Collaborative Research: Constrained Finite Mixture Models for Psychological Diagnosis and Educational Assessment

本项目将开发和实施几种新的有限混合建模方法，用于心理诊断和教育评估。在将这种方法扩展到心理学应用时，将对有限混合模型进行一些修改，以解决心理学研究中存在的不同数据类型，模型要求和诊断特征（例如，由精神障碍诊断和统计手册提供）。研究的具体目标是：(1)开发用于心理测量和诊断的受限有限混合模型；(2)开发利用多种数据类型的能力，以更有效地确定基于《精神疾病诊断与统计手册》规定的标准的概况；(3)构建探索性程序，将行为数据与《精神疾病诊断与统计手册》定义的诊断标准联系起来；(4)为从业者提供广泛可用的软件来利用这些新模型。这项研究的目标将导致心理学，教育评估和统计学的贡献。在心理学中，《精神疾病诊断与统计手册》根据已达到或未达到的标准概况来定义病理学。通常，有多种情况可以导致相同的诊断（病理或非病理），患者可以根据他们的情况进行区分，以更好地选择治疗方案。这项研究将加深对心理障碍的复杂性和不同类型患者的治疗需求的理解。这些方法将有益于心理学的许多其他领域，如发展变化的评估和人格特征的研究。在教育方面，这些方法将进一步为学生提供有关其表现的诊断性反馈，为学生的改进和补救提供途径。这些工具超越了传统的测试形式（例如纸笔测试）和典型的考生群体，允许采用其他评估方法。在统计学中，这个项目拓宽了一套用于分类的程序。作为研究的一部分开发的软件将使研究人员和实践者能够在广泛的应用中实现这些方法。

This project will develop and implement several novel approaches of finite mixture modeling to psychological diagnosis and educational assessment. In extending this methodology to psychological applications, several modifications of the finite mixture models will be made to address the differing data types, model demands, and diagnostic characteristics (for instance, as provided by the Diagnostic and Statistical Manual of Mental Disorders) present in psychological research. Specific objectives of the research are: (1) develop constrained finite mixture models for psychological measurement and diagnosis, (2) develop the capability of utilizing multiple data types to more efficiently determine profiles underlying the criteria specified in the Diagnostic and Statistical Manual of Mental Disorders, (3) construct exploratory procedures for linking behavioral data to diagnostic criteria defined by the Diagnostic and Statistical Manual of Mental Disorders, and (4) provide widely available software for practitioners to utilize these new models.The objectives of this research will result in contributions to psychology, educational assessment, and statistics. In psychology, the Diagnostic and Statistical Manual of Mental Disorders defines pathology based on a profile of criteria that have or have not been met. Typically, there are multiple profiles that can lead to the same diagnosis (pathological or not), where patients can benefit from being differentiated based on their profile to better select treatment options. This research will develop understanding of the complex nature of psychological disorders and the resulting treatment needs of the differing types of patients. Such methods will be beneficial to many other areas of psychology, such as assessment of developmental changes and the study of personality traits. In education, these methods will further the ability to provide students with diagnostic feedback about their performance, providing avenues for student improvement and remediation. These tools extend beyond traditional testing formats (e.g. paper and pencil tests) and typical examinee populations, allowing for alternative methods of assessment. In statistics, this project broadens a set of procedures used for classification. Software developed as part of the research will enable researchers and practitioners to implement such methods in a wide spectrum of applications.