241951
Using the law to define a network for policy analysis and the problem of attrition bias: Three solutions to validate network measures
Monday, October 31, 2011: 2:30 PM
Jason A. Smith, MTS, JD
,
Division of Medical Humanities, Health Law & Ethics, University of Connecticut School of Medicine, Farmington, CT
Bing Wang, PhD
,
Computer Science and Engineering Department, University of Connecticut School of Medicine, Storrs, CT
Yuexin Mao, MS
,
Computer Science and Engineering Department, University of Connecticut, Storrs, CT
This presentation will discuss the use of the law to create both a nominally-defined and realist-defined social network and the problem of attrition bias. Specifically, the presentation will address the effect of attrition bias on three network measures: degree, betweeness, and closeness. Using three different sampling approaches: a proportion method, a uniform method and, an opposite method; the presentation will discuss the approaches and their effect on the results of the nominally-defined network when absentee actors are reintroduced to the network based on the three different sampling methods.
Learning Areas:
Public health or related laws, regulations, standards, or guidelines
Public health or related public policy
Social and behavioral sciences
Systems thinking models (conceptual and theoretical models), applications related to public health
Learning Objectives: 1. Describe three different approaches to compensate for attrition bias.
2. Explain the use of the law to create both nominally-defined and realist-defined social networks.
Keywords: Methodology, Law
Presenting author's disclosure statement:Qualified on the content I am responsible for because: I supervised the research and designed the study.
Any relevant financial relationships? No
I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines,
and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed
in my presentation.
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