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This study introduces a new method to detect intersectional differential item functioning (DIF) by considering the interaction of multiple demographic variables. The approach effectively identifies intersectional uniform DIF, enhancing fairness in assessments.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Sociology

Background:

  • Ensuring assessment fairness is crucial, with differential item functioning (DIF) screening being a key method.
  • Traditional DIF methods often overlook the complex interactions among multiple demographic identities, focusing only on main effects.
  • The intersectionality framework provides a lens to understand how combined demographic factors can uniquely impact individuals.

Purpose of the Study:

  • To propose a novel item response theory (IRT) approach for detecting intersectional DIF, which accounts for interactions among demographic variables.
  • To introduce the concept of intersectional impact, examining interaction effects on group-level mean ability.
  • To develop four distinct models for detecting various forms of intersectional DIF, including uniform and non-uniform DIF, with and without intersectional impact.

Main Methods:

  • Utilizing an intersectionality framework within an item response theory (IRT) model.
  • Implementing fixed effects to control for traditional DIF and random item effects to capture intersectional DIF.
  • Developing a regularized Gaussian variational expectation-maximization algorithm for efficient model estimation.

Main Results:

  • The proposed methods effectively detect intersectional uniform DIF (UDIF).
  • Detection of intersectional non-uniform DIF (NUDIF) was found to be more limited compared to UDIF.
  • Simulation studies validated the utility of the developed models for identifying intersectional DIF.

Conclusions:

  • The novel IRT approach effectively addresses the limitations of traditional DIF methods by incorporating intersectionality.
  • The developed models offer a more nuanced understanding of assessment fairness by considering the interplay of multiple demographic factors.
  • Further research may be needed to enhance the detection capabilities for intersectional non-uniform DIF.