Extending Locally Dependent Item Response Models for Analyzing Psychological and Social Surveys

这项研究涉及开发灵活的统计和心理测量学方法来分析来自教育测试和社会调查的项目反应数据。该项目包含两个主要组件。第一个部分是项目反应模型和扩展的方法论发展，特别是针对二分和多分反应的局部依赖的混合核模型。这些方法适用于在主体效应条件作用后不能独立发挥作用的物品。局部依赖项目的例子包括具有共同阅读词干的项目(如在阅读理解测试中)，或调查相关数量的项目，如感觉的频率和强度(如心理测试)。该项目预计将以几种方式推进标准项目反应模型：(1)利用可分离子模型处理项目群内的相关性，(2)纳入多个量表，(3)容纳特定于项目和特定于个人的协变量，以及(4)开发项目评级量表。该项目的第二个组成部分涉及应用程序。最近的例子包括大规模教育评估和与健康相关的生活质量研究。这项研究补充了最常用的项目反应模型--一维模型--以灵活的方式处理与单维假设的潜在微小偏差。该项目的好处包括提供与标准项目-反应模型兼容的更灵活的分析方法，这意味着保留了可解释性，并增加了新的功能，如处理广泛的反应数据(例如，带有协变量的数据或可作为评分表制定的数据)。鉴于人们对将改进的项目反应模型应用于较新的、往往更复杂的测试和调查越来越感兴趣，预计该项目将对改进工具的质量和随后对收集到的答复的分析产生影响。作为支持调查和统计方法研究的联合活动的一部分，这项研究得到了方法学、测量和统计计划和一个联邦统计机构联盟的支持。

This research concerns the development of flexible statistical and psychometric methods for analyzing item-response data from educational tests and social surveys. The project contains two main components. The first component is the methodological development of item response models and extensions, specifically the locally dependent hybrid kernel models for dichotomous and polytomous responses. These methods are applicable to items that do not function independently after conditioning on subject effect. Examples of locally dependent items include items that have a common reading stem (as in a reading comprehension test), or items that survey related quantities such as the frequency and the intensity of a feeling (as in a psychological test). The project is expected to advance standard item response models in several ways: (1) the handling of dependency within item clusters with separable submodels, (2) the incorporation of multiple scales, (3) the accommodation of item- and person-specific covariates, and (4) the exploitation of rating scales of items. The second component of this project addresses applications. Three data sets from three different areas - education, psychology, and health-related social study - have been identified, and each will be analyzed using the locally dependent models.Item response models are increasingly used in calibrating scientific instruments used for measuring human traits and behavior. Recent examples include large-scale educational assessment and health-related quality-of-life research. This research supplements the most commonly used item response model - the unidimensional model - with flexible ways of dealing with potential minor deviations from the unidimensionality assumption. The benefits of the project include providing more flexible analytic methods that are compatible with the standard item-response models, which means that interpretability is retained, and adding novel features such as the handling of a wide range of response data (e.g., data with covariates or data that can be formulated as a rating scale). Given the increasing interest in the applications of refined item-response models to newer and often more complex tests and surveys, this project is expected to have an impact on improving both the quality of instruments and the subsequent analysis of gathered responses. This research is supported by the Methodology, Measurement, and Statistics Program and a consortium of federal statistical agencies as part of a joint activity to support research on survey and statistical methodology.