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Dee Duygu Cetin-Berber1, Halil Ibrahim Sari2, Anne Corinne Huggins-Manley1

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Summary
This summary is machine-generated.

Handling missing data in computerized adaptive multistage testing is crucial. Imputation and full information maximum likelihood methods are superior to scoring missing data as incorrect for accurate examinee ability estimation.

Keywords:
Missing datacomputerized adaptive multistage testingimputation

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Area of Science:

  • Educational measurement
  • Psychometrics
  • Computerized adaptive testing

Background:

  • Accurate examinee ability estimation is vital for effective routing in computerized adaptive multistage testing (CAMT).
  • Missing responses can significantly complicate ability estimation, potentially leading to examinee misrouting.
  • Careful consideration of missing data handling methods is therefore essential in CAMT.

Purpose of the Study:

  • To investigate the performance of various missing data handling methods in CAMT.
  • To compare imputation techniques, full information maximum likelihood (FIML), and scoring missing data as incorrect.
  • To evaluate these methods under different missing data frameworks (MCAR, MAR, MNAR) and testing conditions.

Main Methods:

  • Comparison of four missing data handling strategies: two imputation techniques, FIML, and scoring missing as incorrect.
  • Evaluation under missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) conditions.
  • Benchmarking against a no-missing-data baseline.

Main Results:

  • Imputation and FIML methods demonstrated superior performance compared to scoring missing data as incorrect.
  • These preferred methods showed lower average bias and average root mean square error.
  • Higher correlations between estimated and true examinee ability were observed with imputation and FIML.

Conclusions:

  • Imputation and FIML are recommended for handling missing data in CAMT to ensure accurate ability estimation and routing.
  • Scoring missing responses as incorrect can lead to significant estimation errors.
  • The choice of missing data method impacts the reliability and validity of CAMT outcomes.