Start with the decision the baseline should support
A baseline should not be treated as a routine data-collection exercise. It should clarify the programme’s starting conditions and create a reference point for learning, adaptation and endline comparison. The first question is therefore not “which questionnaire should we use?” but “what decisions should this evidence help us make?”
For development projects in Ethiopia, this may include decisions about targeting, implementation readiness, service access, inclusion, geographic prioritisation, risk management and future measurement.
Define indicators before designing tools
A common weakness in baseline studies is that questionnaires are drafted before indicators are clearly defined. This creates problems during analysis because variables may not match the logframe, response options may be incomplete and disaggregation may be impossible.
Each indicator should have a clear definition, numerator, denominator, reference period, data source, disaggregation requirement and calculation rule. Once this is agreed, the questionnaire can be designed with endline comparability in mind.
Use a sampling design that is defensible and feasible
The best sampling approach depends on the population, intervention design, budget, timeline, geography and expected level of precision. In many development settings, cluster sampling is practical, but it must be documented clearly. Where comparison groups are needed, selection criteria should be transparent and based on programme logic and contextual similarity.
A baseline report should explain not only the final sample size but also how the sample was selected, how non-response was handled and what limitations remain.
Invest in tool testing and field supervision
Many baseline problems are preventable through careful piloting. A pilot helps identify confusing questions, unrealistic response options, translation problems, skip-logic errors and sensitive questions that require better wording.
Digital data collection can improve speed and quality, but only when forms include validation rules, trained enumerators, daily quality checks and clear escalation procedures.
Design the baseline for future endline use
A strong baseline leaves behind clean datasets, codebooks, syntax or calculation notes, final questionnaires and clear indicator tables. These materials are essential for future evaluators who need to compare baseline and endline results.
When baseline documentation is weak, endline evaluation becomes slower, less credible and more expensive. Designing for future use is therefore one of the most important quality standards.