Collaborative Research: Longitudinal Diagnostic Models

诊断建模，也被称为认知诊断，是心理测量学的一个领域，近年来由于诊断个人精神状态的潜力而增加了研究活动，例如在社会科学（例如，指示心理障碍的标准）或教育（例如，考生可能拥有的技能）。迄今为止，这些模型已被开发和扩展用于广泛的研究领域，并已被证明在一组分类潜在变量上对个体进行分类是有效的。虽然诊断模型的发展侧重于更全面和灵活的测量模型，用于在单一场合收集的数据，但没有系统的努力将这些模型扩展到个体变化的测量。简单地说，希望研究诊断状态随时间变化的研究人员目前无法做到这一点。本研究项目将集中于发展、评估和利用诊断模型的纵向扩展来测量随时间的变化。在开发纵向诊断模型时，必须取得进展，以平衡潜在大量参数的模型要求与实际和准确的近似值。研究人员将开发实用可行的纵向方法，用于诊断测量、建模和评估，他们将检查其准确性、灵敏度和相对效率。研究人员还将检查实际考虑和统计问题，在应用这些方法在现实世界的纵向诊断分析。将为研究人员和从业人员提供免费软件，以便在他们自己的研究中使用这些方法。该项目将扩大诊断模型的实用性，并增加可以使用这些模型的研究和现实场景的数量。该项目开发的模型和软件将填补现有的空白，随着时间的推移，这一空白限制了对诊断现象的研究。此外，关于每个模型所需数据结构的指导方针（即样本量或测量次数）将允许将这些模型应用于广泛的实证研究领域，研究行为现象如何随时间变化。从这个项目中开发的方法将使人们能够更严格地检查精神状态是如何随时间变化的。这项研究的结果有可能极大地促进对人们在诊断属性和临床标准方面如何发展和变化的基本理解。

Diagnostic modeling, also known as cognitive diagnosis, is a field of psychometrics that has seen increased research activity in recent years due to the potential for diagnosing the mental states of individuals, such as in the social sciences (e.g., criteria indicating psychological disorders) or in education (e.g., the skills examinees may possess). To date, such models have been developed and expanded for use in a broad spectrum of research areas and have been shown to be effective at classifying individuals on a set of categorical latent variables. Although the development of diagnostic models has focused on more comprehensive and flexible measurement models for data collected at a single occasion, there have been no systematic efforts to extend these models into the measurement of individual change. Simply put, the researcher who wishes to study changes in diagnostic status over time currently cannot do so. This research project will focus on the development, evaluation, and utilization of longitudinal extensions of diagnostic models for the measurement of change over time. In developing longitudinal diagnostic models, advances must be made that balance the model demands of potentially large numbers of parameters with practical and accurate approximations. The investigators will develop practical and feasible longitudinal methods for diagnostic measurement, modeling, and assessment, and they will examine their accuracy, sensitivity, and comparative efficiency. The investigators also will examine the practical considerations and statistical issues in applying these methods in real-world longitudinal diagnostic analyses. Freely available software will be provided for researchers and practitioners to utilize these methods in their own studies. This project will expand the practicality of diagnostic models and add to the number of research and real-world scenarios where such models can be used. The models and software developed from this project will fill an existing void that has limited the study of diagnostic phenomena over time. Furthermore, guidelines about the structure of data needed for each model (i.e., sample size or number of measurement occasions) will allow for application of such models across a wide spectrum of empirical research areas studying how behavioral phenomena change over time. The methods developed from this project will enable a more rigorous examination of how mental states change over time. The results of this research have the potential to greatly advance basic understanding of how people develop and change with respect to diagnostic attributes and clinical criteria.