Collaborative Research: Longitudinal Diagnostic Models

诊断建模，也称为认知诊断，是心理测量学的一个领域，由于在社会科学(例如，表明心理障碍的标准)或教育(例如，考生可能拥有的技能)中诊断个人心理状态的潜力，近年来该领域的研究活动有所增加。到目前为止，这种模型已经被开发和扩展用于广泛的研究领域，并被证明在根据一组分类潜在变量对个体进行分类方面是有效的。虽然诊断模型的开发侧重于对单一场合收集的数据建立更全面和灵活的测量模型，但没有系统地努力将这些模型扩展到对个别变化的测量。简而言之，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。这项研究项目将集中于开发、评估和利用纵向扩展的诊断模型来测量随时间变化的情况。在开发纵向诊断模型时，必须取得进展，以平衡潜在的大量参数的模型需求与实用和准确的近似。研究人员将开发实用可行的纵向诊断测量、建模和评估方法，并检查其准确性、敏感性和相对效率。研究人员还将研究在现实世界纵向诊断分析中应用这些方法时的实际考虑因素和统计问题。将为研究人员和从业者提供免费提供的软件，以便他们在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，并增加可使用此类模型的研究和现实世界情景的数量。从这个项目开发的模型和软件将填补一个现有的空白，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，样本大小或测量次数)将允许将此类模型应用于研究行为现象如何随时间变化的广泛的实证研究领域。从这个项目开发的方法将使人们能够更严格地检查精神状态如何随着时间的推移而变化。这项研究的结果有可能极大地促进人们对诊断属性和临床标准方面的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.