An adaptive weight estimation approach is proposed to provide robust latent ability estimation in computerized adaptive testing (CAT) with response revision. This approach assigns different weights to each distinct response to the same item when response revision is allowed in CAT. Two types of weight estimation procedures, nonfunctional and functional weight, are proposed to determine the weight adaptively based on the compatibility of each revised response with the assumed statistical model in relation to remaining observations. The application of this estimation approach to a data set collected from a large-scale multistage adaptive testing demonstrates the capability of this method to reveal more information regarding the test taker’s latent ability by using the valid response path compared with only using the very last response. Limited simulation studies were concluded to evaluate the proposed ability estimation method and to compare it with several other estimation procedures in literature. Results indicate that the proposed ability estimation approach is able to provide robust estimation results in two test-taking scenarios.

提出了一种自适应权重估计方法，以在具有响应修订的计算机自适应测试（CAT）中提供鲁棒的潜在能力估计。当在CAT中允许对响应进行修订时，此方法为同一项目的每个不同响应分配不同的权重。提出了两种类型的权重估计程序，即非功能权重和功能权重，以根据每个修订后的响应与假设的统计模型相对于其余观测值的兼容性来自适应地确定权重。将该估计方法应用于从大规模多阶段自适应测试收集的数据集，证明了该方法具有通过使用有效响应路径而不是仅使用最后响应来揭示与应试者潜在能力有关的更多信息的能力。总结了有限的模拟研究，以评估提出的能力估计方法并将其与文献中的其他几种估计程序进行比较。结果表明，所提出的能力估计方法能够在两种测试情况下提供可靠的估计结果。