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Stephenson et al find "alarmingly high rates" of intimate partner violence among male couples

Stafford's findings on gender gap in children's allowances suggest entrenched nature of wage gap

Sastry et al. find parents with childhood trauma more likely to have children with behavioral health problems

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Social Science One making available data that "may rival the total amount that currently exists in the social sciences"

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Bobbi Low retires

Seefeldt promoted to associate professor of social work, associate professor of public policy

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Screen shot of complex data analysis

New methods for modeling decision making in social science

8/1/2014 feature story

Elizabeth Bruch and colleagues develop a framework for linking marketing statistical models to agent-based models of decision making, and apply the statistical framework to decision-making phenomena of interest in population dynamics.

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Elizabeth Eve Bruch

Project Information:

Cognitively Plausible Models of Decision Making

The statistical models used in quantitative social science and public health research are rarely plausible models of the underlying behavior or decision-making process that gave rise to the social phenomenon under investigation. Researchers in business and marketing use much more sophisticated statistical models of how people navigate their environment and make decisions, which draw on insights from cognitive science and decision theory. But these methods have never been applied in population health to decision modeling, and they are much more difficult to master than techniques currently in use. An initial investigation into marketing statistical methods found no standard statistical software, but rather programs usually written from scratch; and no single model or methodological approach, but rather a loose “toolkit” of techniques or strategies customized for specific applications. In addition, models often rely on Bayesian estimation techniques, which require significant expertise outside of standardized software packages. My aims in this project are to: (1) master the statistical skills involved in estimating these models, and gain a formal understanding of the underlying theories of decision making; (2) adapt the marketing statistical models to new substantive applications; (3) develop a methodological framework for linking these “cognitively plausible” models of individual decision-making with agent-based models to understand the implications of decision strategies for aggregate population dynamics; and (4) explore how this statistical framework may be applied to a broader range of decision-making applications relevant to health research. My analysis will apply the choice-modeling framework in two areas of research: mate choice (as observed on an online dating website) and neighborhood choice.

Elizabeth Eve Bruch, Fred M. Feinberg

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