Partially Ordered Item Response Modeling for Longitudinal and Multivariate Data

这个研究项目将开发纵向和多维模型的方法与偏序集或反应集。对心理和社会结构的精确和有效测量是社会科学进步的关键。然而，许多心理结构，如人格，自我效能感和情绪智力没有直接观察。这些结构是通过带有指定回答格式的问卷工具（如多项选择题）间接测量的。分数是以一种传达有关结构的定量信息的方式分配给个人的。然而，在许多测量情况下，评分是一个挑战，可能没有最佳答案。得到的响应通常会产生混合了已排序响应和无法排序响应的数据。这样的结构形成偏序集或偏序集。该项目将通过将偏序集响应作为一种新的响应格式来推进测量科学。将开发可公开获得的软件。该项目将在北卡罗来纳州大学已建立的心理测量学研究生课程的支持下培训和指导研究生。与教育考试服务中心的合作活动将扩大本项目的影响。本研究项目将开发一种灵活的测量和预测模型，用于复杂环境中的偏序集反应，包括多元，多维和纵向数据。由于不同度量尺度的混合，偏序集是众所周知的具有挑战性的模型。这已经反映在迄今为止用于处理偏序集的主要基于非模型的方法中;例如，折叠类别或按加权平均值汇总。该项目的偏序集测量方法将基于潜变量建模理论。这种方法将使研究人员能够测试和伪造模型，以推进科学。偏序集模型将允许量化测量误差，这是其他方法通常缺乏的功能。该模型将通过模拟实验和广泛领域的实际应用进行验证，包括认知测试、从潜在类或聚类分析得出的偏序集、情景判断测试和态度调查。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This research project will develop methods for longitudinal and multidimensional models with partially ordered sets of responses or posets. Precise and valid measurement of psychological and social constructs are key to progress in the social sciences. However, many psychological constructs such as personality, self-efficacy, and emotional intelligence are not directly observed. These constructs are measured indirectly through questionnaire instruments with a designated response format, such as multiple-choice questions. Scores are assigned to individuals in a way that communicates quantitative information about the construct. In many measurement situations, however, scoring is a challenge and there may be no best answer. The resulting responses often result in data that are a mix of ranked responses and responses that cannot be ranked. Such a structure forms a partially ordered set or poset. This project will advance measurement science by including poset response as a new category of response format. Publicly available software will be developed. The project will train and mentor graduate students with the support of an established psychometric graduate program at the University of North Carolina. Collaborative activities with the Educational Testing Service will broaden the impact of this project.This research project will develop a flexible class of measurement and predictive models for poset responses within complex settings that include multivariate, multidimensional, and longitudinal data. Because of the mix of different measurement scales, posets are notoriously challenging to model. This has been reflected in the predominantly non-model-based methods that have so far been used for handling posets; e.g., collapsing categories or summarizing by weighted means. The project's approach to poset measurement will be based on the theory of latent variable modeling. This method will enable researchers to test and falsify the model to advance the science. The poset model will allow measurement errors to be quantified, a feature that is often lacking in other methods. The model will be validated using simulation experiments and real-world applications across a broad range of areas, including cognitive tests, posets derived from latent class or cluster analyses, situational judgment tests, and attitudinal surveys.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.