposition of data matrices. Another advantage of the proposed computational routine is that it facilitates the estimation of both common and unique factor scores. Applications to both real and artificial data illustrate
the algorithm and the EFA solutions.

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About

• Item ORO ID
• 35886
• Item Type
• Journal Item
• ISSN
• 1613-9658
• Keywords
• factor analysis; horizontal data matrices; procrustes problems; singular value decomposition; gene expression data; Thurstone’s box data
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM)
  Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
• Copyright Holders
• © 2011 Springer-Verlag
• Depositing User
• Nickolay Trendafilov