Frequently Asked Questions
Q. How do I set up a research site?- This Powerpoint Presentation will show you step by step.Click HERE to download.
Q. How many users are allowed per research site?
- A maximum of 15 users are allowed per site per licence?
Please enquire if you would like to purchase access for additional users.
Q. What is RDS and Bryant Research Systems?
- 'Respondent Driven Sampling was developed by Douglas Heckathorn at Cornell University in 1997, as part of a National Institute on Drug Abuse-funded HIV-prevention research project targeting drug injectors in several Connecticut cities. RDS served as the recruitment mechanism for an intervention design developed with Robert Broadhead termed 'peer-driven intervention''.
Since 2008 Bryant Research Systems has worked to develop tools to simplify the setting up of respondent driven sampling (RDS) research sites to ensure integrity of the data captured. The system is web based and provides the ability for a researcher to manage the data and performance of the research site online. All data collected is RDSAT compatible.
We have created a unique automated serial numbering system. The system helps to simplify eligibility screening of participants and links a social network via the serial number. The system prints the invitation coupons with a serial number and allows the number to be scanned into the data base to capture the required data for your research.
Q. What are the Data Requrements for RDS Analysis
- While the data requirements for RDS analysis are minimal, there are three fields which are essential for analysis (NOTE: RDS analysis CAN NOT BE PREFORMED without these fields for each respondent):
Personal Network Size (Degree) - Number of people the respondent knows within the target population.
Respondent's Serial Number - Serial number of the coupon the respondent was recruited with.
Respondent's Recruiting Serial Numbers - Serial numbers from the coupons the respondent is given to recruit others.
Q. What is unique about the system that you have created?
- The system has an eligibility questionnaire that captures the respondents network size.
The serial number is automatically generated for each respondent and linked to the person who invited the respondent.
The respondents Recruiting Serial Numbers that are issued are automatically captured into the data base.
We have eliminated human error through automatically generating the coupons. No more copying the number onto hardcopies or research materials.
All data captured is RDSAT compatible. Just copy and paste.
Q. What are the requirements to operate the Bryant System?
- System Requirements
•Desktop or Laptop PC
•Windows XP/7 OS
•Internet Explorer 8(with Javascript enabled)
•Internet connection
•Windows compatible barcode scanner
•Zebra Barcode printer (See FAQ below for full list)
•Zebra Universal Driver v5.5.7.25 (Click to download)
•OPOS Driver v1.11.4.6 (Click to download)
•DotNet Framework from Microsoft (Click to download the Web Installer)
Also see Driver Downloads on the Products Page to get Zebra printers up and running.
(Check with us for other compatible printers or make a request for a custom system)
Q. What are some of the advantages of Bryants web-based system.
- The software is always up to date. No need to download and install updates on a regular basis.
Your data is safe with Bryant. The data sits in a secure database.
Other stand-alone systems have a security risk in that a site PC may be stolen or get damaged along with all data on it.
Q. Which Zebra printers are supported by the Bryant System?
- Supported Printer Models: 105SL, 110PAX4, 170PAX4, GK420d, GK420t, GX420d, GX420t, GX430t, KR403, LP 2844-Z, MZ 220, MZ 320, P4T, QL 220 Plus, QL 320 Plus, QL 420 Plus, RW 220, RW 420, S4M, TLP 2844-Z, TLP 3844-Z, ZM400, ZM600, 110XiIIIPlus, 140XiIIIPlus, 170XiIIIPlus, 220XiIIIPlus, LP 2824-Z, TLP 2824-Z
(Zebra Universal Driver v5.5.7.25 and OPOS Driver v1.11.4.6 and Dot Net Framework Reqiuired to run these printers on the Bryant System)
Q. What tips do you have for best operating the BRYANT system?
- It is best to decide on your project settings and create your final questionnaires before embarking on capturing data.
We recommend testing the system with your settings and questionnaires before running actual site data through it. Once you are satisfied that all data is correct and your chosen settings give the desired results, then you can embark on a study. Your 'test' data can be erased through the administration menu and you can start with a clean database.
Changing certain project settings and questionnaires after a study has started may have undesirable effects on the data due to the complexity of the system, so its best to try not to make any late changes.
If you have any doubts, please ask us. You can send a message to TechSupport on the Bryant messaging system.
Q. My printer skips labels.
- The printer needs to be calibrated to the the label. Press the green button on top of printer down and hold until the green light flashes. Release and allow the printer to calibrate it self. Return to test printer and print test label.
Q. The Bryant system is serving us well but there are other features that we need. How do we get these features incorporated into Bryant?
- We welcome suggestions for new features. Please e-mail us with your proposal and we will contact you to discuss the finer details. All viable proposals will be added to the system.
Q. If we wanted to integrate this with a laboratory so that they could also keep track of the specimens at the lab and report the results back to the staff at the RDS site (many studies do not do rapid testing, but rather have participants return in 2 weeks
- Yes this is possible. The Lab Is given an access code to your study site. They can only access the Medical Results page where they will input the data for each participant. They will scan in the participant barcode from their specimen and input the relevant data. Each participants results will be made available via a PDF form. The participants personal info will need to be verified via a check list before the results are given.
Q. What would be needed at the labs since the IDs would be applied at the RDS site, and then the lab would need to read them and put them into a database?
- Most Labs have barcode scanners. The Labs will allocate their own barcode to a specimen if it has not been allocated a barcode. As our system issues each clients ID as a barcode they will simply scan the allocated barcode. If they have been given access to upload the data to a study site all they would do is login to the site and upload each participants results.
Q. We are planning on doing RDS in 6 sites so does that mean we need to buy the scanner and printers for each site if we want to use this method plus the program database?
- Each site does require a printer. The number of scanners require depends on how many computers are at each site. This speeds up the process if each computer is linked into the system. This would track each participant through the reseach site.
Q. Are there other ways to notify participants?
- The BRYANT System can issue participant e-invites by emailing a link back to the Study Site. The participant can then down load a PDF version of the invite. These e-invites are barcoded and can be scanned into the system. The participants can also be recalled to a study site via email if they provide an email address or via a SMS if they provide a mobile phone contact number.
Q. What are QR barcodes and how are they useful?
- QR barcodes are 2 dimensional compact barcodes that can hold a lot of data. They are beneficial in that they can be scanned by most camera cellphones which then links directly to our website where you can log in and process a participant. Here is an example.
Q. How does BRYANT Research Systems handle and manage their clients data.
- Data Handling & Management
BRYANT Research Systems manages their clients data with confidence and confidentiality assured. We ensure our clients do not capture personal identifiers from their informants or study participants.
BRYANT Research Systems has SSL123 certificates. SSL Certificate provides confidential information exchanged during secure sign-in and self-service interactions with up to 256-bit encryption for intranets, mail servers, and other web-based applications that are not at risk for phishing or fraud. All sensitive data is encoded for each client.
BRYANT system is used to create a unique id number and to simplify eligibility screening of participants and to link the participant social network via the barcode serial number. The System prints recruitment coupons with the serial number and allows number to be scanned into data base to capture required data.
BRYANT Research Systems manages all data via a web-based management system. All data is uploaded to a secure off site data base immediately. Data is backed up every 4 hours.
Each user needs to logon with a user name, password and site code. Access is limited to set functions allocated by Site Manager. No Critical Data is accessible to site staff. All logon data is monitored by the System. All logon attempts or invalid password used are monitored by the system and reported.
Each participant is linked to the study via a unique study barcode number. The System will verify each barcode. Participants will also create a Unique ID number via a personal information questionnaire. No data will contain personal identifiers. Each participant’s recruitment coupon has a barcode printed that will automatically link their social network.
Blood test results and questionnaire datasets is merged using participant unique study barcode number. No data will contain personal identifiers.
Interview questionnaire data is entered directly onto the secure off site data base during the interview. Interview data is uploaded by the researchers with a user name, password and site code directly from the secure off site data base for analysis. All data captured is RDSAT compatible.
Researchers and PI have access to statistical data with a user name, password and site code. They are able to monitor the research site remotely and observe site statistics.
Q. Javascript: How do I enable this on my browser?
- Windows Internet Explorer
(all versions except Pocket Internet Explorer)
Note To allow scripting on this Web site only, and to leave scripting disabled in the Internet zone, add this Web site to the Trusted sites zone.
1. On the Tools menu, click Internet Options, and then click the Security tab.
2. Click the Internet zone.
3. Click Custom Level.
4. In the Security Settings – Internet Zone dialog box, click Enable for Active Scripting in the Scripting section.
Click the Back button to return to the previous page, and then click the Refresh button to run scripts.
Firefox
1.Open the “Tools” menu.
2.Select “Options…”, to open the Options dialog box.
3.In the row of coloured icons at the top, click “Content”.
4.Click the check box to the left of “Enable JavaScript” so that a tick appears. (Click again to remove the tick if you want to disable JavaScript.)
5.Click “OK” to close the Options dialog box.
If you are using Mac OS X, then you need to select Preferences… on the Firefox menu, instead of Options… on the Tools menu.
Opera Software’s Opera version 9,
On the Tools menu, click Preferences.
On the Advanced tab, click Content.
Click to select the Enable JavaScript check box, and then click OK.
Click the Back button to return to the previous page, and then click the Reload button to run scripts.
Netscape 4.x or 6.x (Windows or Mac)
1.Open the Edit menu.
2.Click Preferences…
3.In the Category list, select Advanced.
4.To the right of the list, select “Enable JavaScript” to add or remove the tick.
Google Chrome
1.Click the spanner icon near the top right to open the menu.
2.Select “Options”, to open the Google Chrome Options dialog box.
3.In the Google Chrome Options dialog box, click the “Under the Bonnet” tab.
4.In the Privacy section, click the “Content settings…” button.
5.In the Content Settings dialog box, click “JavaScript” in the list of Features.
6.Click the “Allow all sites to run JavaScript (recommended)” radio button
Safari
1.Open the Safari menu (OS X), or the “Edit” menu (Windows). (If the menu is not visible, press the Alt key.)
2.Select “Preferences…”
3.In the Preferences dialog box, click the Security icon (a padlock).
4.In the Web Content section, click the check box next to “Enable JavaScript” so that a tick appears. (Click again to remove the tick if you want to disable JavaScript.)
5.Close the Preferences dialog box by clicking the red button at the top left (OS X) or by clicking the X icon at the top right (Windows).
Q. How many coupons are required for a respondent driven sampling study?
- In the last three respondent driven sampling studies I have been involved in, 60% of the coupons issued were not returned to site.
If you are looking at a sample size of 900 participants you would require about 2700 pre printed coupons. So to be on the safe side I would recommend 3000 coupons.
The simplest way was to pre print the coupons and leave a blank space to affix a bar coded sticker.
We use a business size card that can fit easily into a wallet.
Q. What is respondent driven sampling?
- Respondent-driven sampling (RDS), combines 'snowball sampling' (getting individuals to refer those they know, these individuals in turn refer those they know and so on) with a mathematical model that weights the sample to compensate for the fact that the sample was collected in a non-random way.
RDS represents an advance in sampling methodology because it resolves what had previously been an intractable dilemma, a dilemma that is especially severe when sampling hard-to-reach groups, that is, groups that are small relative to the general population, and for which no exhaustive list of population members is available. This includes groups relevant to public health, such as drug injectors, prostitutes, and gay men, groups relevant to public policy such as street youth and the homeless, and groups relevant to arts and culture such as jazz musicians and other performance and expressive artists.
The dilemma is that if a study focuses only on the most accessible part of the target population, standard probability sampling methods can be used but coverage of the target population is limited. For example, drug injectors can be sampled from needle exchanges and from the streets on which drugs are sold, but this approach misses many women, youth, and those who only recently started injecting. Therefore, a statistically representative sample is drawn of an unrepresentative part of the target population, so conclusions cannot be validly made about the entirety of the target population.
This same fundamental problem faced pollsters during the recent presidential election. Phone based polls were not able to access voters who had abandoned land-based phones in favor of cell and Internet phones or voters who merely refused to be interviewed. What little was known about these inaccessible voters showed that they were not the same as other voters, for example, they tended to be younger. However, whether they differed in political attitudes was not known, so pollsters had no way of knowing how to adjust their estimates to compensate for those they had missed. Similarly, public health researchers have had no reliable way to determine how those they could access through location-based sampling differed from those who were inaccessible.
The other horn of the dilemma arises if priority is placed on coverage rather than statistical validity. Network-based methods can provide comprehensive coverage of the target population. They start with a set of initial respondents, who refer their peers; these in turn refer their peers, and so on, as the sample expands from wave to wave. Based on the principle of “six degrees of separation,“ this approach could potentially reach any member of a population in only six waves, so total coverage is possible, at least theoretically. However, this approach is prey to a host of biases. For example, most people recruit those whom they resemble in race, ethnicity, education, income, and religion. Well-connected individuals tend to be over-sampled because many recruitment paths lead to them, so the peer recruitment upon which network-based sampling is based is anything but random.
Due to this dilemma, researchers had to choose between a statistically valid sample of the most accessible part of the target population, and a statistically invalid sample of broader coverage. This dilemma was widely assumed to be impossible to resolve. Standard statistical sampling texts describe network-based sampling methods, also known as chain-referral and snowball sampling, as afflicted by biases of unknown size and unknown direction, so any inferences made based upon data from such a sample would be nothing more than mere 'subjective evaluation.'
The situation changed with the advent of respondent-driven sampling (RDS), a sampling method that overcomes this dilemma by showing that the breadth of coverage of network-based methods can be combined with the statistical validity of standard probability sampling methods. This makes it possible for the first time to draw statistically valid samples of previously unreachable groups. In essence, respondents recruit their peers, as in network-based samples, and researchers keep track of who recruited whom and their numbers of social contacts. A mathematical model of the recruitment process then weights the sample to compensate for non-random recruitment patterns. This model is based on a synthesis and extension of two areas of mathematics, Markov chain theory and biased network theory, which were not a part of the standard tool kit of mathematical sampling theory. The resulting statistical theory, termed RDS, enables researchers to provide both unbiased population estimates and measures of the precision of those estimates. This extends the realm within which statistically valid samples can be drawn, to include many groups of importance to public health, public policy, and arts and culture.
RDS was developed by Douglas Heckathorn less than a decade ago, in 1997, as part of a National Institute on Drug Abuse-funded HIV-prevention research project targeting drug injectors in several Connecticut cities. RDS served as the recruitment mechanism for an intervention design developed with Robert Broadhead termed 'peer-driven intervention' (PDI).
RDS was further elaborated, in 2002 as part of a CDC-funded project focusing on young injectors to include means for calculating confidence intervals and weighting the sample to control for differences in network size and clustering across groups.
An article appearing in the journal, Sociological Methodology, offers a further important advance in the sampling method. It shows, using both analytic methods and simulations, that when applied in a way that fits the statistical theory on which RDS is based, it produces estimates that are 'asymptotically unbiased.' This means that bias is only on the order of one divided by the sample size, so the sampling method is unbiased for samples of meaningful size. It also improves the means for controlling for the effects of differences in network sizes. This article was co-authored by Heckathorn with Matthew Salganik when he was a Cornell sociology graduate student in the university's National Science Foundation-funded IGERT (Integrative Graduate Education and Research Trainee) program. Salganik is now in the Columbia University sociology department.
RDS has been applied to study a variety of populations. In collaboration with Joan Jeffri, Director of Columbia University's Center for the Study of Arts and Culture it was employed to study of jazz musicians in New York City, San Francisco, New Orleans and Detroit. Funded by the National Endowment for the Arts, this was the first application of this method to the study of arts and culture. Heckathorn and Jeffri are now conducting studies on both aging artists in the New York City metropolitan area and the national network of professional and semi-professional storytellers.
RDS has also been used by the CDC’s Global AIDS Program to study injection drug users (IDUs) in Bangkok and IDUs and prostitutes in Vietnam, and it has been used by Family Health International, the largest non-profit agency in international public health, in more than a dozen countries, including Bangladesh, Burma, Cambodia, Egypt, Honduras, India, Kosovo, Mexico, Nepal, Vietnam, Pakistan, Papua New Guinea and Russia to study gay men, IDUs and prostitutes. Consequently, though less than a decade old, it has been more than fifteen countries.
Q. What are the response rates to respondent driven sampling?
- With the Bryant System we have found that 1/3 of all coupons issued are retuned to site.
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