Partially Ordered Item Response Modeling for Longitudinal and Multivariate Data

该研究项目将开发具有部分有序响应集或偏序集的纵向和多维模型的方法。准确有效地测量心理和社会结构是社会科学进步的关键。然而，许多心理结构，如人格、自我效能和情商，并不是直接观察到的。这些结构通过具有指定回答格式的问卷工具间接测量，例如多项选择题。分数分配给个人的方式是传达有关结构的定量信息。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。所得到的响应通常是排序响应和无法排序响应的混合数据。这样的结构形成了偏序集或偏序集。该项目通过将偏置反应作为一种新的反应格式，将推动测量科学的发展。将开发公开可用的软件。该项目将在北卡罗来纳大学建立的心理测量学研究生项目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该项目的影响。该研究项目将开发一种灵活的测量和预测模型，用于包括多元、多维和纵向数据在内的复杂环境中的偏置响应。由于混合了不同的测量尺度，假设集的建模是出了名的具有挑战性。这反映在迄今为止主要用于处理poset的非基于模型的方法中；例如，折叠类别或通过加权方法进行汇总。该项目的偏置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。偏置集模型将允许测量误差被量化，这是其他方法通常缺乏的特征。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、来自潜在类或聚类分析的posset、情景判断测试和态度调查。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.