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: 0750859
• 负责人: Jonathan Templin
• 金额: $ 9.29万
• 依托单位: University of Georgia Research Foundation Inc
• 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: 2007-06-17 至 2010-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0750859&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.

该项目将开发并实施几种有限混合建模的新方法，以进行心理诊断和教育评估。 在将这种方法扩展到心理应用中，将对有限混合模型进行几种修改，以解决心理学研究中存在的诊断和统计手册所提供的不同数据类型，模型需求和诊断特征（例如，如诊断和统计手册所提供的）。 研究的具体目标是：（1）开发有限的有限混合模型进行心理测量和诊断，（2）发展能力，可以使用多种数据类型来更有效地确定精神疾病诊断和统计手册中指定标准的基础的概况，（3）通过诊断诊断和统计的统计数据定义诊断的启发性程序，并将其定义为诊断的过程，并将其定义为诊断症状。 （4）为从业人员提供广泛可用的软件来利用这些新模型。这项研究的目标将为心理学，教育评估和统计数据做出贡献。 在心理学中，精神障碍的诊断和统计手册定义了基于已经或尚未满足的标准的概况的病理学。 通常，有多种特征可以导致相同的诊断（无论病理是否存在），在这些诊断中，患者可以根据自己的概况而受益于更好的选择治疗方案。 这项研究将发展对心理疾病的复杂性质以及不同类型患者的治疗需求的理解。 这种方法将对许多其他心理学领域有益，例如评估发展变化和人格特征的研究。 在教育中，这些方法将进一步为学生提供有关其表现的诊断反馈，从而为学生改善和补救提供途径。 这些工具范围超出了传统的测试格式（例如纸和铅笔测试）和典型的考生人群，从而允许替代评估方法。 在统计数据中，该项目扩大了一组用于分类的程序。 作为研究的一部分而开发的软件将使研究人员和从业人员能够在各种应用程序中实施此类方法。