Mental Health Computerized Adaptive Testing

描述（由申请人提供）：心理健康研究严重依赖于过时的测量系统。传统的心理健康量表的构建主要基于主观判断，最多是应用经典测试理论的方法来确定量表的心理测量学属性。在这种应用中，我们借用了教育测量和现代心理测量理论领域在考试建设和管理方面取得的重大进展。特别是，我们建议使用项目反应理论（IRT）来校准一个大的项目池626情绪障碍项目，然后自适应地管理它们，这样一个给定的主题可以评估的一个小的子集的项目，以任何实际的准确度。IRT模型的使用使我们能够评估不同受试者的情绪障碍的强度，这些受试者从项目库中选择了可能不同数量的项目。使用计算机自适应测试（CAT），我们可以自适应地选择最合适的一组项目为每个主题的基础上，他/她的反应，以前的项目，从一个小筛选组的项目，表征低到高水平的损害。最终的结果是，可以开发出彻底表征特定疾病的大型“项目库”。虽然按惯例不可能对任何一个科目的所有项目进行评估，但计算机辅助翻译允许对每个科目的一小部分项目进行评估，信息损失最小且可控。 将IRT应用于心理健康测量问题的一个复杂之处在于，与传统的能力测试不同（例如，数学成绩）本质上是一维的，而心理健康测量量表本质上是多维的。虽然多维度IRT模型是可用的，但在适应性测试的背景下，它们还没有得到很好的研究。注意，心理健康测量量表的多维度性的主要原因之一是项目通常从多个领域（例如，各种情绪障碍），从而违反了一维IRT模型的假设。为此，Gibbons和Hedeker（1992）开发了一个“项目双因素”模型，该模型允许每个项目加载在一个主要维度上（例如，抑郁症）和一个子域（例如，睡眠障碍）。 在心理健康测量的背景下，高因子模型的优点是，它产生了一个整体损害的措施，可以是适应性测试的重点。在这项申请中，我们建议提供一个深入的可行性研究使用CAT和IRT的校准和管理的心理健康测量仪器。具体来说，我们建议开发必要的统计理论和软件，然后将其应用于情绪焦虑谱系障碍量表。

DESCRIPTION (provided by applicant): Mental health research relies heavily on antiquated systems of measurement. The construction of traditional mental health scales is based largely on subjective judgment, and at best, application of methods from classical test theory to determine a scale's psychometric properties. In this application we borrow strength from major advances in test construction and administration that have been developed in the fields of educational measurement and modem psychometric theory. In particular, we propose to use Item Response Theory (IRT) to calibrate a large item pool of 626 mood disorder items and then adaptively administer them such that a given subject can be evaluated on a small subset of the items to any practical degree of accuracy. The use of the IRT model allows us to evaluate the intensity of the mood disorder for different subjects who have taken potentially different numbers of items selected from the item pool. Using computerized adaptive testing (CAT) we can then adaptively select the most appropriate set of items for each subject based on his/her responses to previous items, beginning from a small screening set of items that characterize low to high levels of impairment. The net result is that large "item banks" can be developed that thoroughly characterize a particular disorder. Although it is not routinely possible for any one subject to be evaluated on all of the items, CAT permits each subject to be evaluated on a small subset of the total item pool, with minimal and controllable loss of information. A complication of applying IRT to mental health measurement problems is that unlike traditional ability testing (e.g., mathematics achievement) which are inherently unidimensional, mental health measurement scales are inherently multidimensional. Although multidimensional IRT models are available they have not been well studied in the Context of adaptive testing. Note that one of the primary reasons for the multidimensionality of mental health measurement scales is that the items are often sampled from multiple domains (e.g., various mood disorders), thereby violating the assumption of a unidimensional IRT model. To this end, Gibbons and Hedeker (1992) developed an "item bi-factor" model which allows each item to load on a primary dimension (e.g., depression) and one subdomain (e.g., sleep disturbance). In the context of mental health measurement, the advantage of the hi-factor model is that it yields a measure of overall impairment that can be the focus of adaptive testing. In this application, we propose to provide an in depth feasibility study of the use of CAT and IRT in the calibration and administration of mental health measurement instruments. Specifically, we propose to develop the statistical theory and software that is necessary, and to then apply it to the Mood-Anxiety Spectrum Disorders Scale.