Session: Innovations in Human Trafficking Research: Locating Hard-to-Reach Populations (Society for Social Work and Research 24th Annual Conference - Reducing Racial and Economic Inequality)

195 Innovations in Human Trafficking Research: Locating Hard-to-Reach Populations

Saturday, January 18, 2020: 8:00 AM-9:30 AM
Liberty Ballroom O, ML 4 (Marriott Marquis Washington DC)
Cluster: Violence against Women and Children (VAWC)
David Okech, PhD, University of Georgia, Alex Balch, PhD, University of Liverpool, Jody Clay warner, University of Georgia, Nathan Tumuhamye, PhD, Makerere University and Roy Mayega, PhD, Makerere University
Scholars continue to find challenges in accessing, engaging, and retaining research participants from hard-to-reach populations. A range of techniques to recruit hard-to-reach populations, including snowball sampling, respondent-driven sampling, indigenous field worker sampling, facility-based sampling, targeted sampling, time-location (space) sampling, conventional cluster sampling, and capture re-capture sampling have been employed in the past to find and research theses populations. Among these populations are victims and survivors of human trafficking. The International Labor Organization estimates that there are 40.3 million victims of human trafficking globally. Not only are the validity and reliability of these numbers contested, even the definition and indicators of what constitutes trafficking around the world is still a matter of debate among scholars, practitioners, and policy makers. This dialogic space affords opportunity for researches and key stakeholders to design and apply context-specific data collection techniques that meet fundamental assumptions, including reliability, replicability, and validity. The Africa Programming and Research Initiative to End Slavery (APRIES) is leading efforts to collect baseline child trafficking data in selected countries in sub-Saharan Africa. The to Combat and Monitor Trafficking in Persons at the US Department of State Office is funding a 5-year research project whose goals are to: a) measurably reduce the prevalence of child trafficking in West Africa, and b) identify service and programming gaps and to design evidence informed programs and policies that bridge these gaps. APRIES is an interdisciplinary and international consortium of researchers in the US, the UK, and Africa. Researchers are drawn from the disciplines of social work, sociology, public health, women's studies, and political science. Researchers will be presenting on how to build complex teams of researchers and how to access a low-resourced country and mechanisms that they used in locating victims and survivors of child trafficking. The workshop will demonstrate the application of community-based participatory research (CBPR) techniques that include the use of the novel network scale up (NSUM) method augmented with general household surveys and a set of in-depth interviews and focus groups with survivors and other key stakeholders in two West African countries. To demonstrate briefly, in the NSUM, respondents are asked questions of the type: “How many X do you know?,” where X ranges over different subpopulations of both known and unknown sizes. Known subpopulations could include people named Asante, child workers, and children who have disappeared, while unknown subpopulations are typically the groups of interest, such as victims or survivors of trafficking. Apart from the research design, the workshop will share preliminary findings and lessons learned. The workshop is tailored to researchers working with hard-to-reach populations, especially those in the human trafficking field. Specifically, the workshop will achieve the following objectives: 1. Highlight the major challenges and opportunities in human trafficking research for interdisciplinary teams 2. Demonstrate the application of the NSUM techniques in the collection of trafficking data Discuss the limitations and biases of the NSUM in collecting trafficking data 3. Present findings on child trafficking data and offer lessons learned in the field of baseline data collection
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