Analyses of Overly Dispersed Covariance within Latent Structures and Applications in Psychological and Behavioral Research

该研究项目将调查多个变量随时间的协变模型，并应用各种模型来分析社会和行为科学的数据。 理解协变往往是研究因果关系和导致特定结果的可能机制的第一步。 随着时间的推移，协变的研究可能会揭示有趣的和重要的模式内的相互关联的变量系统。 例如，在经济学和金融学中，研究市场数据协变模式的变化对资产管理和投资组合多样化具有重要意义。 世界各地的股票市场指数通常是相关的，但熊市和危机期间的相关性往往比正常时期高得多。 认知领域的共变，如记忆、推理和处理信息的速度，可用于评估早期认知障碍。 例如，在由于年龄而导致的一般认知能力下降的总体趋势中，各领域的表现差异可能表明存在问题。 该项目将为研究界开发工具，以促进对数据协变的解释。 该项目还将培养研究生和博士后研究人员。该项目将研究在潜在结构建模的背景下研究过度分散协变的不同方法。 过度分散的协变是指驱动变量之间的关联，但不被规则的潜在结构捕获的变化来源。 该项目将使用统计学和机器学习的最先进工具来检查来自社会和行为科学的数据。 该项目的一个独特的智力贡献将是这些工具集适应社会和行为科学数据，这些数据通常强调多个结果变量，而不是统计和机器学习中的多个预测变量。 该项目将由几个示例性应用程序组织，包括（1）态度调查中的反应一致性，（2）认知障碍的模式，（3）儿童之间形成友谊的变化动态，以及（4）老年人的认知需求日常活动。 这些应用在各自的内容中是广泛的。 虽然它们说明了不同的和具体的策略来处理过于分散的协变，他们作为原型的例子，在其他领域的研究类似的应用程序的进一步发展。

This research project will investigate models for covariation of multiple variables over time and apply the various models to analyze data from the social and behavioral sciences. Understanding covariation often is a first step in the study of causation and possible mechanisms that lead to specific outcomes. The study of covariation over time may reveal interesting and important patterns within a system of interconnecting variables. In economics and finance, for example, studying the change in patterns of covariation of market data has important implications for asset management and portfolio diversification. Stock-market indexes across the world often are correlated, but the correlations during bear markets and crisis periods tend to be much higher than during normal times. Covariation in cognitive domains, such as memory, reasoning, and speed of processing information may be used to assess early cognitive impairment. For example, divergence in performance across domains within the overall trend of general cognitive decline due to age could indicate problems. The project will develop tools for the research community to facilitate the interpretation of covariation in data. The project also will train graduate students and postdoctoral researchers.This project will examine different approaches for studying overly dispersed covariation in the context of modeling with latent structures. Overly dispersed covariation refers to sources of variation that drive the association between variables but are not captured by regular latent structures. The project will use state-of-the-art tools from statistics and machine learning to examine data from the social and behavioral sciences. A unique intellectual contribution of the project will be the adaptation of these toolsets to social and behavioral science data that often emphasize multiple outcome variables rather than multiple predictor variables as in the case of statistics and machine learning. The project will be organized by several exemplary applications including (1) response consistency in attitudinal survey, (2) patterns of cognitive impairment, (3) dynamics of change in forming friendship among children, and (4) cognitively demanding daily activities in older adults. The applications are broad in their respective content. While they illustrate different and specific strategies for handling overly dispersed covariation, they serve as prototypical examples for further development of similar applications in other fields of study.