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Marginalized Maximum Likelihood Estimation for the 1PL-AG IRT Model.

Ryoungsun Park1, Keenan A Pituch1, Jiseon Kim2

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Summary
This summary is machine-generated.

A new marginal maximum likelihood estimation (MML/EM) method is presented for the one-parameter logistic model with ability-based guessing (1PL-AG) in item response theory (IRT). Results were validated against Statistical Analysis System procedures.

Keywords:
1PL-AGEMIRTMMLestimator

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Area of Science:

  • Psychometrics
  • Educational Measurement
  • Statistical Modeling

Background:

  • Item response theory (IRT) models are crucial for educational and psychological assessments.
  • The one-parameter logistic model with ability-based guessing (1PL-AG) is a commonly used IRT model.
  • Accurate parameter estimation is vital for reliable test scoring and analysis.

Purpose of the Study:

  • To develop and present a novel marginal maximum likelihood estimation (MML/EM) method for the 1PL-AG IRT model.
  • To validate the performance of the MML/EM estimator.
  • To compare the MML/EM estimator with existing methods.

Main Methods:

  • Development of the MML/EM algorithm specifically for the 1PL-AG model.
  • Cross-validation using the NLMIXED procedure in Statistical Analysis System (SAS).
  • Numerical data analysis for comparative assessment.

Main Results:

  • The MML/EM estimator provides a viable alternative for 1PL-AG model parameter estimation.
  • The MML/EM results demonstrate consistency with estimates from PROC NLMIXED.
  • Numerical comparisons highlight the performance characteristics of both methods.

Conclusions:

  • The proposed MML/EM method is effective for estimating parameters in the 1PL-AG IRT model.
  • The MML/EM approach offers a robust tool for psychometricians and researchers.
  • Further research can explore extensions of this estimation technique.