IRT and MIRT Models for Item Parameter Estimation With Multidimensional Multistage Tests

Paul A. Jewsbury and Peter W. van Rijn
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Paul A. Jewsbury: Educational Testing Service
Peter W. van Rijn: ETS Global

Journal of Educational and Behavioral Statistics, 2020, vol. 45, issue 4, 383-402

Abstract: In large-scale educational assessment data consistent with a simple-structure multidimensional item response theory (MIRT) model, where every item measures only one latent variable, separate unidimensional item response theory (UIRT) models for each latent variable are often calibrated for practical reasons. While this approach can be valid for data from a linear test, unacceptable item parameter estimates are obtained when data arise from a multistage test (MST). We explore this situation from a missing data perspective and show mathematically that MST data will be problematic for calibrating multiple UIRT models but not MIRT models. This occurs because some items that were used in the routing decision are excluded from the separate UIRT models, due to measuring a different latent variable. Both simulated and real data from the National Assessment of Educational Progress are used to further confirm and explore the unacceptable item parameter estimates. The theoretical and empirical results confirm that only MIRT models are valid for item calibration of multidimensional MST data.

Keywords: multistage testing; MIRT; item calibration; missing data (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:45:y:2020:i:4:p:383-402

DOI: 10.3102/1076998619881790

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