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

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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.