Soğuksu Yeşim Beril, Demir Ergül
Turkish Ministry of National Education, Kahramanmaraş, Türkiye.
Ankara University, Türkiye.
Educ Psychol Meas. 2024 Dec 23:00131644241306024. doi: 10.1177/00131644241306024.
This study explores the performance of the item response tree (IRTree) approach in modeling missing data, comparing its performance to the expectation-maximization (EM) algorithm and multiple imputation (MI) methods. Both simulation and empirical data were used to evaluate these methods across different missing data mechanisms, test lengths, sample sizes, and missing data proportions. Expected a posteriori was used for ability estimation, and bias and root mean square error (RMSE) were calculated. The findings indicate that IRTree provides more accurate ability estimates with lower RMSE than both EM and MI methods. Its overall performance was particularly strong under missing completely at random and missing not at random, especially with longer tests and lower proportions of missing data. However, IRTree was most effective with moderate levels of omitted responses and medium-ability test takers, though its accuracy decreased in cases of extreme omissions and abilities. The study highlights that IRTree is particularly well suited for low-stakes tests and has strong potential for providing deeper insights into the underlying missing data mechanisms within a data set.
本研究探讨了项目反应树（IRTree）方法在缺失数据建模中的性能，并将其性能与期望最大化（EM）算法和多重填补（MI）方法进行比较。通过模拟数据和实证数据，在不同的缺失数据机制、测验长度、样本量和缺失数据比例下对这些方法进行评估。使用期望后验进行能力估计，并计算偏差和均方根误差（RMSE）。研究结果表明，与EM和MI方法相比，IRTree能提供更准确的能力估计，且RMSE更低。在完全随机缺失和非随机缺失情况下，其整体性能尤其出色，特别是在测验较长且缺失数据比例较低时。然而，IRTree在遗漏回答处于中等水平和中等能力的考生中最为有效，不过在极端遗漏和能力情况下，其准确性会下降。该研究强调，IRTree特别适用于低风险测试，并且在深入洞察数据集中潜在的缺失数据机制方面具有强大潜力。
