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Temporal Dynamics, Intervention Analysis and Policymaking: Latent Trajectory Group Analyses of Service Interventions for Homeless Mentally Ill

by William McAllister (ISERP), Mary Clare Lennon (Sociomedical Sciences), Daniel Herman (Epidemiology), and Jennifer Hill (International and Public Affairs)

The broad goal of this project is to make more feasible incorporating the temporal dynamics of people's lives into intervention analyses and programs and into theorizing and policymaking more generally. It accomplishes this by comparing the analytic utility of two relatively new approaches in "latent group trajectory analysis"—growth mixture modeling and optimal matching—for understanding people who are homeless and mentally ill. Both techniques focus on the temporal dynamism of people's lives—the sequencing, duration and timing of an outcome of interest (e.g., homelessness)—and group those with similar but latent trajectories. The techniques differ radically, however, in how they arrive at such groupings. For example, optimal matching is non-parametric, relying on algorithms derived from DNA sequencing, while growth mixture modeling is parametric, with roots in hierarchical and structural equations modeling. The project will specifically look at the relative analytic utility of each method for three important issues in intervention research and policymaking: assessing risk for homelessness, documenting intervention effects and determining intervention timing. The research studies comprising the project address each of these issues by analyzing two datasets typical of the kinds available to researchers: an experimentally designed dataset of a mental health intervention, and an observationally designed dataset of administrative programmatic data. This project is also a part of the NIMH-funded Columbia Center for Homeless Prevention Studies, directed by Carol Caton in the Mailman School of Public Health.

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