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Abstract #150093

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Jichuan Wang, PhD and Linna Li, MS. Community Health, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, 937-775-2084, jichuan.wang@wright.edu

Respondent-driven sampling (RDS) has been increasingly applied to sampling illicit drug use populations. RDS is capable of generating a more representative sample of a “hidden” population than the traditional methods, such as snowball and targeted sampling, because of the controls on volunteerism and masking imposed on the recruitment process. In the current practice of RDS sample analysis, sample recruitment patterns are used as the estimates of personal network compositions that are used for population proportions estimation. The precision of replacing network compositions with sample recruitment patterns relies on the assumption of no significant effects of volunteerism and masking. Although this assumption serves as the basis of RDS analysis, testing of this assumption has been challenging. In this study, the authors will present a SAS macro program to conduct such a test in RDS sample analysis, using bootstrap method. Real data will be used for demonstration.

Learning Objectives: At the conclusion of the presentation, participants (learners) will know

• The basic concepts of RDS sample analysis.
• Why and how the effects of volunteerism and masking in RDS are tested.
• Resources available to assist in the use of SAS macro that is designed to test the effects of volunteerism and masking in RDS.

Keywords: Survey, Statistics

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

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.

• Slides (pdf) or Handout