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Modelling sequentially scored item responses.

W Akkermans1

  • 1University of Twente, Department of Educational Measurement and Data Analysis, Enschede, The Netherlands. l.m.w.akkermans@plant.wag-ur.nl

The British Journal of Mathematical and Statistical Psychology
|July 15, 2000
PubMed
Summary

This study examines item response models for sequential scoring. The partial credit and graded response models do not fit sequential scoring due to a lack of item characteristic curve invariance, leading to specification errors.

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Sequential scoring is a method used in assessments.
  • Item response theory (IRT) models are commonly applied in educational measurement.
  • The suitability of existing IRT models for sequential scoring requires investigation.

Purpose of the Study:

  • To investigate the suitability of the partial credit model and the graded response model for sequential scoring.
  • To identify a key property of sequential scoring and assess its presence in IRT models.

Main Methods:

  • Derivation of a property termed 'item characteristic curve invariance' for sequential scoring.
  • Empirical or theoretical demonstration of whether the partial credit model and graded response model satisfy this property.

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Main Results:

  • The partial credit model and the logistic graded response model were found to violate the property of item characteristic curve invariance.
  • This violation indicates a mismatch when these models are applied to sequentially scored data.

Conclusions:

  • The partial credit model and the graded response model are not appropriate for sequentially scored variables.
  • Applying these models leads to specification errors in the analysis of sequential scoring data.