The Hard-to-Reach Population Methods Research Group

RDS network

The Hard-to-Reach Population Methods Research Group (HPMRG) focuses on developing statistical methodology to help improve understanding of hard-to-reach or otherwise “hidden” populations.

These populations are characterized by the difficulty in survey sampling from them using standard probability methods. Typically, a sampling frame for the target population is not available, and its members are rare or stigmatized in the larger population so that it is prohibitively expensive to contact them through the available frames. Examples of such populations in a behavioral and social setting include injection drug users, men who have sex with men, and female sex workers. Examples in an economic setting include unregulated workers and the self-employed. Hard-to-reach populations in the US and elsewhere are under-served by current sampling methodologies mainly due to the lack of practical alternatives to address these methodological difficulties.

See the menu bar at the top for inormation about RDS Analyst software

About our members

The Hard-to-Reach Population Methods Research Group is a collaborative interdisciplinary group of researchers from several universities:

Dr. Mark S. Handcock  is Professor of Statistics in the Department of Statistics at the University of California – Los Angeles. His research involves methodological development, and is based largely on motivation from questions in the social and epidemiological sciences. He has published extensively on survey sampling, network inference, and network sampling methods. For details see his web page.

Dr. Katherine R. McLaughlin  is Assistant Professor of Statistics in the Department of Statistics at the Oregon State University. She works in many areas of statistics, often with application to social demography and global health. She is an expert in social network analysis, network sampling, survey sampling and social statistics. Her dissertation work focuses on modeling preferential recruitment for RDS and Peer-Driven Interventions. For details see her web page.

Dr. Krista J. Gile is Professor of Statistics in the Department of Mathematics and Statistics at the University of Massachusetts - Amherst. Her research focuses on developing statistical methodology for social and behavioral science research, particularly related to making inference from partially-observed social network structures. Most of her current work is focused on understanding the strengths and limitations of data sampled with link-tracing designs such as snowball sampling, contact tracing, and respondent-driven sampling. In particular, her dissertation and recent work focus on understanding the implications of assumptions of current respondent-driven sampling (RDS) methodology, and on introducing improved estimation strategies for RDS data. For details see her web page.

Dr. Lisa G. Johnston is an Epidemiologist-Independent Consultant providing technical assistance for over 15 years to international organizations, Universities, and institutions worldwide to conduct surveys and population size estimation techniques among hard-to-reach populations. Most of her work has focused on conducting HIV bio-behavioral surveillance surveys and size estimations using RDS among men who have sex with men, sex workers and people who inject drugs, high risk adolescents and youth and migrants. She has conducted and analyzed data from over 400 RDS surveys in over 40 countries and has published over 45 peer reviewed journal articles about RDS. She has authored several book chapters and developed manuals and guidance on implementing surveys and analyzing data using RDS. For details see her web page.

Dr. Ian E. Fellows is a professional statistician based out of the University of California, Los Angeles. His research interests range over many sub-disciplines of statistics, with his dissertation work focusing on new methods in the analysis of social network sampling designs (such as RDS). He has designed statistical user interfaces for both academic and corporate clients, and in 2011 one of his designs won the prestigious John Chambers Award. He is the primary author of RDS Analyst’s graphical user interface. or details see his web page.

Dr. Cori M. Mar is the former Director of the Statistics Core at the Center for Studies in Demography and Ecology (CSDE) at the University of Washington. Her duties included providing training in statistical methods, data analysis techniques, and statistical programming. Dr. Mar has taught R in a variety of formats from a 2-3 hour one class introduction to one hour a week through a 10 week course. Dr. Mar has extensive experience as a translator between statisticians and the applied researchers. For details see the web page.

About the RDS Analyst software

Introduction

RDS Analyst (‘RDS-A’) is a software package for the analysis of Respondent-driven sampling (RDS) data that implements recent advances in statistical methods.

RDS Analyst has an easy-to-use graphical user interface to the powerful and sophisticated capabilities of the computer package R. RDS Analyst provides a comprehensive framework for working with RDS data, including tools for sample and population estimations, testing, confidence intervals and sensitivity analysis.

Example capabilities are an easy format for entering data, the visualization of recruitment chains, regression modeling, and missing data.

The interface of RDS Analyst is similar to SPSS. RDS Analyst is also a free, easy to use, alternative to proprietary data analysis software such as SPSS, STATA, SAS/ JMP, and Minitab. It has a menu system to do common data manipulation and analysis tasks, and an Excel-like spreadsheet in which to view and edit data.

RDS Analyst is meant for users who want to use state-of-the-art techniques for estimation and quantification of uncertainty from data collected via RDS. It represents advanced, comprehensive and open-source software to visualize, model and conduct sensitivity analyzes for RDS data.

RDS Analyst is an intuitive, cross-platform graphical data analysis system for the analysis of RDS data. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an Excel-like spreadsheet for easy data frame visualization and editing. It is also the front-end to the very powerful capabilities accessible via the R command-line interface and also the extensive capabilities of the R statistical language.

Basic facts

  • RDS Analyst is written for the R statistical environment.
  • The current development form is for Windows and Macintosh. A LINUX version will be available in installers when it is released publicly.

Installation on an Windows PC

The installer is here.

Download the installer and double-click on it to install the software.

This can install all programs and utilities needed. If you already have some elements installed you can deselect (or cancel) during the installs. It is recommended that you install this all the first time. This installer is almost 400Mb in size and will take time to download.

A reboot is not required. You do not need to uninstall any components to update (This includes R and Java). However the RDS Analyst application or the R application must not be running when you update.

This is a new portable version that does not need the Java Runtime Environment to use.

Note for experienced users: This creates a private version of R for RDS Analyst to use and ensures RDS Analyst has the right version of R available for its use. If you already have R installed separately, the two versions will peacefully coexist and you can use the other version of R just as you were originally.

Finally, be sure to sign up for the RDS Analyst Users Group

Installation on an Apple Macintosh

There is a version for Apple Macintosh computers.

The installer is here

Download the installer and double-click on it to install the software.

This can install all programs and utilities needed. If you already have some elements installed you can deselect (or cancel) during the installs. It is recommended that you install this all the first time. This installer is almost 400Mb in size and will take time to download.

A reboot is not required. You do not need to uninstall any components to update (This includes R and Java). However the RDS Analyst application or the R application must not be running when you update.

The RDS Analyst application and R will be in your Applications folder. To run RDS Analyst, double-click on it in the Applications folder.

This is a new portable version that does not need the Java Runtime Environment to use.

Note on Xcode and gfortran: Some Macs need the application Xcode and a version of Fortran to be installed. Xcode is software written by Apple to help people develop software on the Mac, including compiling some R packages from source code. In some cases, RDS Analyst will not run unless it is installed.

Here are the steps:

  • Install the Xcode application from the Mac App Store
  • Open the installed Xcode app, agree to the license, and let it install some “components”
  • Open a terminal window by opening the Terminal application (Applications > Utilities > Terminal). Type xcode-select –install and follow the dialogs that open. This installs some tools for making R packages from source.
  • Install gfortran by downloading the installer at: https://cran.r-project.org/bin/macosx/tools/gfortran-4.2.3.pkg The file will be an Apple style installer (.pkg) which you will need to open to install gfortran on your computer. It should install by default in /usr/local/bin/gfortran.

Note for experienced users: This creates a private version of R for RDS Analyst to use and ensures RDS Analyst has the right version of R available for its use. If you already have R installed separately, the two versions will peacefully coexist and you can use the other version of R just as you were originally.

Finally, be sure to sign up for the RDS Analyst Users Group.

Getting started - The Manual

For the manual, go to the RDS Analyst Manual

RDS Analyst citation information

To cite RDS Analyst, go to citation.

RDS Analyst Discussion and Help Forum

The best way to contact us with questions, comments or suggestions is through the RDS Analyst users group.

To post and receive messages from this forum, you need to join. To subscribe, go to rdsanalyst_help.

You can use the forum to:

  • get help from the Hard-to-Reach Population Methods Research Group and other users

  • post questions, comments and ideas to other users

  • be informed about RDS Analyst updates

  • learn about bugs (and bug fixes)

  • If you find bugs, please report them and use “Bug:” in the subject line.

  • For software feature requests, use “Feature Request:” in the subject line.

Once you have joined the list, you can post your questions and comments to rdsanalyst_help@googlegroups.com.

A full list of all messages posted to this list is available here.

Enjoy!

Citation

RDS Analyst citation information

If you are using RDS Analyst for research that will be published, we request that you acknowledge this with the following citation:

Mark S. Handcock, Ian E. Fellows, Krista J. Gile (2022) RDS Analyst: Software for the Analysis of Respondent-Driven Sampling Data, Version 0.72, URL https://hpmrg.org.

A BibTeX entry for LaTeX users is

+@Manual{RDSA,
+  title = {RDS Analyst: Software for the Analysis of Respondent-Driven Sampling Data},
+  author = {Mark S. Handcock and Ian E. Fellows and Krista J. Gile},
+  year = {2022},
+  note = {Version 0.72},
+  address = {Los Angeles, CA},
+  url = {https://hpmrg.org},
+}@@

We have invested a lot of time and effort in creating RDS Analyst for use by other researchers. Please cite it in all papers where it is used.

Citing the component packages of RDS Analyst (e.g., RDS or DeducerRDSAnalyst)

To cite the individual R packages (e.g., RDS or DeducerRDSAnalyst) please use the information given by their individual citation function calls. For example, at the R Console window type:

citation(package="RDS")

Citing Deducer

RDS Analyst is built on the graphical user interface and capabilities of Deducer. To cite Deducer, see here.

Contact

  • handcock@stat.ucla.edu
  • UCLA, Department of Statistics, 8125 Mathematical Sciences Building, Los Angeles, CA 90095-1554
  • Mathematical Sciences Building – Room 8105C