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A Small Sample Correction for Factor Score Regression.

Jasper Bogaert1, Wen Wei Loh1, Yves Rosseel1

  • 1Ghent University, Ghent, Belgium.

Educational and Psychological Measurement
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Summary
This summary is machine-generated.

A new small sample correction (SSC) improves bias correction for factor score regression (FSR) in small datasets. This method enhances accuracy compared to standard techniques, offering reliable structural equation modeling alternatives.

Keywords:
factor score regressionmeasurement errormethod of Croonsmall sample estimationstructural equation modeling

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Area of Science:

  • Statistics
  • Psychometrics
  • Quantitative Psychology

Background:

  • Factor score regression (FSR) is a common alternative to structural equation modeling (SEM) for latent variable analysis.
  • Measurement error in factor scores can introduce bias into structural parameter estimates.
  • Croon's method (MOC) is a known technique for correcting this bias, but can perform poorly in small samples.

Purpose of the Study:

  • To develop and evaluate a small sample correction (SSC) for Croon's method (MOC) in factor score regression (FSR).
  • To compare the performance of the proposed SSC against standard SEM, standard MOC, and naive FSR.

Main Methods:

  • A simulation study was conducted to compare estimation methods.
  • The study evaluated standard SEM, standard MOC, naive FSR, and MOC with the proposed SSC.
  • Robustness of the SSC was assessed across models with varying predictors and indicators.

Main Results:

  • The MOC with the proposed SSC demonstrated smaller mean squared errors in small samples compared to SEM and standard MOC.
  • The proposed SSC performed comparably to naive FSR but yielded less biased estimates.
  • Naive FSR showed higher bias due to its failure to account for measurement error.

Conclusions:

  • The proposed small sample correction (SSC) effectively addresses bias in factor score regression (FSR) for small sample sizes.
  • This improved MOC offers a more accurate and robust alternative to existing methods for latent variable analysis in limited data scenarios.