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Conditional pairwise person parameter estimates in Rasch models.

Svend Kreiner1

  • 1Department of Biostatistics, University of Copenhagen, Copenhagen K, Denmark. S.Kreiner@biostat.ku.dk

Journal of Applied Measurement
|December 29, 2012
PubMed
Summary
This summary is machine-generated.

Conditional pairwise estimation in Rasch models separates item and person parameter inference. This study investigates person parameter estimation, offering a modification to reduce estimation error for better accuracy.

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Area of Science:

  • Psychometrics
  • Statistical modeling

Background:

  • Conditional pairwise estimation in Rasch models allows separate inference of item and person parameters.
  • While pairwise item parameter estimates are consistent with large sample sizes, they introduce extra error compared to conditional maximum likelihood estimates.
  • Pairwise person parameter estimates are simple to compute but often lack consistency due to small item numbers and unknown properties.

Purpose of the Study:

  • To investigate the properties of conditional pairwise estimation for person parameters in Rasch models.
  • To propose a modification to the conditional pairwise estimate for person parameters to mitigate estimation errors.

Main Methods:

  • Analysis of conditional pairwise estimation techniques for Rasch models.
  • Simulation study to evaluate the performance of existing and modified person parameter estimates.

Main Results:

  • Conditional pairwise estimates for person parameters can be subject to error, particularly with limited item sets.
  • The proposed modification to the conditional pairwise estimate demonstrates potential in reducing estimation error.
  • The study provides insights into the consistency and properties of person parameter estimates.

Conclusions:

  • Conditional pairwise estimation offers a method for separating parameter inference in Rasch models.
  • A modified conditional pairwise estimation approach shows promise for improving the accuracy of person parameter estimates.
  • Further research is warranted to fully explore the benefits and applications of the modified estimation technique.