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This study introduces a new framework for differential item functioning (DIF) decomposition, separating item and testlet effects. The research offers practical implementation guidance for this advanced measurement technique.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Item Response Theory

Background:

  • Differential Item Functioning (DIF) analysis is crucial for detecting item bias in educational and psychological testing.
  • Existing DIF decomposition models, like Beretvas and Walker's (2012), often rely on complex estimation methods.
  • Practitioners require accessible methods for implementing advanced DIF analyses, such as DIF decomposition.

Purpose of the Study:

  • To present an alternative model-building framework and estimation approach for DIF decomposition.
  • To adapt DIF decomposition modeling within the random-weights linear logistic test model (RW-LLTM) framework.
  • To provide practical, implementable guidance for using DIF decomposition in item response theory (IRT).

Main Methods:

  • Developed a DIF decomposition model using the random-weights linear logistic test model (RW-LLTM).
  • Employed the marginal maximum likelihood (MML) estimation method for model parameter estimation.
  • Illustrated the model's performance and provided step-by-step implementation details for IRT software.

Main Results:

  • The proposed RW-LLTM framework offers a viable alternative for DIF decomposition modeling.
  • The MML estimation method provides an efficient approach for estimating model parameters.
  • Demonstrated the practical feasibility of implementing DIF decomposition with provided guidance.

Conclusions:

  • The new DIF decomposition framework provides a valuable tool for psychometricians and test developers.
  • Accessible implementation guidance can lower barriers for practitioners using advanced DIF analysis techniques.
  • This research enhances the practical application of DIF decomposition in test bias detection.