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Using restricted factor analysis to select anchor items and detect differential item functioning.

Laura Kolbe1, Terrence D Jorgensen2

  • 1Department of Child Development and Education, University of Amsterdam, Amsterdam, The Netherlands. L.Kolbe@uva.nl.

Behavior Research Methods
|November 8, 2018
PubMed
Summary
This summary is machine-generated.

Restricted factor analysis (RFA) effectively tests for uniform differential item functioning (DIF). A rank-based anchor selection strategy showed lower contamination risk than iterative methods, while product indicators (PI) offered a better alternative to latent moderated structural equations (LMS) for detecting nonuniform DIF.

Keywords:
Differential item functioningFactor analysisLatent moderated structural equationsMeasurement invarianceProduct indicators

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Educational measurement

Background:

  • Restricted factor analysis (RFA) is crucial for detecting uniform differential item functioning (DIF).
  • Empirical anchor item selection in RFA can lead to inflated Type I error rates.
  • Detecting nonuniform DIF with RFA necessitates modeling interaction effects with latent factors.

Purpose of the Study:

  • To compare the performance of two empirical anchor-selection strategies in RFA.
  • To evaluate the effectiveness of product indicators (PI) versus latent moderated structural equations (LMS) for detecting nonuniform DIF in RFA.

Main Methods:

  • A simulation study was conducted to compare a one-step rank-based anchor selection strategy with an iterative selection procedure.
  • The study also compared the Type I error rates and power of product indicators (PI) against latent moderated structural equations (LMS) for modeling latent interactions in RFA.

Main Results:

  • The rank-based anchor selection strategy demonstrated a low risk and degree of contamination, even with small sample sizes, outperforming the iterative procedure.
  • Product indicators (PI) achieved comparable statistical power to LMS but exhibited significantly lower Type I error rates in detecting nonuniform DIF.

Conclusions:

  • The rank-based strategy is a robust method for selecting anchor items in RFA, mitigating contamination issues.
  • Product indicators (PI) provide a more statistically sound and reliable approach for modeling latent interactions to detect nonuniform DIF within the RFA framework.