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Modeling Not-Reached Items in Cognitive Diagnostic Assessments.

Lidan Liang1,2,3, Jing Lu1, Jiwei Zhang4

  • 1Key Laboratory of Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun, China.

Frontiers in Psychology
|June 30, 2022
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Summary
This summary is machine-generated.

This study introduces a new model to address missing data in timed cognitive diagnostic assessments. The model accurately estimates item parameters and improves diagnostic feedback when test-takers cannot complete all items due to time constraints.

Keywords:
Bayesian analysiscognitive diagnosis assessmentsmissing data mechanismnot-reached itemssequential model

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Cognitive Science

Background:

  • Timed cognitive diagnostic assessments often result in not-reached items due to examinee dropout caused by time limitations.
  • These not-reached items are frequently linked to specific cognitive attributes or knowledge structures, indicating a non-ignorable missing data mechanism.
  • Existing models may not adequately account for this non-ignorable missingness in cognitive diagnostic contexts.

Purpose of the Study:

  • To propose and evaluate a novel missing data model specifically designed for not-reached items in cognitive diagnostic assessments.
  • To accurately model the relationship between examinee attributes, response patterns, and the propensity to drop out of the test.
  • To enhance the precision of diagnostic feedback and item parameter estimation in time-limited assessments.

Main Methods:

  • A sequential model with linear restrictions on missing indicator parameters was developed.
  • The deterministic inputs, noisy "and" gate (DI-NAND) model was employed to represent examinee responses.
  • A higher-order structure was incorporated to link ability parameters with dropout propensity, estimated using a Bayesian Markov Chain Monte Carlo method.

Main Results:

  • Simulation studies demonstrated that the proposed model significantly improved diagnostic feedback accuracy.
  • The model yielded accurate item parameter estimates, even when the missing data mechanism was non-ignorable.
  • The model's practical utility was confirmed through its application to the PISA 2018 computer-based mathematics assessment data.

Conclusions:

  • The developed missing data model effectively addresses the challenges posed by not-reached items in timed cognitive diagnostic assessments.
  • Accurate modeling of non-ignorable missing data enhances the reliability of cognitive attribute diagnosis and assessment outcomes.
  • This approach offers a valuable tool for improving the interpretability and accuracy of results from large-scale, time-constrained educational assessments.