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Risk in Context: New Methodology for Modeling Risk by Context Interactions

背景中的风险：通过背景交互进行风险建模的新方法

基本信息

批准号：
9215752
负责人：
M LEE VAN HORN
金额：
$ 1.75万
依托单位：
UNIVERSITY OF NEW MEXICO
依托单位国家：
美国
项目类别：
财政年份：
2007
资助国家：
美国
起止时间：
2007-08-22 至 2018-09-30
项目状态：
已结题

项目摘要

This grant will examine the use of new statistical methods for better identifying groups of respondents who differ in the effects of contexts (families, schools, and communities) on outcomes. The grant focuses on the use of regression mixture models, which have the unique ability to identify groups of subjects based on the relationship of predictors with outcomes. This work focuses on the use of these methods under conditions encountered when assessing the effects of contexts with real data. Specifically, we aim to: 1) test new methods for validating the results of regression mixtures; 2) examine the use of regression mixtures for assessing complex moderation where many contextual variables come together to cause differential effects; and 3) test the use of regression mixtures for assessing multilevel contextual effects, including developing a set of best practices for the use of these methods. These aims will be investigated through the use of Monte Carlo simulations as well as applications with three real datasets looking at the effects of families, schools, and communities on physical activity, achievement and social skills, and the development of depression. This grant aims to provide a basis for better finding differential effects, the tools developed will allow for better understanding of differences in the effects of environmental contexts which are expected to inform efforts to prevent and treat health problems.

这笔赠款将用于研究如何使用新的统计方法，以便更好地确定受访者群体他们在环境（家庭，学校和社区）对结果的影响方面有所不同。授出侧重于使用回归混合模型，它具有独特的能力，以确定群体的基于预测因素与结果的关系。这项工作的重点是使用这些在评估具有真实的数据的上下文的影响时遇到的条件下的方法。具体来说，我们的目标是：1）测试验证回归混合结果的新方法; 2）检查使用回归混合评估复杂的适度，其中许多上下文变量聚集在一起导致差异效应; 3）测试回归混合物的使用评估多层次的背景影响，包括制定一套最佳做法，这些方法。这些目标将通过使用蒙特卡罗模拟以及三个真实的数据集的应用程序，着眼于家庭、学校和社区对身体活动，成就和社交技能，以及抑郁症的发展。该补助金旨在为更好地发现差异效应提供基础，开发的工具将允许更好地理解环境背景的影响差异，这些影响预计将为努力预防和治疗健康问题。