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

合作研究：用于心理诊断和教育评估的约束有限混合模型

• 批准号: 0648861
• 负责人: Robert Henson
• 金额: $ 8.28万
• 依托单位: University of North Carolina Greensboro
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-04-15 至 2010-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0648861&HistoricalAwards=false
• 关键词: Collaborative; Research; Constrained; Finite; Mixture

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.

本计画将发展并实作数种新颖的有限混合模型方法，应用于心理诊断与教育评估。 在将这种方法扩展到心理学应用中时，将对有限混合模型进行几次修改，以解决心理学研究中存在的不同数据类型，模型需求和诊断特征（例如，由精神疾病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约束有限混合模型，（2）开发利用多种数据类型的能力，以更有效地确定《精神障碍诊断和统计手册》中规定的标准所依据的特征，（3）建立探索性程序，将行为数据与《精神障碍诊断和统计手册》定义的诊断标准联系起来，（4）提供广泛使用的软件，以供实践者使用这些新模型。本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，《精神障碍诊断和统计手册》根据已经或尚未满足的标准来定义病理学。 通常情况下，有多种特征可以导致相同的诊断（病理性或非病理性），其中患者可以受益于基于其特征进行区分，以更好地选择治疗方案。 这项研究将发展对心理障碍的复杂性质以及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质的研究。 在教育方面，这些方法将进一步提高向学生提供有关其表现的诊断性反馈的能力，为学生的改进和补救提供途径。 这些工具超越了传统的考试形式（例如纸笔考试）和典型的考生群体，允许采用其他评估方法。 在统计方面，该项目扩大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从业人员能够在广泛的应用中实施这些方法。