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Published on: September 19, 2012
Adaptive sampling in research on risk-related behaviors
Steven K Thompson1, Linda M Collins
1Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA.
Drug and Alcohol Dependence
|September 27, 2002
Summary
Adaptive sampling methods offer efficient ways to study hard-to-reach populations, like those with substance use disorders. These designs adjust during the study, improving data collection for rare or hidden groups.
Area of Science:
- Public Health Research Methods
- Epidemiology
- Social Sciences
Background:
- Conventional sampling designs are often insufficient for rare, unevenly distributed, hidden, or hard-to-reach populations.
- Substance use research frequently encounters these sampling challenges with groups such as injection drug users or nicotine-dependent adolescents.
Purpose of the Study:
- To introduce adaptive sampling designs to substance use researchers.
- To highlight the advantages of adaptive sampling over conventional methods for specific populations.
- To discuss various adaptive sampling techniques and their application.
Main Methods:
- Discussion of adaptive sampling designs, including link-tracing (snowball sampling, random walk, network sampling), adaptive allocation, and adaptive cluster sampling.
- Emphasis on the need for specialized estimation procedures tailored to adaptive sampling designs.
Main Results:
- Adaptive sampling designs can yield significantly improved estimates compared to conventional methods.
- These designs offer substantial efficiency gains for studying rare and clustered populations.
- Link-tracing and other adaptive procedures may be the only feasible methods for sampling hidden populations.
Conclusions:
- Adaptive sampling provides practical and efficient solutions for substance use research involving challenging populations.
- Proper estimation methods are crucial for maximizing the benefits of adaptive sampling designs.
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