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

扩展用于分析心理和社会调查的局部相关项目响应模型

• 批准号: 0417349
• 负责人: Edward Ip
• 金额: --
• 依托单位: Wake Forest University School of Medicine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0417349&HistoricalAwards=false
• 关键词: Extending; Locally; Dependent; Item; Response

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.

这项研究涉及开发灵活的统计和心理测量方法，用于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成部分。 第一个组成部分是项目响应模型和扩展的方法开发，特别是用于二分和多分响应的局部依赖混合核模型。 这些方法适用于在调节主体效果后不能独立发挥作用的项目。 局部相关项目的示例包括具有共同阅读主干的项目（如在阅读理解测试中），或调查相关数量的项目，例如感觉的频率和强度（如在心理测试中）。 该项目预计将以多种方式推进标准项目响应模型：（1）使用可分离子模型处理项目集群内的依赖性，（2）合并多个尺度，（3）适应项目和个人特定的协变量，以及（4）利用项目的评分量表。 该项目的第二个组成部分涉及应用程序。 已经确定了来自三个不同领域（教育、心理学和健康相关的社会研究）的三个数据集，每个数据集都将使用局部相关模型进行分析。项目响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的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响应模型（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响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，并添加了新的功能，例如处理各种响应数据（例如，具有协变量的数据或可以制定为评级量表的数据）。 鉴于人们对将精致的项目响应模型应用于更新且通常更复杂的测试和调查的兴趣日益浓厚，该项目预计将对提高工具的质量和收集的响应的后续分析产生影响。 这项研究得到了方法、测量和统计计划以及联邦统计机构联盟的支持，作为支持调查和统计方法研究的联合活动的一部分。