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An Entropy-Based Approach for Measuring Factor Contributions in Factor Analysis Models.

Nobuoki Eshima1, Minoru Tabata2, Claudio Giovanni Borroni3

  • 1Center for Educational Outreach and Admissions, Kyoto University, Kyoto 606-8501, Japan.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel entropy-based method to measure factor contributions in factor analysis, addressing limitations of the conventional sums of squared factor loadings approach. The new technique offers a more robust assessment of latent variable contributions.

Keywords:
entropy coefficient of determinationfactor contributionfactor loadingpath analysis

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Factor analysis conventionally assesses latent variable contributions using sums of squared factor loadings.
  • This traditional method has inherent limitations in accurately reflecting factor importance.

Purpose of the Study:

  • To critically examine the issues associated with the conventional method for assessing factor contributions.
  • To propose and demonstrate an alternative entropy-based approach for measuring factor contributions.

Main Methods:

  • The study analyzes the conventional sums of squared factor loadings method.
  • A novel entropy-based approach is developed to measure the contribution of common factor vectors to manifest variable vectors.
  • The proposed method allows for the decomposition of factor contributions.

Main Results:

  • The conventional method's limitations are identified.
  • The entropy-based approach provides a new framework for quantifying factor contributions.
  • A numerical example illustrates the practical application and effectiveness of the proposed method.

Conclusions:

  • The entropy-based approach offers a potentially more accurate and nuanced way to assess factor contributions in factor analysis.
  • This method enhances the understanding of latent variable influence on observed variables.