Sampling Hard-to-Reach Populations 1 |
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| Session Organisers |
Professor Ulrich Kohler (University of Potsdam) Professor Lena Hipp (WZB Berlin Social Science Center/University of Potsdam) Mr Dimitri Prandner (Johannes Kepler University of Linz, Austria) Professor Martin Weichbold (University of Salzburg, Austria) |
| Time | Friday 19th July, 09:00 - 10:30 |
| Room | D09 |
Public interest in learning more about demographic groups that are either small, hidden, mobile, or engaged in illicit behaviors has grown in recent years. Prominent examples of these “hard-to-reach” populations are drug addicts, homeless people, or prostitutes as well as very rich people and migrants, particularly those who are not documented or who travel a lot. None of these demographic groups can be adequately sampled with probability surveys, either because of the absence of sampling frames, the small size of these groups compared to the total population, their unstable residency, or their reluctance to participate.
In order to learn more about these hard-to-reach populations, researchers have employed a few different sampling methods, including network sampling, link-tracing designs (aka snowball sampling), and respondent-driven sampling. Although the use of such nonprobability sampling methods to survey hard-to-reach groups has rapidly expanded and has been employed in many different contexts (though particularly in developing countries), numerous questions regarding both the implementation of these surveys and the analyses of the collected data have not yet been (fully) resolved.
- How can the target population be adequately delineated and identified in the sampling process?
- How should researchers choose incentives and interview locations when surveying hard-to-reach populations? What are best practices in seed selection?
- What do we know about mode differences when surveying hard-to-reach populations and asking individuals about illicit behaviors?
- What challenges occur when employing nonprobability sampling in comparative studies, for example with regard to the number of initial seeds and assumptions regarding the referral
- What are the best estimators when analyzing data collected from non-probability samples?
- How can we best calculate the variability of the estimates from non-probability samples?
- What are the ethical issues when surveying hard-to-reach populations and how can they be resolved in an acceptable way for researchers, respondents, and funding agencies?
- How can nonprobability surveys be combined with other methodological approaches to assess the accuracy of their findings?
Keywords: hard-to-reach/hidden populations, illicit/stigmatized behaviors, nonprobability sampling
Professor Rainer Schnell (University of Duisburg-Essen) - Presenting Author
Mr Christian Borgs (University of Duisburg-Essen)
Rare populations are a standard problem in applied sampling. The demand for information on such populations is increasing in all western societies. The most often surveyed rare populations are migrants.
For many countries, sampling frames will not contain information on migration status. Therefore, survey sampling designs targeting migrants will require special techniques. Most available techniques rely on unique characteristics such as ethnic names or the use of ethnic infrastructures. One approach based on spatial clustering is adaptive cluster sampling.
Although widely applied in biology, adaptive cluster sampling has rarely been used for human populations. If the spatial distribution of the population is clustered, this clustering can be used for sampling. For human populations, most often official statistics are used for identifying such clusters. In the US, two studies used spatial clustering of subpopulations within landline phone number prefixes for sampling rare populations.
To test this approach for other populations, we tested adaptive cluster sampling of rare populations with RDD prefixes as PSU in Germany
using respondents with Turkish or Russian ethnic background as target population.
We applied the TCS approach by drawing a nation-wide simple random sample of phone numbers. If a selected phone number resulted in a contact to a member of the target population, the prefix of that number is used as PSU for further selections. Within a PSU number block, all possible phone numbers are generated. Screening within in each PSU continues until a specified number of eligible households was reached.
We compare TCS with name-based sampling and large general population surveys. As expected, TCS yields higher screening rates. Unexpectedly, we observed an increased refusal rate. However, the effort required for implementing this technique may limit its practical use.
Dr Markus Grabka (DIW Berlin / SOEP)
Professor Carsten Schroeder (DIW Berlin / SOEP) - Presenting Author
Professor Martin Kroh (DIW Berlin / SOEP)
Dr Charlotte Bartels (DIW Berlin / SOEP)
Mr Rainer Siegers (DIW Berlin / SOEP)
High-worth individuals are typically underrepresented or completely missing in population surveys. The lack of a register-based sampling frame on high-worth individuals in many countries challenged previous attempts to sample high-worth individuals in voluntary scientific surveys. In order to reach the hard-to-reach population of wealthy households, we have developed a novel sampling strategy. This strategy draws on register data on the shareholding structures of companies, and builds on the empirical regularity that high-worth individuals are likely to hold at least part of their assets in the form of shareholdings. Based on data from over 270 million companies worldwide, in a feasibility study for Germany we identified individuals who are both German residents and registered shareholders of companies. In this study, we interviewed 124 households using the SOEP-standard person questionnaire. Our analysis shows that register data on shareholding structures correctly identifies the individuals’ rank in the wealth distribution, that the quality of personal information, particularly the residential address, is sufficiently high for subsequent interviewing, and that the proposed sampling strategy can fill a major data and research gap in the study of high-worth individuals. Currently we are implementing a large-scale study with 2,000 households based on this tested sampling strategy. We will show results from the feasibility study and preliminary findings from the large-scale study.
Dr Ksenia Eritsyan (National Research University Higher School of Economics) - Presenting Author
Mrs Alexandra Lyubimova (The Sociological Institute of the RAS – Branch of the Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences, NGO )
Ms Ksenia Babikhina (Treatment Preparedness Coalition in East Europe and Central Asia)
During last 15 years numerous studies among populations vulnerable to HIV were conducted in Russia within the framework of integrated bio-behavioural HIV surveillance (IBBS). The target populations for those studies are usually hard-to-reach and include people who inject drugs, sex-workers, men who have sex with men and their sexual partners. The sampling frames used usually included the respondent-driven sample (RDS), time-location sample (TLS) or some modification of those. However methodological challenges regarding implementation of those studies haven’t been previously summarized and discussed.
We reanalyzed the field notes from studies we’ve coordinated (n=8) as well as the conducted a series of explorative semi-structured interviews with experts (n=20) who were involved in IBBS studies in different regions Russia on different levels (from principal investigators to interviewers). Short or absent formative research step coupled with rigid field data collection plan based on non-relevant international experience was one of the main issues discussed. It resulted in wrong choices of sample frame, data collection sites, incentives, or field staff. The RDS claimed to be often considered as a magic bullet to sampling hard-to reach populations which led to ineffective recruitment in cases where social networks between population members were underdeveloped or when they connected mostly online. RDS was also found to be very vulnerable to “leaders” who may interfere in coupon distribution. The TLS used for sex-workers tend to be flawed towards the places known and accessible to researchers.
The amount of incentives raised both ethical and methodological questions where low amount lead to ineffective recruitment and high – to undue inducement and cheating, moreover the same amount of incentives might be considered as too low or high depending on the study site. The strategies to overcome those methodological challenges are discussed.
Dr Jacob Steinwede (infas Germany)
Dr Reiner Gilberg (infas Germany) - Presenting Author
Sampling of disabled people in Germany
Jacob Steinwede and Reiner Gilberg
The infas Institute for Applied Social Sciences is carrying out the comprehensive "representative survey on the participation of persons with disabilities"– being the first survey of its kind in Germany – on behalf of the Federal Government from 2017 to 2021. The study examines the extent to which physical and mental impairment affect the opportunities for participation in different areas of life and is thus divided into several sub-studies. Among these, the main survey focuses on 16,000 people with disabilities as well as 1,000 homeless and hard-to-reach people with disabilities. The study operates multi-methodically and with barrier-free survey methods. As a participative research project people with disabilities are involved in the research process.
The task of the study is to build the first national sample of disabled people and to interview people who are "not questionable". Against this background, the lecture will discuss two approaches to sampling hard-to-reach populations: On the one hand, the building of the first-ever national sample of impaired and disabled people by using a registry offices’ sample (in 250 communities), which is combined with a comprehensive screening approach. On the other hand, we will discuss the use of methods of time-location sampling and respondent-driven sampling in order to interview 1,000 homeless and hard-to-reach people with disabilities.