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An approach to structural equation modeling with both factors and components: Integrated generalized structured

Heungsun Hwang1, Gyeongcheol Cho1, Kwanghee Jung2

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Integrated generalized structured component analysis (IGSCA) is a new statistical method for analyzing complex data. IGSCA outperforms existing methods like partial least squares (PLSc) in estimating model parameters accurately.

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Area of Science:

  • Statistics
  • Structural Equation Modeling
  • Component Analysis

Background:

  • Existing statistical methods struggle to analyze models with both components and factors simultaneously.
  • Generalized structured component analysis (GSCA) and its variant GSCAM have limitations in unified parameter estimation.

Purpose of the Study:

  • To introduce Integrated Generalized Structured Component Analysis (IGSCA), a unified statistical approach.
  • To evaluate the performance of IGSCA against existing methods, particularly partial least squares (PLSc).

Main Methods:

  • Developed IGSCA by integrating GSCA and GSCAM for simultaneous estimation of factor and component parameters.
  • Conducted two simulation studies to assess parameter recovery and compare IGSCA with PLSc under various conditions.
  • Applied IGSCA to real-world data investigating gene influence on depression.

Main Results:

  • IGSCA and PLSc were the only methods to provide unbiased estimates for all parameters in the first simulation.
  • IGSCA demonstrated superior performance compared to PLSc across various experimental factors in the second simulation.
  • The real data application highlighted IGSCA's utility in complex biological and psychological research.

Conclusions:

  • IGSCA offers a robust and accurate approach for analyzing models with both components and factors.
  • IGSCA provides a significant advancement over existing methods for complex statistical modeling.
  • Future research should explore IGSCA's implications and limitations further.