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A Two-Level Alternating Direction Model for Polytomous Items With Local Dependence.

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  • 1National Board of Chiropractic Examiners, Greeley, CO, USA.

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|March 12, 2020
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Summary

This study introduces a new algorithmic approach to address local independence violations in chiropractic competency exams. Both Bayesian and generalized maximum likelihood methods showed good parameter recovery, with Bayesian methods slightly outperforming in discrimination parameter estimation.

Keywords:
Bayesian methodsMarkov Chain Monte Carlo (MCMC)generalized maximum likelihood estimation (GMLE)testlet response theory (TRT)violation of local independence

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Chiropractic Education

Background:

  • Chiropractic clinical competency examinations often use case vignettes, creating item dependencies.
  • Violations of the local independence assumption can impact the accuracy of test results.
  • Accurate parameter estimation is crucial for reliable educational assessments.

Purpose of the Study:

  • To evaluate a novel algorithmic approach for addressing local independence violations in testlet-based assessments.
  • To compare the performance of Bayesian and generalized maximum likelihood estimation methods in parameter recovery.
  • To assess the utility of the TensorFlow platform for complex psychometric computations.

Main Methods:

  • A two-level alternating directions testlet model was simulated to address local independence violations.
  • Item difficulty, discrimination, and test-taker ability parameters were generated.
  • Markov Chain Monte Carlo (MCMC) Bayesian methods and generalized maximum likelihood estimation (GML) were employed for parameter recovery.
  • The TensorFlow platform was utilized for computational efficiency.

Main Results:

  • Both MCMC Bayesian and GML methods demonstrated satisfactory parameter recovery.
  • Bayesian methods showed a slight superiority in recovering item discrimination parameters.
  • The study confirmed the feasibility of using the TensorFlow platform for complex psychometric modeling.

Conclusions:

  • The proposed algorithmic approach effectively addresses local independence violations in testlet-based assessments.
  • Bayesian methods offer a robust alternative for parameter estimation, particularly for discrimination parameters.
  • Accurate parameter and reliability estimates are achievable, enhancing the validity of competency examinations.