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This study introduces auto-regressive models for item response theory, accounting for sequential dependencies in cognitive tests. These models improve fit and provide more reliable estimates for progressive matrices.

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Area of Science:

  • Psychometrics
  • Cognitive Psychology
  • Educational Measurement

Background:

  • Traditional measurement models assume item response independence, often overlooking sequential dependencies.
  • Auto-regressive models are underexplored in item response theory, particularly for cognitive ability tests.

Purpose of the Study:

  • To extend binary item response models, specifically the 2-parameter logistic (2PL) model, to incorporate auto-regressive sequential dependencies.
  • To investigate the impact of sequential effects on cognitive ability testing, using progressive matrices as a case study.

Main Methods:

  • Development of an auto-regressive lag-1 2PL model.
  • Application and comparison of the proposed model against a traditional 2PL model using a publicly available progressive matrices dataset.

Main Results:

  • The auto-regressive lag-1 2PL model demonstrated superior model fit compared to the traditional 2PL model.
  • The proposed model yielded more conservative discrimination parameters and standard errors, suggesting improved precision.

Conclusions:

  • Sequential dependencies are likely a significant, overlooked factor in cognitive ability testing, especially in progressive matrices.
  • Auto-regressive models offer a more accurate representation of response processes in sequential testing scenarios.