At the 2017 MERL Tech London conference, my team and I gave a presentation that addressed the possibilities for and limitations of evaluating complex situations using simple Excel-based tools. The question we explored was: can Excel help us manipulate data to create predictive models and suggest promising avenues to project success? Our basic answer was “not yet,” at least not to its full extent. However, there are people working with accessible software like Excel to make analysis simpler for evaluators with less technical expertise.
In our presentation, Rick Davies, Mark Skipper and I showcased EvalC3, an Excel based evaluation tool that enables users to easily identify sets of attributes in a project dataset and to then compare and evaluate the relevance of these attributes to achieving the desired outcome. In other words, it helps answer the question ‘what combination of factors helped bring about the results we observed?’ In the presentation, after we explained what EvalC3 is and gave a live demonstration of how it works, we spoke about our experience using it to analyze real data from a UNICEF funded War Child UK project in Afghanistan–a project that helps children who have been deported back to Afghanistan from Iran.
Our team first learned of EvalC3 when, upon returning from a trip to our Afghanistan country programme, we discussed how our M&E team in Afghanistan uses Excel for storing and analysing data but is not able to use the software to explore or evaluate complex causal configurations. We reached out to Rick with this issue, and he introduced us to EvalC3. It sounded like the solution to our problem, and our M&E officer in Afghanistan decided to test it by using it to dig deeper into an Excel database he’d created to store data on one thousand children who were registered when they were deported to Afghanistan.
Rick, Hosain Hashmi (our M&E Officer in Afghanistan) and I formed a working group on Skype to test drive EvalC3. First, we needed to clean the data. To do this, we asked our social workers to contact the children and their caretakers to collect important missing data. Missing data is a common problem when collecting data in fragile and conflict affected contexts like those where War Child works. Fortunately, we found that EvalC3 algorithms can work with some missing data, with the tradeoff being slightly less accurate measures of model performance. Compare this to other algorithms (like Quine-McCluskey used in QCA) which do not work at all if the data is missing for some variables. We also had to reduce the number of dimensions we used. If we did not, there could be millions of combinations that could be possible outcome predictors, and an algorithm could not search all of these possibilities in a reasonable span of time. This exercise spoke to M. A. Munson’s theory that “model building only consumes 14% of the time spent on a typical [data mining] project; the remaining time is spent on the pre and post processing steps”.
With a few weeks of work on the available dataset of children deported from Iran, we found that the children who are most likely to go back to Iran for economic purposes are mainly the children who:
Are living with friends (instead of with. relatives/caretakers)
Had not been doing farming work when they were in Iran
Had not completed 3 months vocational training
Are from adult headed households (instead of from child headed households).
As the project is still ongoing, we will continue to investigate the cases covered by the model described here in order to better understand the causal mechanisms at work.
This experience of using EvalC3 encouraged War Child to refine the data it routinely collects with a view to developing a better understanding of where War Child interventions help or don’t help. The in-depth data-mining process and analysis conducted by the national M&E Officer and programmes team resulted in improved understanding of the results we can achieve by analyzing quality data. EvalC3 is a user-friendly evaluation tool that is not only useful in improving current programmes but also designing new and evidence based programmes.