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Clustered Common Factor Exploration in Factor Analysis.

Kohei Uno1, Kohei Adachi2, Nickolay T Trendafilov3

  • 1Graduate School of Human Sciences, Osaka University, 1-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. kohei.uno.stat@gmail.com.

Psychometrika
|March 9, 2019
PubMed
Summary
This summary is machine-generated.

Factor analysis (FA) has score estimation issues. Clustered common factor exploration (CCFE) addresses this by selecting undetermined factor score parts to form interpretable clusters, enhancing FA analysis.

Keywords:
clustered common factor scoresexploratory factor analysisfactor identificationfactor indeterminacymatrix decomposition solution

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Factor analysis (FA) models suffer from factor indeterminacy, preventing unique estimation of common and unique factor scores.
  • While factor scores cannot be fully determined, a portion can be uniquely estimated, allowing for interpretation.
  • Existing FA interpretation methods often focus on rotation indeterminacy, leaving factor indeterminacy underutilized.

Purpose of the Study:

  • To propose a novel method, clustered common factor exploration (CCFE), to address factor indeterminacy in FA.
  • To enhance the interpretability of factor scores by clustering individuals based on determined and selected undetermined parts.
  • To leverage factor indeterminacy for improved FA model interpretation.

Main Methods:

  • CCFE selects the undetermined part of factor scores to maximize the cluster classification of individuals.
  • An alternating least squares algorithm is developed to implement the CCFE procedure.
  • The method is demonstrated using real-world data examples.

Main Results:

  • CCFE successfully generates well-clustered common factor scores, facilitating easier interpretation.
  • The proposed approach provides a new way to resolve factor indeterminacy by focusing on score clustering.
  • The method is shown to be effective in practical applications.

Conclusions:

  • Clustered common factor exploration (CCFE) offers a valuable new approach to interpreting factor analysis models.
  • By utilizing factor indeterminacy, CCFE enhances the practical utility of FA by yielding interpretable factor score clusters.
  • This method represents a novel contribution to the field of factor analysis interpretation.