According to the results, the average SEM estimates of Bootstrap method are less than the average SEM estimates of JML in terms of the ability parameter.
📌 3 Key Takeaways
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Objective & Hypothesis:Ability parameter of persons/examinees estimates can be obtained using Joint Maximum Likelihood (JML) estimation method in Item Response Theory (IRT).
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Key Findings & Data:This research evaluate the performances of JML and Bootstrap estimates of ability parameter in terms of Standard Error Measurement (SEM) in 2-Parameter Logistic (2-PL) model conducting a detailed Monte Carlo simulation study.
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Conclusion & Impact:According to the results, the average SEM estimates of Bootstrap method are less than the average SEM estimates of JML in terms of the ability parameter.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:ability parameter · difficulty parameter · joint maximum likelihood estimation
Abstract
Ability parameter of persons/examinees estimates can be obtained using Joint Maximum Likelihood (JML) estimation method in Item Response Theory (IRT). However, JML estimates can be biased in some cases. Although Bootstrap method has been considered for JML, existing studies remain far from satisfactory with respect to the ability parameter estimation. This research evaluate the performances of JML and Bootstrap estimates of ability parameter in terms of Standard Error Measurement (SEM) in 2-Parameter Logistic (2-PL) model conducting a detailed Monte Carlo simulation study. According to the results, the average SEM estimates of Bootstrap method are less than the average SEM estimates of JML in terms of the ability parameter.
OLMUŞ, H., NAZMAN, E. (2020). Inferences from Bootstrap Method for Ability Parameters in 2-Parameter Logistic Model. Celal Bayar Üniversitesi Fen Bilimleri Dergisi, Cilt: 16 - Sayı: 3, 333–338. https://ajindex.com/articles/88593
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