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首页> 外文期刊>Educational and Psychological Measurement >Taking the Missing Propensity Into Account When Estimating Competence Scores: Evaluation of Item Response Theory Models for Nonignorable Omissions
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Taking the Missing Propensity Into Account When Estimating Competence Scores: Evaluation of Item Response Theory Models for Nonignorable Omissions

机译：评估能力得分时应考虑缺失的倾向：不可忽略遗漏的项目响应理论模型的评估

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摘要

When competence tests are administered, subjects frequently omit items. These missing responses pose a threat to correctly estimating the proficiency level. Newer model-based approaches aim to take nonignorable missing data processes into account by incorporating a latent missing propensity into the measurement model. Two assumptions are typically made when using these models: (1) The missing propensity is unidimensional and (2) the missing propensity and the ability are bivariate normally distributed. These assumptions may, however, be violated in real data sets and could, thus, pose a threat to the validity of this approach. The present study focuses on modeling competencies in various domains, using data from a school sample (N = 15,396) and an adult sample (N = 7,256) from the National Educational Panel Study. Our interest was to investigate whether violations of unidimensionality and the normal distribution assumption severely affect the performance of the model-based approach in terms of differences in ability estimates. We propose a model with a competence dimension, a unidimensional missing propensity and a distributional assumption more flexible than a multivariate normal. Using this model for ability estimation results in different ability estimates compared with a model ignoring missing responses. Implications for ability estimation in large-scale assessments are discussed.
机译：在进行能力测验时，受试者经常遗漏物品。这些缺失的响应对正确估计熟练程度构成了威胁。基于模型的更新方法旨在通过将潜在的缺失倾向纳入度量模型来考虑不可忽略的缺失数据过程。使用这些模型时，通常会做出两个假设：（1）缺失倾向是一维的;（2）缺失倾向和能力是双变量正态分布的。但是，这些假设可能会在实际数据集中被违反，因此可能对该方法的有效性构成威胁。本研究着重使用国家教育小组研究的学校样本（N = 15396）和成人样本（N = 7256）的数据对各个领域的能力进行建模。我们的兴趣是研究在能力估计方面的差异是否违反一维性和正态分布假设是否严重影响了基于模型的方法的性能。我们提出了一个具有能力维度，一维缺失倾向和分布假设的模型，该模型比多元正态更灵活。与忽略缺失响应的模型相比，使用此模型进行能力估计会导致不同的能力估计。讨论了能力评估在大规模评估中的含义。

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