Item Response Theory for Sequential Data in Drug Therapy

描述（由申请人提供）：研究阐明与青少年药物滥用的发病、病程和停止有关的家庭过程的类型，对于该人群的持续理论和干预发展至关重要。关于与青少年药物滥用有关的家庭过程，所知的大多数情况是基于家庭评估的自我报告方法。然而，最近，对家庭研究的观察方法的兴趣和进展大大增加。家庭研究者越来越多地利用观察评估策略来捕捉微妙的和动态的基于理论的家庭沟通过程，这些过程不能很好地用自我报告的方法来测量。不幸的是，现行的分析方法的观测编码数据的能力有限，直接建模的基础上观察到的通信序列的理论为基础的过程。因此，很少有直接的信息提供，以告知理论和干预的发展。在认识到需要在数据分析策略的进步，观察编码数据的家庭互动，拟议的项目旨在开发一种新的项目反应理论（IRT）的应用，可适用于从观察家庭评估的微编码数据。IRT非常适合于提供关于表征潜在特质或过程的特定行为模式的细粒度信息。与目前基于经典测试理论的序列数据分析方法相比，IRT分析方法可以更充分地利用观测数据的丰富性，对观测到的通信序列的过程进行理论驱动的研究。虽然IRT在历史上一直被用于传统测试项目的心理测量分析，但IRT软件的新发展极大地扩展了其在心理和行为研究中的潜在应用范围。目前的研究将开发一个非常灵活的广义线性混合模型（GLMM）框架内实施的IRT的应用程序。GLMM公式使得IRT能够适应观察数据的独特偶然性，包括家庭之间观察到的通信序列的数量和类型的随机变化。拟议项目的具体目标是（a）制定能够对观察到的家庭沟通序列进行建模的IRT模型，（B）使用有关家庭沟通模式的观察数据在非线性混合建模框架内执行IRT模型接受药物滥用治疗的青少年，（c）使用IRT模型参数，对观察到的沟通序列进行描述，以表征不同水平的"家庭凝聚力"，及（d）使用IRT模式参数，根据观察到的与青少年药物滥用水平相关的沟通模式，制定"家庭凝聚力"概况。

DESCRIPTION (provided by applicant): Research explicating the types of family processes connected to the onset, course, and cessation of drug abuse among adolescents is critical for ongoing theory and intervention development for this population. The majority of what is known regarding family processes related to adolescent drug abuse is based on self-report methods of family assessment. Recently, however, interest and advances in observational methods of family research have surged considerably. Family researchers are increasingly utilizing observational assessment strategies to capture subtle and dynamic theory-based family communication processes which are not well measured using self-report methods. Unfortunately, prevailing analytic approaches for observationally coded data are limited in the capacity to directly model theory-based processes underlying observed sequences of communication. Consequently, little direct information is provided to inform theory and intervention development. In recognition of the need for advances in data analytic strategies for observationally coded data on family interaction, the proposed project aims to develop a novel application of item response theory (IRT) which can be applied to microcoded data from observational family assessments. IRT is well suited for providing fine-grained information about specific patterns of behavior characterizing underlying latent traits or processes. Compared to current methods of sequential data analysis, all of which are grounded in classical test theory, an IRT analytic approach can more fully capitalize on the richness of observational data for conducting theory-driven research on processes underlying observed sequences of communication. Although IRT has historically been utilized for psychometric analyses of traditional test items, new developments in IRT software have greatly expanded its range of potential applications in psychological and behavioral research. The current study will develop an application of IRT implemented within the very flexible generalized linear mixed modeling (GLMM) framework. The GLMM formulation enables adaptations of IRT to fit the unique contingencies of observational data, including random variation in the number and types of observed communication sequences between families. The specific aims of the proposed project are (a) formulate an IRT model capable of modeling observed sequences of family communication, (b) execute the IRT model within a nonlinear mixed modeling framework using observational data on patterns of communication in families of adolescents in treatment for substance abuse, (c) use the IRT model parameters to develop descriptive profiles of observed sequences of communication characterizing different levels of "family cohesion", and (d) use the IRT model parameters to develop profiles of "family cohesion" based on observed patterns of communication that are associated with levels of adolescent substance abuse.