The Open UniversitySkip to content
 

Clustered Common Factor Exploration in Factor Analysis

Uno, Kohei; Adachi, Kohei and Trendafilov, Nickolay T. (2019). Clustered Common Factor Exploration in Factor Analysis. Psychometrika (Early Access).

DOI (Digital Object Identifier) Link: https://doi.org/10.1007/s11336-019-09666-5
Google Scholar: Look up in Google Scholar

Abstract

The factor analysis (FA) model does not permit unique estimation of the common and unique factor scores. This weakness is notorious as the factor indeterminacy in FA. Luckily, some part of the factor scores can be uniquely determined. Thus, as a whole, they can be viewed as a sum of determined and undetermined parts. The paper proposes to select the undetermined part, such that the resulting common factor scores have the following feature: the rows (i.e., individuals) of the common factor score matrix are as well classified as possible into few clusters. The clear benefit is that we can easily interpret the factor scores simply by focusing on the clusters. The procedure is called clustered common factor exploration (CCFE). An alternating least squares algorithm is developed for CCFE. It is illustrated with real data examples. The proposed approach can be viewed as a parallel to the rotation techniques in FA. They exploit another FA indeterminacy, the rotation indeterminacy, which is resolved by choosing the rotation that transforms the loading matrix into the 'most' interpretable one according to a pre-specified criterion. In contrast to the rotational indeterminacy, the factor indeterminacy is utilized to achieve well-clustered factor scores by CCFE. To the best of our knowledge, such an approach to the FA interpretation has not been studied yet.

Item Type: Journal Item
Copyright Holders: 2019 The Psychometric Society
ISSN: 1860-0980
Keywords: exploratory factor analysis; factor indeterminacy; clustered common factor scores; factor identification; matrix decomposition solution
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 60053
SWORD Depositor: Jisc Publications-Router
Depositing User: Jisc Publications-Router
Date Deposited: 26 Mar 2019 09:44
Last Modified: 29 Mar 2019 12:45
URI: http://oro.open.ac.uk/id/eprint/60053
Share this page:

Metrics

Altmetrics from Altmetric

Citations from Dimensions

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU