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Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning.

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Summary
This summary is machine-generated.

This study introduces novel machine learning methods, specifically recursive partitioning, to detect differential item functioning (DIF) in large surveys. These techniques efficiently identify subgroups exhibiting DIF in complex measurement models.

Keywords:
algorithmic modelingcategorical analysisdifferential item functioninggraded response modelmachine learningmultidimensional item response theorysurveys

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Area of Science:

  • Psychometrics
  • Statistical modeling
  • Machine learning applications in social sciences

Background:

  • Differential item functioning (DIF) poses challenges in analyzing latent traits from large-scale surveys.
  • Existing methods may lack guidance when numerous potential subgroups exhibit DIF.

Purpose of the Study:

  • To propose and evaluate recursive partitioning techniques for DIF detection.
  • Focus on multidimensional latent variable models with ordinal data.

Main Methods:

  • Implementation of tree-based approaches for DIF subgroup identification.
  • Development of a scalable extension inspired by random forests.
  • Comparison via simulations.

Main Results:

  • Proposed methods efficiently detect DIF in complex measurement models.
  • Decision rules are extracted, defining subgroups with well-fitting models.
  • Effectiveness demonstrated through simulations.

Conclusions:

  • Recursive partitioning offers a powerful tool for DIF detection in multidimensional models.
  • The methods provide interpretable rules for identifying problematic subgroups.
  • Scalable extensions enhance applicability to large datasets.