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Journals / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2020 / Cilt: 11 - Sayı: 3

A Short Note on Obtaining Item Parameter Estimates of IRT Models with Bayesian Estimation in Mplus

Sedat ŞEN
Allan COHEN
Seock-ho KIM
Pages
266–282
Publication Date
2020-09-27
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Results indicated Bayesian and MLE estimates differed only slightly, clearly demonstrating the consistency between estimates from the two methods.

📌 3 Key Takeaways

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Objective & Hypothesis:Parameter estimation of Item Response Theory (IRT) models can be applied using both Bayesian and non-Bayesian methods.
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Key Findings & Data:Although maximum likelihood estimation (MLE), a non-Bayesian method, has predominated since the 1970s, there is an increasing use of Bayesian methods, due to their capability for estimating complex models and for their implementation in commercially available software.
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Conclusion & Impact:Results indicated Bayesian and MLE estimates differed only slightly, clearly demonstrating the consistency between estimates from the two methods.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Item response theory · dichotomous models · Bayesian estimation

Abstract

Parameter estimation of Item Response Theory (IRT) models can be applied using both Bayesian and non-Bayesian methods. Although maximum likelihood estimation (MLE), a non-Bayesian method, has predominated since the 1970s, there is an increasing use of Bayesian methods, due to their capability for estimating complex models and for their implementation in commercially available software. In view of the recent increase in the popularity of these methods, a comparison between model parameter estimates from the two types of methods would be useful for practitioners. In this study, we compare MLE and Bayesian estimation, two popular methods for obtaining parameter estimates for dichotomous IRT models, using the MLE and Bayes estimator options as implemented in the Mplus software package. Results indicated Bayesian and MLE estimates differed only slightly, clearly demonstrating the consistency between estimates from the two methods. Further, Bayes estimator option in Mplus can be a viable and relatively easy to use tool for calibrations of IRT models.

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Cite This Article

ŞEN, S., COHEN, A., KIM, S. (2020). A Short Note on Obtaining Item Parameter Estimates of IRT Models with Bayesian Estimation in Mplus. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Cilt: 11 - Sayı: 3, 266–282. https://ajindex.com/articles/137295

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