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Multilevel Modeling of Cognitive Diagnostic Assessment: The Multilevel DINA Example.

Wen-Chung Wang1, Xue-Lan Qiu1

  • 1The Education University of Hong Kong, Tai Po, Hong Kong.

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|December 22, 2018
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Summary
This summary is machine-generated.

New multilevel cognitive diagnostic models (CDMs) address data complexities. Ignoring these structures in models like the DINA leads to inaccurate results, highlighting the need for advanced modeling techniques.

Keywords:
Bayesian methodscognitive diagnostic assessmentlarge-scale assessmentmultilevel models

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistical Modeling

Background:

  • Existing cognitive diagnostic models (CDMs) are typically unilevel, limiting their applicability to complex, multilevel data structures.
  • Multilevel linear and item response theory models exist, but a gap remains in applying CDMs to hierarchical data.

Purpose of the Study:

  • To develop and evaluate novel multilevel cognitive diagnostic models (CDMs) capable of handling hierarchical data structures.
  • To assess the performance of a new multilevel deterministic input, noisy, and gate (DINA) model in parameter recovery and compare it to unilevel approaches.

Main Methods:

  • Developed multilevel CDMs using the log-linear CDM, latent continuous variable approach, and multivariate Bernoulli distribution approach.
  • Utilized the multilevel DINA model as a case study in simulation studies.
  • Employed the freeware Just Another Gibbs Sampler (JAGS) for parameter estimation.

Main Results:

  • The newly developed multilevel DINA model demonstrated fair parameter recovery in simulation studies.
  • Ignoring multilevel structures by using standard unilevel DINA models resulted in biased estimates for student-level covariates and underestimated standard errors.
  • Failure to account for multilevel data led to poor recovery of individual latent attribute profiles.

Conclusions:

  • The developed multilevel CDMs effectively handle hierarchical data structures in cognitive diagnosis.
  • Ignoring multilevel data in CDMs can lead to significant estimation errors and inaccurate conclusions about individual abilities.
  • The multilevel DINA model offers a viable solution for analyzing complex educational assessment data.