Unkel, Steffen and Trendafilov, Nickolay T.
(2013).
|
(Version of Record)
Due to publisher licensing restrictions, this file is not available for public download Click here to request a copy from the OU Author. |
| DOI (Digital Object Identifier) Link: | https://doi.org/10.1007/s00180-011-0275-z |
|---|---|
| Google Scholar: | Look up in Google Scholar |
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.
| Item Type: | Journal Item |
|---|---|
| Copyright Holders: | 2011 Springer-Verlag |
| ISSN: | 1613-9658 |
| Keywords: | factor analysis; horizontal data matrices; procrustes problems; singular value decomposition; gene expression data; Thurstone’s box data |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics |
| Item ID: | 35886 |
| Depositing User: | Nickolay Trendafilov |
| Date Deposited: | 19 Dec 2012 16:52 |
| Last Modified: | 07 Dec 2018 13:25 |
| URI: | http://oro.open.ac.uk/id/eprint/35886 |
| Share this page: |     |
Metrics
Altmetrics from Altmetric | Citations from Dimensions |
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)