The project will develop an analytic methodology that will provide researchers with a means for extracting qualitative information about the dynamics of individual behavior from multi-wave, multi-variable data sets. This innovation will be timely and useful in that it coincides with recent improvements in study design and data collection methodologies that have led to increased availability of and interest in longitudinal data among social, behavioral, and economic researchers. The methodology will be motivated by a diverse range of applications that include studies of crop diversification of tobacco farmers, suicidal behavior of adolescents, and emotional changes monitored through "Experience Sampling Methods," among others. The methodology will be called Temporal Configuration Analysis (TCA) so as to emphasize the focus on detection and identification of meaningful patterns in individual behavior trajectories over time. The key element in TCA will be a dynamic model that tracks the transition of "latent states" from one time point to another for each individual. Latent states (e.g., health state, psychological state, political inclination) can be viewed as summary measures of observed variables that translate to or condition manifested behavior. TCA will allow a researcher to identify homogeneous subgroups of individuals in terms of their temporal trajectories of latent states and then to examine the profile of each subgroup. Accordingly, a researcher will be able to interpret the results from analysis of multi-wave data in terms of both observed and latent measures and their trajectories. This will provide additional behavioral meaning and insight. TCA will also feature a broad range of component statistical models to provide users with flexibility in handling commonly encountered data characteristics, such as cross-sectional dependence, state-dependence, and serial correlation. TCA will create a statistical framework for analyzing multi-wave data in which inter-temporal qualitative information is of interest.
The project will have immediate, broad, and significant impact on the social, behavioral, and economic sciences and statistical research. First, researchers will be able to analyze multi-wave data in a new way that is different from and more informative than traditional methods such as latent growth curve analysis. For example, it will be possible to describe and visualize behavioral responses by projecting these configurations onto the space of actual behavior. Second, the project will contribute to the science of statistics, specifically in the area of hidden Markov models (HMM). The investigators expect that once this method is developed, explicated, and applied, statisticians and mathematical scientists will expand the TCA framework to include additional innovative research applications. The project's impact will be broadened and maximized in several ways: (1) dissemination of accessible, user-friendly, high-quality software that will document the TCA methodology and allow researchers to use it easily; (2) integration of research and education at several levels-grades 10-12, undergraduate, and graduate; and (3) interaction between the team of applied and theoretical investigators and various research groups, both nationally and internationally, with emphasis on publishing applied research findings. The project will deliver Web-based end-products-high-quality programs in packages familiar to social, behavioral, and economic researchers. The investigators will collaborate with the Center of Excellence in Research, Teaching, and Learning (CERTL) at Wake Forest University, using its infrastructure and experience in training American students in science and engineering, to integrate research into education. The project team, which includes national and international researchers from several disciplines, will leverage this collaboration to increase both the impact and visibility of the project through extensive coordination with various national and international research groups. This award was supported as part of the fiscal year 2005 Mathematical Sciences priority area special competition on Mathematical Social and Behavioral Sciences (MSBS).
