According tothe results, the average SEM estimates of the Bootstrap method are less than the average SEM estimatesof JML in terms of the ability parameter.
📌 3 Key Takeaways
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Objective & Hypothesis:The ability parameter of persons/examinees estimates can be obtained using the Joint MaximumLikelihood (JML) estimation in Item Response Theory (IRT).
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Key Findings & Data:This research evaluates the performances ofJML and Bootstrap estimates of the ability parameter in terms of Standard Error Measurement (SEM) inthe 2-Parameter Logistic (2-PL) model conducting a detailed Monte Carlo simulation study.
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Conclusion & Impact:According tothe results, the average SEM estimates of the Bootstrap method are less than the average SEM estimatesof JML in terms of the ability parameter.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
The ability parameter of persons/examinees estimates can be obtained using the Joint MaximumLikelihood (JML) estimation in Item Response Theory (IRT). However, JML estimates can be biased insome cases. Although the Bootstrap method has been considered for JML, existing studies remain farfrom satisfactory concerning the ability parameter estimation. This research evaluates the performances ofJML and Bootstrap estimates of the ability parameter in terms of Standard Error Measurement (SEM) inthe 2-Parameter Logistic (2-PL) model conducting a detailed Monte Carlo simulation study. According tothe results, the average SEM estimates of the Bootstrap method are less than the average SEM estimatesof JML in terms of the ability parameter.
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Cite This Article
Nazman, E., Olmuş, H. (2022). Inferences from Bootstrap Method for Ability Parametersin 2-Parameter Logistic Model. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 16 - Sayı: 3, 333–338. https://ajindex.com/articles/472159
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