With so many players latching on to the idea of big data these days, it is inconceivable that everyone has the same definition in mind. I’ve heard folks describe big data as just being the combination of existing data sets while others don’t consider data to be big until there are hundreds of thousands of observations. I’ve even seen the idea that big data just means the increasing availability of open data. There is a similar challenge with data analytics. On one end of the spectrum, data analytics is just data analysis, but with a cooler name. On the other, data analytics involves big data (really big data) and machine learning. I needed to get a grasp on the various terms and concepts for my work, so I thought I’d share some of what I learned with you. Prepare to learn.

My team recently encouraged a colleague who is collecting and analyzing data in one of our projects to submit the analysis and results to a journal. His response was “why?” He argued that his audience is not reading journals, that there are other mechanisms for getting feedback, and that since he is a technical person and not a researcher, he doesn’t need citations. My colleague believes publishing in other ways is equally valued. This post is my attempt to convince him, and you, that practitioners who are conducting research should publish in journals.

There is a lot of excitement these days about science, technology, and innovation and the potential for these activities to contribute to economic and social development globally. The flurry of activity begs the question, how much of this excitement is supported by scientific facts? To help answer this question, the US Global Development Lab at USAID commissioned a project to create and populate a map of the evidence base for science, technology, innovation, and partnerships (STIP). As part of the project, scoping research was conducted to identify not just where there are evidence clusters and gaps, but also where the demand for new evidence by stakeholders is the greatest. In the recently published scoping paper, I and my co-authors analyze the data in the map together with the information from the stakeholders to recommend priorities for investment in new research on STIP. While there is good evidence out there, new research is necessary for strategies and programming to fully benefit from scientific facts. In this post, I briefly describe the research we conducted, summarize a few of the many findings, and list some of our recommendations.

In my previous job at 3ie I spent a lot of time telling researchers that a randomized controlled trial (RCT) with a few focus groups thrown in for good measure doesn’t count as a mixed methods impact evaluation. In the course of repeatedly saying that focus groups are not enough, I must have developed an unconscious bias against focus groups, because I was pleasantly surprised by what I learned from a recently published article written by some of my FHI 360 colleagues. In their study, Guest, et al. use an RCT to compare the performance of individual interviews against focus groups for collecting certain data.

I recently served on a Center for Global Development (CGD) panel to discuss a new study of the effects of community-based education on learning outcomes in Afghanistan. (Burde, Middleton, and Samii 2016) This exemplary randomized evaluation finds some important positive results. But the authors do one thing in the study that almost all impact evaluation researchers do – where they have null results, they make, what I call for the sake of argument, excuses.