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Differential Item Functioning in item response models:new estimators and diagnostic instruments

项目响应模型中的差异化项目功能：新的估计器和诊断工具

基本信息

批准号：
262767932
负责人：
Professor Dr. Gerhard Tutz
金额：
--
依托单位：
Institut für Statistik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2014
资助国家：
德国
起止时间：
2013-12-31 至 2018-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/262767932?language=en
关键词：
Differential Item Functioning item response

项目摘要

Item response models build the methodological background for the measurement of performance and intelligence. The basic concept is that the solution of an item is determined by two latent traits, the ability of the person and the difficulty of the item. Item response models are intrinsically high-dimensional. The aim of the project is to develop regularization methods that are able to capture the deviation of specific items from the structure assumed by the model..The potential of regularized estimators is in particular used for the diagnosis of differential item functioning. Differential item functioning (DIF) describes the phenomenon that the difficulty of an item may vary over individuals depending on their social or cultural background yielding biased estimates of person abilities. It is planned to develop penalized likelihood and boosting methods that meet the complexity of the modeling task. Since the number of parameters is growing strongly when the potentially DIF-inducing variables are included all conventional estimators fail. The methods should be able to identify the items that are responsible for DIF and also find the predictors that are responsible for DIF. In addition, the methods are to be extended to fit models for items with ordered response. The methods to be developed are definitely beyond the methods that are available. The adaptation and modification of methods that have been first proposed in the machine learning community and in contemporary statistics to meet the challenges of DIF in item response models has just started. New methods are expected that crucially extend the tool box of available diagnostic instruments.

项目反应模型为绩效和智力的测量建立了方法学背景。其基本概念是，一个项目的解决方案是由两个潜在的特质，人的能力和项目的难度。项目反应模型本质上是高维的。该项目的目的是开发正则化方法，能够捕获特定项目与模型假设结构的偏差。正则化估计量的潜力特别用于诊断差异项功能。项目功能差异（Differential item functioning，DIF）是指一个项目的难度可能因个人的社会或文化背景而异，从而对个人能力产生偏见的现象。计划开发满足建模任务复杂性的惩罚似然和提升方法。由于参数的数量增长强劲时，潜在的DIF诱导变量包括所有传统的估计失败。这些方法应该能够识别负责DIF的项目，并找到负责DIF的预测因子。此外，该方法被扩展到适合模型的项目有序响应。要开发的方法肯定超出了现有的方法。在机器学习社区和当代统计学中首次提出的适应和修改方法，以应对项目响应模型中DIF的挑战，才刚刚开始。新的方法，预计至关重要地扩展了现有的诊断仪器的工具箱。