SOFTWARE AND TUTORING SYSTEM FOR LONGITUDINAL GLMMS

在这里，我们建议在以下方面实施最新的方法进展 广义线性混合效应模型(GLMM)对研究的响应 NIMH计划说明中的主题7B2和8C6。泛化 线性混合效应模型(GLMM‘s)通常用于数据分析 发生在纵向研究中。除去随机效应，这些 模型在横断面数据分析中也很常见。使用 更多的研究，最近的方法学进步应该会使 可以开发例行计算GLMM估计的算法 用于纵向数据。我们建议开发全面和容易的- 使用实现这些算法的商业软件。因为这些 算法对于新手用户来说将很难理解和使用，我们 还建议提供专家数据--关于使用 GLMM算法。该建议直接以HTML编写，将包括一个 包含实验室练习的在线教程，用于说明和解释 模型和算法，决策网络将使其更容易 用户选择合适的分析、样例，用户可以 在他们自己的分析中用作模板，链接到相关信息 网络和大量的在线帮助。将包括多媒体材料 在适当的情况下。 建议的商业应用： 广义线性混合效应模型是一种广泛应用的心理测量学方法 用于心理学、社会科学和其他行为学领域 和科学研究。我们建议实施的模型有 与现有技术相比具有显著的优势。教程、决策 网络，以及系统提供的指导将使这些方法 更广泛的研究人员可以接触到。建议的产品 应该会找到一个现成的市场。

Here we propose to implement recent methodological advances on generalized linear mixed effects models (glmm's) in response to research topics 7B2 and 8C6 in the program description for NIMH. Generalized linear mixed effects models (glmm's) can be used to analyze data commonly occurring in longitudinal studies. Removing the random effects, these models are also common in the analysis of cross-sectional data. With additional research, recent methodological advances should make it possible to develop algorithms that routinely compute glmm estimates for use on longitudinal data. We propose to develop comprehensive and easy- to-use commercial software implementing these algorithms. Because these algorithms will be difficult for novice users to understand and use, we further propose to deliver expert data-analytic advice on the use of the glmm algorithms. This advice, directly written in HTML, will include an online tutorial with laboratory exercises illustrating and explaining the models and algorithms, a decision network that will make it easier for users to select an appropriate analysis, worked examples that users can use as a template in their own analyses, links to related information on the web, and extensive online help. Multimedia materials will be included where appropriate. PROPOSED COMMERCIAL APPLICATIONS: Generalized linear mixed effects models are a psychometric method widely used in psychology, the social sciences, and other areas of behavioral and scientific research. The models we propose to implement have significant advantages over existing techniques. The tutorials, decision network, and guidance provided with the system will make these methods accessible to a wider audience of researchers. The proposed product should find a ready market.