Begin with the decision the NGO must make
Good baseline research is designed backwards from programme decisions. For an NGO operating in South Sudan, that means defining intended participants, understanding local delivery structures, and choosing indicators that will still make sense at midline and endline. Comparing experiences in Juba and Wau can strengthen the initial design because the two settings are not identical: a capital with humanitarian and commercial concentration, while Wau represents a western city with a distinct local economy.
Hold a short inception exercise before building the sample. Programme staff should explain what the intervention will deliver, when exposure begins, and which target groups could be affected first. The research team then turns vague ambitions into a limited set of testable questions. For instance, a health programme may need to distinguish awareness of a service from physical access and actual use. Keeping these constructs separate makes later comparisons more credible and ensures that resources are spent collecting evidence that can inform management.
Understand how research geography works in South Sudan
Fieldwork should follow the administrative vocabulary used in South Sudan: states, counties, payams and bomas. These structures help researchers identify the correct boundaries, local gatekeepers and likely sources of population lists, but they do not automatically supply a complete probability sampling frame. First establish which boundaries apply to the actual programme area, then verify maps and household or facility counts. Administrative labels should be used only after checking their exact local meaning and relationship to the study population. Before committing to a location, review current security, displacement patterns and partner access. Do not pressure enumerators or respondents to travel to unsafe sites simply to fill geographic quotas. A smaller ethically feasible study with stated limits is preferable to a nominally broad but unsafe design.
Define the target population and sampling frame
Define the population before choosing sample size. The frame may be households, registered beneficiaries, service users, facilities or another identifiable universe. Decide whether the study needs representative estimates, comparisons between key groups, or exploratory findings; each calls for different selection rules. For probability work, document the frame, selection stages, inclusion probabilities, nonresponse approach, clustering and any planned weights. Quota and purposive samples may be appropriate for some questions, but their descriptive limits should be reported openly rather than hidden by a large number of interviews.
Baseline research in Juba: designing the first city sample
In Juba, where the city functions as a capital with humanitarian and commercial concentration, a baseline might start with an inventory of programme catchments and service points. If the intervention concerns food security and cash assistance, the field team should test measures of food consumption patterns, payment experience, market access and coping strategies. Interviewer routes should deliberately cover selected neighbourhoods rather than concentrating around accessible offices and major roads. Compare the register of intended participants with people who actually use services; discrepancies may expose targeting or access problems. Whether the unit is a household, young person, facility or beneficiary must remain consistent through analysis.
Baseline research in Wau: testing a contrasting city context
Wau deserves an independent field plan because it is a western city with a distinct local economy. Before importing the Juba questionnaire unchanged, conduct a short cognitive pilot and verify where the target population resides, travels and receives services. The sample allocation should reflect the stated comparison question, not an arbitrary equal split made for convenience. If the NGO needs a comparison between the two cities, agree the minimum precision and subgroup coverage needed. Findings should describe the observed difference without attributing it automatically to geography or programme performance.
Build indicators that can be measured again
Treat indicator definitions as the bridge between programme claims and survey evidence. For each proposed outcome, ask whether it can be observed, self-reported or verified from records. Avoid relying on a single question when the construct has several dimensions. If measuring household resilience, for instance, clarify the time horizon and components rather than applying a generic label. Pilot response categories and translation choices. The baseline data dictionary should allow an independent analyst to reproduce the reported indicators without guessing.
Choose appropriate survey and qualitative methods
A credible baseline rarely depends on a survey alone. Administrative records can describe programme reach, facility reviews can establish service readiness, and qualitative interviews can explain barriers not captured by fixed response options. The survey provides structured measures when the sampling design supports them. Decide which source is authoritative for each construct and how discrepancies will be investigated. A photograph, GPS point or timestamp should be collected only if needed and ethically justified; technology is not a substitute for valid measurement.
Translate and pilot instruments for local respondents
Tools and consent scripts should be tested in English, Juba Arabic and locally relevant languages. A language that works for a formal meeting may not be the clearest language for a household interview, and bilingual respondents may switch vocabulary when describing sensitive experiences. Prepare a glossary for core constructs, reconcile translations with the source questionnaire and role-play difficult questions. Recruit interviewers for language competence and cultural familiarity, not only device skills. Translation changes need recorded approval so that future midline and endline rounds can preserve conceptual equivalence.
Design field operations around actual access conditions
Operationally, displacement, safety and weak or shifting population frames limit generalisation. Plan interviewer assignments, transport, safe operating hours, supervision and escalation around this reality. An initial reconnaissance visit can check listing feasibility, accessibility, the actual duration of an interview and any mismatch between administrative maps and lived settlement boundaries. A rigorous pilot should include sample-selection rehearsal, consent practice, skip-logic tests and mock data submission. Budget for replacements that comply with the original selection procedure rather than allowing opportunistic substitution when a respondent is absent.
Protect consent, privacy and vulnerable participants
Ethics is an operational design requirement. Explain that participation does not guarantee benefits or change programme eligibility. Avoid collecting precise location, images or contact details simply because a platform allows it. Where safeguarding topics or vulnerable populations are involved, establish a clear response protocol and train interviewers not to promise confidentiality they cannot protect. Consent wording should be understandable in the language used for the actual conversation. Institutional and local permissions must be checked for the relevant sites and activities.
Check quality while fieldwork is still active
Quality assurance should begin with the sampling frame and tool. Train enumerators on eligibility, consent, neutrality, translation, route procedures and escalation. Use role plays and certification interviews before deployment. During collection, dashboards can flag unusual duration, repeated coordinates, inconsistent answers and incomplete quotas; none of these signals proves misconduct by itself. Supervisors should investigate patterns using observations, call-backs and documented checks. Predefine interview rejection rules to prevent convenient decisions after fieldwork.
Analyse change without overstating causal claims
Analysis should move from data validation to clearly labelled findings. Report weighted estimates only when design and weights justify them. Examine subgroup differences with attention to sample sizes and uncertainty, and separate observed conditions from assumptions about programme causality. Triangulate conflicting findings: a facility may report stock availability while households describe barriers to access. The final product should include baseline values, limitations, an indicator annex or calculation dictionary, and a short decision brief for programme managers.
Turn baseline evidence into action in South Sudan
For a hypothetical NGO programme focused on food security and cash assistance, the useful deliverable is not simply a table showing food consumption patterns, payment experience, market access and coping strategies. It is an explanation of which barriers can be addressed through the intervention and which reflect factors outside programme control. If participants know a service exists but rarely use it, explore cost, location, opening time, quality, trust and social norms. Recommend a response only when the evidence distinguishes among those explanations. The baseline should also register external shocks and programme exposure dates to protect later interpretation.
At the end of the exercise, the NGO should be able to say what the baseline means for South Sudan, what it means specifically for Juba and Wau, and what it does not mean. Set a schedule for repeating core indicators, preferably at comparable seasonal periods. Keep the sample frame, contact protocol, questionnaire versions, interview-date records and calculation syntax. Use a short management session to decide whether targeting, staffing, communication or referral practices should change. Record those decisions so the endline can examine both outcomes and the adaptation process.
What a useful baseline report should deliver
A strong South Sudan baseline report should provide a clearly defined study population, an honest sampling account, accepted-interview totals, response outcomes, reproducible indicators and accessible recommendations. City comparisons should carry their own denominators and limitations. The report can be supported by a clean dataset, fieldwork memo, codebook and monitoring matrix, for internal analysis and subsequent measurement rounds. Any gaps in coverage should be visible so implementers can decide whether further listing or targeted qualitative inquiry is needed before expanding activities.
How Surveysphere Africa can support
Surveysphere Africa helps NGOs and development organisations design baseline studies, define indicators, plan samples, manage fieldwork, verify data quality and turn findings into actionable programme decisions. For work involving Juba, Wau or wider coverage in South Sudan, the research design should begin with the intended decision and the population it needs to represent, then adapt responsibly to the local context.
Related guidance
For the continent-wide view, read our guide to baseline, midline and endline studies.
Our services for research in South Sudan
We run research across African markets. Start with these services for work in South Sudan:
- Monitoring, evaluation and learning in South Sudan: Baseline, midline, endline and programme evaluation.
- Social and development research in South Sudan: Qualitative and quantitative studies for social and development programmes.
We also offer: Outsourced fieldwork, Market and consumer research, Public health research, Policy, economic and institutional research, Data analytics and insights visualisation.
