Improving Health Research on Small Populations Proceedings of a Workshop (2018) / Chapter Skim
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4 Techniques Used in Survey Research to Identify and Find Small Populations for Health Research
4 Techniques Used in Survey Research to Identify and Find Small Populations for Health Research
Pages 31-50
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From page 31... ...
summarized the strengths and weakness of the approaches presented. PROBABILITY SAMPLING METHODS FOR SMALL POPULATIONS Marc Elliott provided background on probability sampling and shared examples well suited for probability sampling of rare populations: screening, disproportionate stratification at the level of the cluster and the person, and network sampling.
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In a probability sample everyone in the target population has a non-zero probability of being selected. These probabilities are either known or can be estimated.
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Oversampling at the Cluster and Household Levels Oversampling at the cluster level is another approach for sampling rare populations. The clusters might be hospitals with a high incidence of a condition or geographic areas with a high concentration of a target population.
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However, there are commercial lists of people highly likely to be Chinese, often generated by running surnames and addresses through telephone directories. Directly sampling from these lists would not result in a valid probability sample because the lists are incomplete.
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Lee noted that respondent-driven sampling was originally proposed by Douglas Heckathorn.2 It has frequently been applied in public health research to gather data on rare populations. Respondent-driven sampling uses an initial sample of the target population called seeds to start the process in the first wave.
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Under these assumptions, unbiased estimators can be obtained after equilibrium using weights equal to the number of nodes for a subject's recruiter. Injection Drug Users in Michigan In her first application of respondent-driven sampling, Lee discussed Positive Attitude Toward Health (PATH)
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Unlike injection drug users, Korean Americans are rare, but not a highly stigmatized group. Frequently when immigrants come to the United States, they develop ethnic enclaves.
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VENUE-BASED AND ONLINE SAMPLING Patrick Sullivan quoted from an issue of the Lancet (2012) 4 about the difficulty in addressing HIV in much of the world where men who have sex with men (MSM)
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In a recent study in Atlanta, where they were recruiting MSM, they identified 183 such venues using a threshold of 30 men per time period. These enumerations need to be validated by stopping suspected members of the target population, asking about demographic characteristics, age, and other information.
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This analysis identified a major area of high activity that was missing venues on the sampling frame, the Atlanta University Center, the site of four historically black colleges and universities. Online Sampling and Social Media Sullivan started his discussion of online sampling approaches by illustrating an evaluation of bias.8 He and his colleagues did a study almost 6 Wejnert, C., Hess, K.L., Rose, C.E., Balaji, A., Smith, J.C., and Paz-Bailey, G
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For example, MSM recruited through a sex-seeking app were more likely to have had an HIV test, to have had an STI test, to be living with HIV, and to report condomless anal intercourse, and were somewhat more likely to use marijuana. As a result, in preparing survey results, AMIS uses standardization to a general population to look at time trends and compare estimates over time.
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Quantifying uncertainty is important to consider when considering which methods to use. Overview and Comparison of the Methods Gile compared the sampling frames of the four methods discussed (probability sampling, respondent-driven sampling, venue-based sampling, and online sampling)
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Gile compared the four methods on different points: elements of formative research and rapport, setting up the sampling frame and what is known about sampling rates and decisions about participation, methods for statistical inference (point estimates, confidential intervals) , dependence between sampled individuals, and populations not suitable for each of the methods.
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In a probability sample, the design controls the different rates of inclusion for the different people in the sampling frame. This allows for more straightforward and clean-cut inference, which is why probability sampling is the gold standard for survey research.
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Venuebased sampling presents a valid sampling frame based on times and locations and avoids many biases that might occur in more subjective sampling methods. Finally, online sampling offers great ease of implementation and tremendous cost-benefit over the other approaches.
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Sullivan pointed out that for some populations there are no sampling frames, so probability sampling is not possible. Respondent-driven sampling arose because of hidden populations.
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However, he said, researchers generally want to make inferences about a larger population. This situation is actually a nonprobability sample of this larger population.
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She wondered what the panel thought about the approach of using multiple sampling methods to recruit a representative control group for a case control study. Gile responded that as a mechanism to match characteristics of two groups, she viewed this example as a targeted convenience sample of people who match the cases.
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Despite progress in the United States, there are places where receiving an HIV test kit in the mail may cause social harm. Kalton commented that Gile had called probability sampling the gold standard.
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