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Pseudolikelihood estimation of the Rasch model.

A Smit1, H Kelderman

  • 1Faculty of Work & Organizational Psychology, De Vrije Universtiteit, v.d. Boechorststraat 1, 1081 BT, Amsterdam, The Netherlands.

Journal of Outcome Measurement
|March 29, 2001
PubMed
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A new method estimates Rasch model parameters using pseudolikelihood theory. Simulation results show this maximum pseudolikelihood estimation is similar to existing conditional and unconditional maximum likelihood methods for item parameters.

Area of Science:

  • Psychometrics
  • Statistical modeling

Background:

  • The Rasch model is a widely used item response theory model.
  • Accurate estimation of item parameters is crucial for model application.
  • Existing estimation methods include conditional maximum likelihood (CML) and unconditional maximum likelihood (UML).

Purpose of the Study:

  • To propose a novel estimation method for the Rasch model based on pseudolikelihood theory.
  • To compare the performance of the proposed method against established CML and UML methods.

Main Methods:

  • The proposed method utilizes pseudolikelihood theory, drawing from Arnold and Strauss (1988).
  • A simulation study was designed to generate data under the Rasch model.
  • Item parameters were estimated using the new maximum pseudolikelihood (MPL) approach and compared with CML and UML estimates.

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Main Results:

  • The simulation study indicated a high degree of similarity between the MPL estimates and the CML and UML estimates.
  • No significant discrepancies were observed across the different estimation methods for item parameters.

Conclusions:

  • The proposed maximum pseudolikelihood estimation method provides results comparable to traditional methods for the Rasch model.
  • This new method offers a viable alternative for estimating Rasch model item parameters.