Questionnaire Design

Profiling Producers to better support them

If your supply base encompasses thousands of smallholder producers, or your development project reaches similar numbers of beneficiaries, you’ll certainly have groups of producers behaving the same way or facing the same issues.  For example, producers whose yields are under the average, producers who never use fertilisers, producers who always deliver top quality, those who receive training but keep their old habits, and the ones who have dependents under the age of 6.

                Profiling them based on set criteria could be an efficient way to better support them and monitor the impacts of your interventions on these groups.  Good databases and systems include query functions allowing you to isolate producers using filters and set criteria.  But in most cases, you’ll need to repeat the same iteration to obtain the same result.  For example, female farmers having field of less than 1 hectare, having 4 children under 16 and a yield under the average if you want to monitor this specific group.

                Our web platform has a powerful feature called the User Story.  With this feature, users can create groups of producers and save their stories by giving it a name: Female with less 1ha, with 4 and up children under 16 and yield under average.  This means you can access this search repeatedly without having to redefine your criteria.

                Suppose at the start of your program you have 46 women meeting the criteria mentioned above.  Your objective should be to decrease this number overtime and support these women to improve their yield, despite the small size of their farm and their obligation toward young dépendants.

Screen shot from the web platform showing 46 female famers who meet the criteria selected

Screen shot from the web platform showing 46 female famers who meet the criteria selected

In addition to being able to save set criteria, the System is dynamic and updates the results as data is added.  This means you can set the criteria at the start of the project and then assess how the results change over time.  So, you could build a complex M&E framework with various milestones to reach overtime.

Screen shot showing a pre-defined list of searches used for monitoring and evaluation

Screen shot showing a pre-defined list of searches used for monitoring and evaluation

The User Story also has the ability to show you when certain criteria are not met.  For example, you can create a story for the producers who only deliver C quality, the lowest.  But you can reverse the result and obtain the producers never delivering the C quality.  This is very useful to instantly create control groups.

Screen shot showing the ability to invert search criteria

Screen shot showing the ability to invert search criteria

The System lets you modify your existing stories by adding or removing criteria.  This way, you can follow your groups as they evolve and bring them to a next level.

You can learn more on the User Story by getting in touch with us (

4 Tips for Designing Questionnaires on Smallholders

People often ask us to support them as they decide what data to collect on projects. It is essential to take time getting this right at the start of a project. Over the years, we’ve seen the problems and cost implications that occur when data requirements change during a project. So here are some tips on how to avoid this angst.

1) Focus on needs rather than wants

There are endless possibilities of data you could collect. It’s easy to get carried away with ‘nice-to-haves’ but it takes time, and money, to sift through all the data and pick out what you actually need. Instead, be really clear at the start of the project about what you are trying to achieve. Then link every piece of data you collect back to the project goals.

2) Think about who will use the data and how

Once you have an initial list of data, look at it again through the lens of ‘who’ will use the data and ‘how’ the data will be used. This will help you refine the list of data further to what is completely necessary.

3) Consider how your questions will generate accurate data

It’s amazing how easy it is to write a question and not provide an adequate answer option. You should consider the potential barriers to eliciting an accurate answer. For example:

o   Is the question answerable? Make sure that every question can be answered, even if it is ‘not known’ or ‘respondent doesn’t want to answer’, otherwise your data may be distorted.

o   Who are your interviewers and the interviewees? How could their demographics affect the data you receive (e.g. is the question culturally sensitive? Are language or literacy barriers at play?)

o   How will your chosen format affect the data generated (e.g. if you are collecting data in remote areas, will a tablet or mobile phone battery last long enough? In some areas, perhaps a more informal paper field questionnaire would be more appropriate?)

o   What kind of training do the interviewers need and how will you verify their work? We ensure that all data uploaded on our systems go through a data verification processes. For larger projects we sometimes also conduct a data audit to check that the data gathered is correct and assess whether interviewers require additional training or support.

4) Put yourself in the data analysis mode

The choice of what data to collect needs to be driven by the analysis required. It is often the case that people collect lots of data and then try to work out how to use it to show the impacts they want to measure. However, a more effective method is to start with the impacts you want to measure, then ask what you need to know in order to measure that impact and keep drilling down until you reach a piece of data that can be collected. You also need to consider what you want to measure throughout the duration of the project.

At GeoT we’ve helped organisations collect data on over 180,000 people. Feel free to contact us to discuss how we can help you with your data needs.