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These datasets provided hard evidence of how household assets and behaviours changed from the beginning to the end of the programme.

Comparing the findings from the surveys conducted at baseline, mid-term, endline and annually helps us to understand what is changing, where and by how much.

- Richard Ponsford , Monitoring and Evaluation Advisor, Christian Aid.

Analysing the data

The survey data was then uploaded to an online database and downloaded to data analysis software. The analysis software enabled the team to identify changes in key indicators and breakdown the data by gender, location and other key variables.

This helped the project team understand if the changes were being experienced by all beneficiary groups, or if they varied by gender or location. If a group was identified as not experiencing much change, the team were able to use this evidence to explore why and adjust project activities accordingly. 

Collecting data digitally saved the team a lot of time, allowing them to spend more time with the communities validating their findings. 

By endline, the time saved allowed for us to validate the survey findings with communities whilst also using the survey findings to choose the villages for qualitative data collection that we felt would offer the most additional learning – the highest and lowest performing villages.

- Richard Ponsford , Monitoring and Evaluation Advisor, Christian Aid.

Overcoming challenges in data analysis

The team encountered common challenges with using the new technology. These included setting up devices properly, linking them to the online database, and setting up the questionnaire so data was coded in an easy-to-analyse way.

The survey software is easy to use to create a questionnaire, but we learnt it’s just as important to check how answers are recorded. Making sure the survey set-up links well with the analysis plan and software makes the process much easier. This will help us develop good practice guides for the use of data collection tools.

- Richard Ponsford, Monitoring and Evaluation Advisor, Christian Aid.

Sharing learning for future data analysis

The team hope that sharing this learning in an accessible way through data stories will allow others with an interest in climate-resilient agriculture to identify the learning most relevant for them.

In this way, the project’s data has the potential to keep contributing towards improving the resilience of vulnerable communities.

An important characteristic of the data being shared is that it includes outcomes (changes), not just outputs (activities delivered).

This means that people can see what is and is not changing. Which is more useful than just knowing what has been delivered.

- Richard Ponsford, Monitoring and Evaluation Advisor, Christian Aid.

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