دورية أكاديمية
Model-Based and Design-Based Inference: Reducing Bias Due to Differential Recruitment in Respondent-Driven Sampling
العنوان: | Model-Based and Design-Based Inference: Reducing Bias Due to Differential Recruitment in Respondent-Driven Sampling |
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اللغة: | English |
المؤلفون: | Shi, Yongren, Cameron, Christopher J., Heckathorn, Douglas D. |
المصدر: | Sociological Methods & Research. Feb 2019 48(1):3-33. |
الإتاحة: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: http://sagepub.com |
Peer Reviewed: | Y |
Page Count: | 31 |
تاريخ النشر: | 2019 |
Sponsoring Agency: | National Institute of Nursing Research (NIH) |
Contract Number: | 1R21NR10961 |
نوع الوثيقة: | Journal Articles Reports - Evaluative |
Descriptors: | Statistical Inference, Bias, Recruitment, Sampling, Probability, Models, Social Environment, Computation, Response Rates (Questionnaires) |
DOI: | 10.1177/0049124116672682 |
تدمد: | 0049-1241 |
مستخلص: | Respondent-driven sampling (RDS), a link-tracing sampling and inference method for studying hard-to-reach populations, has been shown to produce asymptotically unbiased population estimates when its assumptions are satisfied. However, some of the assumptions are prohibitively difficult to reach in the field, and the violation of a crucial assumption can produce biased estimates. We compare two different inference approaches: design-based inference, which relies on the known probability of selection in sampling, and model-based inference, which is based on models of human recruitment behavior and the social context within which sampling is conducted. The advantage of the latter approach is that when the violation of an assumption has been shown to produce biased population estimates, the model can be adjusted to more accurately reflect actual recruitment behavior, and thereby control for the source of bias. To illustrate this process, we focus on three sources of bias, differential effectiveness of recruitment, a form of nonresponse bias, and bias resulting from status differentials that produce asymmetries in recruitment behavior. We first present diagnostics for identifying types of bias and then present new forms of a model-based RDS estimator that controls for each type of bias. In this way, we show the unique advantages of a model-based estimator. |
Abstractor: | As Provided |
Number of References: | 41 |
Entry Date: | 2019 |
رقم الأكسشن: | EJ1203789 |
قاعدة البيانات: | ERIC |
تدمد: | 0049-1241 |
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DOI: | 10.1177/0049124116672682 |