Parameter Estimation with Mixture Item Response Theory Models: A Monte Carlo Comparison of Maximum Likelihood and Bayesian Methods
Parameter Estimation with Mixture Item Response Theory Models: A Monte Carlo Comparison of Maximum Likelihood and Bayesian Methods
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使用混合项目响应理论模型进行参数估计：最大似然法和贝叶斯方法的蒙特卡罗比较
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
B. French
中科院分区：
文献类型：
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作者：
W. H. Finch;B. French
混合项目反应理论 (MixIRT) 可用于识别数据中潜在的考生类别，并估计项目参数，例如每个组的难度和辨别力。比较了通过最大似然 (MLE) 的参数估计和基于马尔可夫链蒙特卡罗 (MCMC) 的贝叶斯估计的分类精度以及难度和辨别力的参数估计偏差。比较项目数量、潜在组数量、组大小比例、总样本量和基础项目响应模型的标准误差大小和覆盖率。结果表明，MCMC 可以在不同条件下提供更准确的组成员恢复，并为较小的样本和较少的项目提供更准确的参数估计。 MLE 可以为更大的样本和更多的项目生成比 MCMC 更窄的置信区间和更准确的参数估计。讨论了这些结果对研究和实践的影响。
The Mixture Item Response Theory (MixIRT) can be used to identify latent classes of examinees in data as well as to estimate item parameters such as difficulty and discrimination for each of the groups. Parameter estimation via maximum likelihood (MLE) and Bayesian estimation based on the Markov Chain Monte Carlo (MCMC) are compared for classification accuracy and parameter estimation bias for difficulty and discrimination. Standard error magnitude and coverage rates were compared across number of items, number of latent groups, group size ratio, total sample size and underlying item response model. Results show that MCMC provides more accurate group membership recovery across conditions and more accurate parameter estimates for smaller samples and fewer items. MLE produces narrower confidence intervals than MCMC and more accurate parameter estimates for larger samples and more items. Implications of these results for research and practice are discussed.