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: 0648876
• 负责人: Jonathan Templin
• 金额: $ 9.29万
• 依托单位: University of Kansas Center for Research 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: 2007-04-15 至 2007-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0648876&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。在将这一方法扩展到心理学应用时，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《精神障碍诊断和统计手册》提供的)。这项研究的具体目标是：(1)开发用于心理测量和诊断的受限有限混合模型，(2)发展利用多种数据类型更有效地确定基于《精神疾病诊断和统计手册》所规定标准的特征的能力，(3)构建探索性程序以将行为数据与《精神疾病诊断和统计手册》所定义的诊断标准相联系，以及(4)为从业者提供广泛可用的软件来使用这些新模型。本研究的目的将对心理学、教育评估和统计学做出贡献。在心理学中，《精神疾病诊断和统计手册》根据已满足或未满足的标准的概况来定义病理学。通常，有多个配置文件可以导致相同的诊断(无论是否病理)，其中患者可以受益于根据他们的配置文件进行区分，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质以及不同类型患者由此产生的治疗需求的理解。这些方法将有益于心理学的许多其他领域，如对发展变化的评估和人格特征的研究。在教育中，这些方法将进一步提高向学生提供关于其表现的诊断反馈的能力，为学生提供改进和补救的途径。这些工具超越了传统的考试形式(例如纸质和铅笔考试)和典型的考生群体，允许使用其他评估方法。在统计方面，该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从业者能够在广泛的应用中实施这种方法。