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This study introduces a unified framework for factor analysis, handling mixed data types and missing values. It improves model parameter estimation and regularization for clearer loading patterns.

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Area of Science:

  • Statistics
  • Psychometrics
  • Data Analysis

Background:

  • Traditional factor analysis struggles with mixed data types and missing values.
  • Existing methods often lack a unified approach across the confirmatory-exploratory spectrum.

Purpose of the Study:

  • To develop a unified framework for factor analysis accommodating mixed data and missingness.
  • To simultaneously estimate parameters and regularize loading structure and local dependence.

Main Methods:

  • Utilized a combination of Bayesian adaptive and covariance Lasso procedures.
  • Developed several model variants with varying identification constraints.
  • Implemented the methodology in the R package LAWBL.

Main Results:

  • Satisfactory parameter recovery was achieved.
  • The framework successfully identified items not measuring intended constructs.
  • Loading estimates related to local dependence showed potential inflation, especially with categorical data or missingness.

Conclusions:

  • The proposed unified framework offers a flexible approach to factor analysis.
  • It enhances the ability to discern loading patterns and identify problematic items.
  • Further research may be needed to fully address information loss with specific data types.