A New Approach to MEAL Design
When do you start planning your data collection tools?
As MEAL (monitoring, evaluation, accountability and learning) practitioners we’ve become experts at designing SMART indicators, gender-sensitive indicators, environmentally sensitive indicators, and other indicators specific to the unique sectors in which we work. However, sometimes we end up with a wonderful collection of indicators that we quickly find out would require a huge number of data collection tools and staff to collect.
So, when is the right time to start planning your data collection tools?
Though many people do this after they have designed their indicators and put them in a performance measurement framework. We have a different idea: Planning your data collection tools should ideally come before you’ve even designed your first indicator.
Does that sound different from how you do it? Let’s see if we can convince you to try out this process the next time you’re planning a new project.
Here are three reasons why we begin planning data collection tools before defining project indicators.
1.Makes data collection tools realistic and manageable.
Most of us are working with limited time and resources when it comes to monitoring and evaluation. When you start planning your project indicators without the data collection tools in mind, you can end up with indicators that aren’t measurable because you simply can’t manage the number of data collection tools.
This is not about sacrificing the quality of indicators because of resource limitations but finding a balance between the number of tools and measurable indicators.
2. Ensures all data sources are captured.
Planning data collection tools before indicators brings describing your data sources to the forefront. We recommend a process where you list all you data collection tools and all your data sources and match the two together to make sure that you have the appropriate tools to gather data from all your data sources.
Data sources may be as wide ranging as a group of project participants (i.e. adolescent girls) to records and documents that need to be reviewed (i.e. health facility records). In Kinaki, we’ve designed what we call the “MEL Wizard” to link data collection tools to data sources. We’ll show you more below.
3. Saves time down the road.
Have you ever looked at a project measurement framework (PMF) and seen under the data collection method “survey” and the data source “project participants” or “beneficiaries”? If you haven’t, perhaps you’ve seen just an empty space?
Without assigning specific tools and sources to indicators the project team that is responsible for collecting data and reporting on it is left with having to determine this information themselves. This is particularly challenging if the project team was not engaged in the project design or if the project took some time to be approved so the PMF was created months earlier. This can take up a lot of time during a period when the project team has a lot on their plate getting the project up and running.
This process can be facilitated in the MEL Wizard in Kinaki. (You can use this feature in Kinaki by signing up for a free account.
1.The first step in the MEL Wizard is to list all your possible data sources.
(Think of who or what will provide the data you need. And get as specific as possible.)
2. Next, list all the methods you plan on using to gather your data.
3. Then you match your data sources with the methods. Thinking about with method works best for each source (or if multiple methods are needed for some sources).
If you do this in Kinaki, this process will create your Data Collection Tools table which you can add to, update and refer back to at any time.
By using this process, when you create your project indicators you can refer back to the Data Collection Tools table and link these tools to the appropriate indicator. You may find that while you’re working on your PMF you want to make modifications to the Data Collection Tools table, which is great. It’s not a linear process, but by starting out with realistic expectations for your data collection tools and a good understanding of your data sources you can save time and make your MEAL planning much more manageable.
Was this article useful? Will you try this out next time you’re designing a project? Join the discussion on LinkedIn!
Watch a video to see how it’s done in Kinaki:
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