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Related Experiment Video

Updated: Jul 1, 2026

The 5-Choice Serial Reaction Time Task: A Task of Attention and Impulse Control for Rodents
09:43

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Published on: August 10, 2014

Non-ignorable missingness item response theory models for choice effects in examinee-selected items.

Chen-Wei Liu1, Wen-Chung Wang1

  • 1Department of Psychology, The Education University of Hong Kong, Hong Kong.

The British Journal of Mathematical and Statistical Psychology
|April 9, 2017
PubMed
Summary

Examinee-selected item (ESI) design creates missing data that is often non-ignorable. A new two-dimensional item response theory (IRT) model effectively handles this missing data, improving parameter recovery in analyses.

Keywords:
choice effectexaminee-selected itemsmissing not at randommultidimensional item response theory

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Statistics

Background:

  • Examinee-selected item (ESI) design in assessments results in incomplete data.
  • Missing data in ESI designs are often missing not at random (MNAR), posing challenges for standard item response theory (IRT) models.
  • Existing IRT models are often infeasible with MNAR data.

Purpose of the Study:

  • To propose a novel two-dimensional IRT model to address MNAR data in ESI designs.
  • To demonstrate the non-ignorable nature of ESI data.
  • To evaluate the performance of the proposed model and the consequences of ignoring MNAR data.

Main Methods:

  • Development of a two-dimensional IRT model with separate components for observed data and selection patterns.
  • Assumption of a bivariate normal distribution for the two latent variables.
  • Parameter estimation using the mirt freeware package.
  • Conducting experimental and simulation studies to assess model performance.

Main Results:

  • ESI data were demonstrated to be frequently non-ignorable.
  • The proposed two-dimensional IRT model showed good parameter recovery.
  • Treating MNAR data as ignorable led to poor parameter recovery.

Conclusions:

  • The proposed two-dimensional IRT model offers a viable solution for analyzing ESI data with MNAR missingness.
  • Ignoring MNAR data in ESI designs can lead to significant estimation biases.
  • Accurate parameter estimation requires accounting for the non-ignorable nature of missing data.