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Journals / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2026 / Volume: 16 Issue: 3

Stabilizing Maximum Likelihood Estimation with a Damping Factor in the Initial Phase of Computerized Adaptive Testing

Alper Tosun(AKDENİZ ÜNİVERSİTESİ)0000-0001-9715-5209
Eren Can Aybek(PAMUKKALE UNIVERSITY, FACULTY OF EDUCATION)0000-0003-3040-2337
Alper Sinan(AKDENIZ UNIVERSITY, FACULTY OF EDUCATION)0000-0001-6632-5500
Pages
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Publication Date
2025-09-30
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AI Summary & Key Takeaways

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⚡ TL;DR(Too Long; Didn't Read)

Results indicate that damping improves MLE stability, reducing extreme ability fluctuations and enhancing estimation accuracy, particularly in short tests and small sample conditions.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Maximum Likelihood Estimation (MLE) is a widely used ability estimation method in Item Response Theory (IRT)-based CAT applications.
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Key Findings & Data:Using Monte Carlo simulations based on a 3-Parameter Logistic (3PL) model in R, we examine the performance of the adjusted MLE compared to standard MLE, Maximum A Posteriori (MAP), and Expected a Posteriori (EAP) estimation methods.
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Conclusion & Impact:Results indicate that damping improves MLE stability, reducing extreme ability fluctuations and enhancing estimation accuracy, particularly in short tests and small sample conditions.
🔬Design:Lab & Field Experiment
📍Scope:Comprehensive Sample
⚙️Variables:Computerized Adaptive Testing · Maximum Likelihood Estimation · Maximum a Posteriori

Abstract

Maximum Likelihood Estimation (MLE) is a widely used ability estimation method in Item Response Theory (IRT)-based CAT applications. However, traditional MLE is highly sensitive to initial responses, often leading to substantial fluctuations and estimation instability, particularly in short tests or small item pools. This study investigates the effects of incorporating a damping factor into MLE at the early stages of CAT to mitigate undue ability estimate fluctuations. Using Monte Carlo simulations based on a 3-Parameter Logistic (3PL) model in R, we examine the performance of the adjusted MLE compared to standard MLE, Maximum A Posteriori (MAP), and Expected a Posteriori (EAP) estimation methods. Results indicate that damping improves MLE stability, reducing extreme ability fluctuations and enhancing estimation accuracy, particularly in short tests and small sample conditions.

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Cite This Article

Tosun, A., Aybek, E. C., Sinan, A. (2025). Stabilizing Maximum Likelihood Estimation with a Damping Factor in the Initial Phase of Computerized Adaptive Testing. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Volume: 16 Issue: 3. https://doi.org/10.21031/epod.1658558

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