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Related Experiment Videos

Modeling local item dependence with the hierarchical generalized linear model.

Hong Jiao1, Shudong Wang, Akihito Kamata

  • 1Psychometrics and Research Services, Harcourt Assessment, Inc., 19500 Bulverde Road, San Antonio, TX 78259, USA. hong_jiao@harcourt.com

Journal of Applied Measurement
|June 9, 2005
PubMed
Summary

This study introduces a three-level hierarchical generalized linear model (HGLM) to accurately model local item dependence (LID) arising from nested test items. The proposed HGLM outperforms traditional two-level models in estimating item difficulties and ability variance.

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Educational Measurement

Background:

  • Local item dependence (LID) occurs when test items are nested within common stimuli or item groups.
  • Existing models may not adequately account for contextual effects leading to LID.

Purpose of the Study:

  • To propose and evaluate a three-level hierarchical generalized linear model (HGLM) for modeling LID.
  • To compare the proposed model with a two-level HGLM that ignores nested structures.

Main Methods:

  • Development of a three-level HGLM to address LID in nested item structures.
  • Analysis of simulated data sets to test the proposed model.
  • Comparison with a Rasch-equivalent two-level HGLM.

Main Results:

  • The proposed three-level HGLM successfully captures LID and estimates its magnitude.
  • The three-level HGLM yielded smaller differences in item difficulty estimates compared to the two-level HGLM.
  • The three-level HGLM provided unbiased estimates of ability distribution variance, unlike the two-level HGLM which underestimated it.

Conclusions:

  • The three-level HGLM is effective for modeling LID caused by contextual effects in nested item structures.
  • Ignoring LID leads to biased estimates of item difficulties and ability variance.
  • The proposed model offers improved accuracy in psychometric analysis when LID is present.