All time references are in CEST
Methods of including and boosting under-represented population subgroups |
|
| Session Organisers | Dr Olga Maslovskaya (University of Southampton) Dr Carina Cornesse (DIW Berlin) Mr Curtis Jessop (NatCen) |
| Time | Tuesday 18 July, 09:00 - 10:30 |
| Room | |
Some population subgroups are consistently under-represented or excluded from survey samples or may be present in too small frequencies for robust subgroup analysis. While approaches to targeting these population subgroups exist, they may not provide sufficient level of quality for social surveys (e.g., use of convenience samples) or may be prohibitively expensive or unfeasible in a self-completion mode (e.g., over-sampling for the Postcode Address File (PAF) and/or screening). As survey research globally aims to become more inclusive, it is crucial to explore effective methods of including and boosting under-represented population subgroups.
In this session we would like to investigate different effective methods of including and boosting under-represented population subgroups including respondent-driven sampling approach.
We encourage papers from researchers with a variety of backgrounds and across different sectors, including academia, national statistics, and research agencies. We particularly welcome contributions that use experimental designs, and/or other designs that can inform future strategies for including and boosting under-represented subgroups in large scale high-quality probability-based surveys.
The session is proposed by Research Strand 1 of the Survey Futures project, “Enhanced Sampling Frames and Procedures”. Survey Futures is a UKRI-ESRC funded research programme focused on ensuring large-scale social surveys in the UK can innovate and adapt in a changing environment. https://surveyfutures.net/
Keywords: under-represented subgroups, respondent-driven sampling, high quality surveys
Dr Mariel Leonard (DIW-Berlin) - Presenting Author
Ms Julia Witton (DIW-Berlin)
Dr Carina Cornesse (GESIS)
Dr Julian Axenfeld (DIW-Berlin)
Dr Jean-Yves Gerlitz (University of Bremen)
Dr Olaf Groh-Samberg (University of Bremen)
Dr Sabine Zinn (DIW-Berlin)
Respondent-driven sampling (RDS) is a network sampling technique for surveying complex populations in the absence of sampling frames. The idea is simple: identify some people (“seeds”) who belong or have access to the target population, encourage them to start a survey invitation chain-referral process in their community, ensure that every respondent can be traced back along the referral chain. Due to the reliance on respondent referral, RDS is frequently implemented with hidden or rare target populations, where members are assumed to know each other and thus have a higher degree of access than researchers.
We conducted a pilot study in 2023 where we invited 5,000 panel study members to a general population multi-topic online survey. During the survey, we asked respondents whether they would be willing to recruit up to three of their network members. Willing respondents then received personalized links with which to recruit their network members.
We found that younger individuals, individuals with higher incomes, and individuals with migration backgrounds were all (1) more likely to recruit, and (2) they typically recruited individuals similar to themselves. We additionally found that those recruits also had a higher propensity to participate, thereby potentially increasing the overall diversity of the survey. In this paper, we present a detailed overview of our results, along with relevant methodological findings such as method of recruitment. Additionally, we discuss 2025 fielding of RDS in the Social Cohesion Panel which builds upon the findings from our pilot study.
Dr Olga Maslovskaya (University of Southampton) - Presenting Author
Dr Carina Cornesse (GESIS)
Mr Curtis Jessop (NatCen)
Mr Mark Todd (NatCen)
Mr Toby Li (NatCen)
Mr Stephen Milner (ONS)
Ensuring inclusivity in general population surveys is paramount for robust survey research. However, some population subgroups are consistently under-represented or excluded, or are included in insufficient numbers for meaningful subgroup analysis. While methods to targeting these population subgroups exist, they may not provide sufficient level of quality for social surveys (e.g., use of convenience samples) or may be prohibitively expensive or unfeasible in a self-completion mode (e.g., over-sampling from the Postcode Address File (PAF) in the UK and/or screening).
Respondent Driven Sampling (RDS) has emerged as a successful strategy for recruiting hard-to-reach groups, such as LGBTQ+ communities, migrants, or ethnic minorities (Heckathorn, 1997). There have also been attempts to apply RDS in the context of boosting population subgroups in probability-based surveys.
Developed as a network sampling approach, RDS combines snowball sampling with the mathematical properties of probability-based sampling techniques to approximate a probability sample under Markov Chain assumptions (e.g., Thompson, 2014).
An evidence review is essential to consolidate the existing literature, grey literature, and reports, summarising various approaches used for RDS. We created a detailed coding scheme to capture all indicators of RDS methodologies. This review assesses strengths and limitations of these approaches in terms of effectiveness, costs of implementation, and speed of implementation. Additionally, we identify gaps in the evidence, which will provide valuable directions for future research in this area.
In this presentation, we summarise all findings from the evidence review and discuss recommendations for survey practice.