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A Two-Dimensional Multiple-Choice Model Accounting for Omissions.

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  • 1Department of Social Psychology and Methodology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain.

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|January 9, 2019
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Summary
This summary is machine-generated.

A new model (MCMO) explains omitted test answers as a latent propensity to omit, not guessing. Simulations and empirical data show MCMO improves model fit and validity for multiple-choice items.

Keywords:
guessingitem response theorymissing datamultiple-choice itemsnon-ignorable missing datanon-responsesomitted responsespolytomous responses

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Area of Science:

  • Educational Measurement
  • Psychometrics
  • Item Response Theory

Background:

  • Traditional multiple-choice models often treat omitted responses as incorrect.
  • Understanding the reasons for omissions is crucial for accurate ability estimation.
  • Existing models may not fully capture the nuances of test-taker behavior regarding omissions.

Purpose of the Study:

  • To introduce and validate a new two-dimensional Multiple-Choice Model accounting for Omissions (MCMO).
  • To investigate the MCMO's parameter recovery and accuracy through Monte Carlo simulations.
  • To compare the MCMO's fit and validity against established models using real-world data.

Main Methods:

  • Developed the MCMO based on Thissen and Steinberg's multiple-choice models, incorporating a latent propensity to omit.
  • Conducted Monte Carlo simulations varying sample size, test length, and omission rates.
  • Applied the MCMO to Trends in International Mathematics and Science Study (TIMSS) Advanced 2015 data, comparing it with B-MIRT and 3PL models.

Main Results:

  • MCMO demonstrated good recovery of scale and position parameters in simulations; pseudo-guessing parameters were less accurate but did not significantly impact ability estimation.
  • Empirical studies showed high reliability for ability estimates and better absolute item fit for MCMO compared to other models.
  • MCMO significantly improved convergent validity between multiple-choice and constructed-response items, and propensity to omit varied by country but not test content.

Conclusions:

  • The MCMO provides a more accurate representation of multiple-choice item responses, including omissions.
  • The model enhances the validity of ability estimates and the assessment of item fit.
  • The propensity to omit is influenced by examinee background (country) but is independent of specific test content.