focus groups and key informant interviews can shed light on local dynamics, but only when conducted by experienced teams who can carry out such work while minimizing bias. Multiple data sources are often useful to validate initial assessments, for instance comparing behavior and incidents data with findings from surveys focused on people’s perception. At the diagnostic stage, the understanding of possible adverse impacts and spillovers of similar interventions can be useful in refining the project design and monitoring structures. Visual tools like stakeholder mapping can be useful in helping project teams process available information.

# _M ONITORING_

In collecting data for monitoring project interventions, timing and availability are key constraints. Project design will inform the most relevant indicators, and the team must consider whether available data sources collect relevant indicators frequently enough to fulfill monitoring needs. If surveys need to be put in place, the team must evaluate if the data being collected captures both short-term expected and unexpected outcomes, to allow them to build useful feedback systems within the project. Moving beyond typical quantitative indicators, the team could consider whether the project and context offer space for ethnographic research streams where immediate impacts of implementation can be observed in daily contexts over time, including to identify potential adverse impacts. If monitoring requires the collection of sensitive information, such as support for rebel groups, surveys could utilize emerging tools such as list and endorsement experiments to elicit sensitive information anonymously.

# _A NTICIPATORY A NALYTICS OR E ARLY W ARNING S YSTEMS_

In designing early warning systems, the challenge is in accurately identifying relevant indicators that can predict conflict trajectories. This requires strong diagnostics, including inputs from local stakeholders through qualitative approaches. Once indicators have been identified, teams will need to assess the existing availability of such data versus collection needs. This can involve integrating data from multiple partners and will require validation approaches. Using multiple sources of data can corroborate trends in key predictive indicators: for instance, tracking multiple indicators of trust or incidents of violence can be useful in reaching operational conclusions. Finally, providing space for the emergence and observation of unexpected outcomes in predicting conflict is key to ensure the team is not too narrowly focused.

# _E VALUATIONS BASED ON RANDOMIZED CONTROLLED TRIALS_

In setting up a measurement strategy to evaluate an intervention, it is often useful to begin by assessing if randomization is possible technically, administratively, and politically. Evaluations strategy must anticipate and adapt to the political and ethical implications of randomization in different settings – for instance a cash transfer during active conflict versus a dispute resolution mechanism during a post-conflict period – must be considered. Next, it should be considered whether it is feasible to embed an impact evaluation at the onset of the program itself, which would allow for the collection of accurate baseline information and enable more meaningful comparisons. The quality of implementation, e.g. through process evaluations, is an integral component of impact evaluations given that outcomes are critically dependent on actual implementation. This is all the more relevant for interventions targeting social cohesion, which are context and situation specific along with being heavily dependent on participant behavior, unlike a more simplistic standardized intervention such as the distribution of malaria nets or cash. This process level information of impact evaluations can also be potentially used for predictive or program monitoring purposes. Finally, in the design of the impact evaluation itself, the 25 | P a g e