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Summary
This summary is machine-generated.

This study explored person-specific measurement models in Item Response Theory (IRT), finding that individual differences in item parameters can lead to underestimated discrimination and lower reliability in factor scores. These findings highlight potential sources of heterogeneity in testing applications.

Keywords:
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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Standard factor models can obscure individual differences in factor loadings.
  • Person-specific measurement models offer a more nuanced approach to understanding individual responses.

Purpose of the Study:

  • To investigate person-specific measurement models within Item Response Theory (IRT).
  • To assess the impact of person-specific discrimination and difficulty parameters on model fit and parameter estimation.

Main Methods:

  • Introduced random variation at the item-by-person level to create person-specific discrimination and difficulty parameters.
  • Applied standard fitting algorithms for the 2-parameter logistic (2PL) IRT model.
  • Utilized common diagnostic tools to evaluate person- and item-level misfit.

Main Results:

  • Modest evidence of person- or item-level misfit was detected using standard diagnostic tools.
  • Item difficulties were generally well-estimated, but item discriminations were noticeably underestimated.
  • Factor scores exhibited lower-than-expected reliability due to underlying heterogeneity.

Conclusions:

  • Person-specific IRT models represent limiting cases of more complex structures like multilevel or mixture models.
  • Unacknowledged sources of heterogeneity in testing applications can impact parameter estimation and score reliability.
  • The study underscores the importance of considering individual differences in measurement models.