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

本研究关注灵活的统计和心理测量方法的发展，以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部分。第一个组成部分是项目响应模型和扩展的方法学发展，特别是二分和多分响应的局部依赖混合核模型。这些方法适用于经主体效应调节后不能独立起作用的项目。局部依赖项目的例子包括具有共同阅读梗的项目（如在阅读理解测试中），或调查相关数量的项目，如频率和强度的感觉（如在心理测试中）。该项目预计将在几个方面推进标准项目反应模型：(1)使用可分离子模型处理项目集群内的依赖关系，(2)合并多个量表，(3)适应项目和个人特定的协变量，以及(4)利用项目评级量表。这个项目的第二个部分涉及应用程序。已经确定了来自三个不同领域（教育、心理学和与健康有关的社会研究）的三个数据集，每个数据集都将使用本地依赖模型进行分析。项目反应模型越来越多地用于校准用于测量人类特征和行为的科学仪器。最近的例子包括大规模教育评估和与健康有关的生活质量研究。本研究补充了最常用的项目反应模型-单维模型-灵活的方法处理潜在的小偏差的单维假设。该项目的好处包括提供更灵活的分析方法，与标准的项目-反应模型兼容，这意味着保留了可解释性，并增加了新的功能，例如处理广泛的反应数据（例如，带有协变量的数据或可以制定为评级量表的数据）。鉴于人们对将改进的项目反应模型应用于更新的、往往更复杂的测试和调查越来越感兴趣，预计该项目将对提高工具的质量和随后对收集到的反应的分析产生影响。作为支持调查和统计方法研究的联合活动的一部分，本研究得到了方法、测量和统计计划和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.