Blog Archives

[RDS] UCSF Surveillance Training: Methods for Sampling Key Populations – September 22-26, 2014 – Registration Open

The University of California, San Francisco (UCSF) Global Health Sciences is offering a course for implementing integrated bio-behavioral surveillance surveys among key populations using respondent driven sampling and time location sampling and integrating population size estimation. This course will be based on the recently released IBBS Toolbox available on line at This one week course will be at UCSF Global Health Sciences located in downtown San Francisco September 22-26, 2014 and will cover:

• Overview of HIV Surveillance and Sampling Methods

• Formative Assessment for IBBS

• Questionnaire Design

• Respondent Driven Sampling Nuts & Bolts

• Population Size Estimation

• Time Location Sampling Nuts & Bolts

• Using Results

Participants will leave with a CD or flash drive of the Toolbox.

The cost of the course is $1,200. To register, please use this registration link:

Register here


Toolbox for conducting integrated HIV bio-behavioral surveillance (IBBS) in key populations

Improving health and reducing inequities worldwide

This toolbox is for governments, nongovernmental organizations, and private researchers who wish to implement IBBS surveys for key populations at higher risk for HIV infection. The toolbox features two methods

  • Time location sampling (TLS)
  • Respondent driven sampling (RDS)

These methods focus on three key populations at higher risk for HIV infection

  • Female sex workers (FSW)
  • Men who have sex with men (MSM)
  • Persons who inject drugs (PWID)

However, these tools are easily modifiable for other sampling designs as appropriate. This tool box is complete with everything you would need to implement a survey. These documents are based on our years of experience implementing IBBS all over the world, including San Francisco, CA, USA; eight countries in Africa; Brazil; China and the Caribbean.

Posted by BRYANT Research Systems

[RDS] RDSAT 7.1 available with significant new features and enhancements.

We are pleased to announce a new version of RDSAT with new data import tools, table builder and batch processing features. The older RDSAT interface is still shown by default, but you can access the batch mode features by clicking the “Batch Mode” tab below the menu bar.

With the new Batch Mode, you can:
– Import data from delimited text files with column name header (preferred) or SAS xprt (no more manually crafting RDS headers)!
– Define the same set of analyses to a set of data files (for multi-site studies).
– Save the results of analyses to Excel or CSV files.
– Quickly define groups for prevalence calculations.
– Specify a table structure and let RDSAT automatically populate the table.
– Create and save analysis specifications, including data file names, analysis options and output options to an .xml file.
– Document the analysis procedures with the human-readable .xml file.
– Reload and run the analysis (.xml) file to reproduce an analysis.

We tried to design an interface that was more efficient for common RDS analysis tasks. We welcome feedback about the new design, especially concerns regarding usability issues or bugs.

The RDSAT 7.1 software and manual is available from the downloads section at


Respondent-driven Sampling

Respondent-driven Sampling
written by Paul Johnson,

So I was excited for the AAPOR webinar on hard-to-reach populations because I really feel like this is the hardest nut to crack in the industry. Unfortunately, I left being underwhelmed probably because of a misalignment of expectations. I came in thinking that hard-to-reach is the same as hard-to-sample so I was expecting the webinar to focus on the hard-to-sample challenges. I am grateful to Dr. Tourangeau for helping me broaden my horizon. As an employee of a sampling company, sometimes I get too focused on the hard-to-sample problem and not enough on the big picture. Still, for this post I want to focus on the hard-to-sample population and open a debate on whether or not respondent-driven sampling can actually produce good estimates that can help a company make informed decisions.

read more

Respondent-Driven Sampling and Time-Location Sampling: A Comparison of Implementation and Operational Challenges for HIV Behavioral Research

Respondent-Driven Sampling and Time-Location Sampling: A Comparison of Implementation and Operational Challenges for HIV Behavioral Research

Marissa Hall, University of North Carolina at Chapel Hill
Clare Barrington, University of North Carolina at Chapel Hill
Sanny Y. Chen, Centers for Disease Control and Prevention (CDC)
Nelson Arambu, Universidad del Valle de Guatemala
Sonia Morales, Universidad del Valle de Guatemala
William M. Miller, University of North Carolina at Chapel Hill
Berta Alvarez, Universidad del Valle de Guatemala
Gabriela Paz-Bailey, Universidad del Valle de Guatemala

Respondent-driven sampling (RDS) and time-location sampling (TLS) are used to recruit men who have sex with men (MSM) for HIV behavioral research. Two cross-sectional surveys, one using RDS and the other TLS, were conducted simultaneously among MSM in Guatemala City in 2010. The purpose of this study is to analyze the strengths and challenges associated with implementing each method based on data obtained from key informant interviews (n=10) and one focus group with field staff. Both RDS and TLS successfully and efficiently recruited the target sample size. RDS offered greater privacy and safety, required fewer human and financial resources, and presented fewer logistical challenges. TLS led to a greater understanding of the context in which MSM socialize and meet sex partners, providing important information for prevention efforts and data interpretation. We conclude with concrete recommendations for improving RDS and TLS implementation

See paper


The Respondent Driven Sampling (RDS) Simulator is designed to aid researchers with basic questions of sample design.

For latest version, please check out by subversion.

The Respondent Driven Sampling (RDS) Simulator is designed to aid researchers with basic questions of sample design
• What is the optimal number of seeds?
• What is the optimal number of coupons to give each responent?
• What is the probability of the recruitment chain dying out?
• How long is will it take for the sample to be collected?
• What is the probability of the sample completing before the desired finish date?

Unfortunately, there are no conventional mathematics to answer these questions. RDS Sim. overcomes this hurdle by simulating numerous recruitment chains in continuous time. This allows you to investigate the ramifications of changing sample design on the timing and logistics of your recruitment chain.
There is no documentation, but RDS Sim. has been designed to be extremely easy to use. Contact the author with any questions, feature requests or bug-reports.
The first version (0.01) is now available in a Windows installer. If you use Mac or Linux (good for you), you can download the source-code and run it with a python interpreter. RDS Simulator is free, open-source, and licensed under the GPL.

The windows version has been tested on XP, but depends on several DLLs. If you have any problems due to failed dependencies, contact the author.
The source-code version is platform-independent, but requires python and depends on several libraries: Matplotlib and scipy. Future versions will likely require networkx.

to download rdssimulator