Paper-based data collection: Moving backwards or expanding the arsenal?

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Considerable effort has gone into perfecting the art of tablet data collection, which is the method typically used to collect data for evaluating education programs. The move away from paper has been a welcome shift, as for many research and evaluation professionals, paper conjures images of junior staff buried under boxes of returned questionnaires manually entering data into computers. Indeed, when our team recently began experimenting with paper-based data collection in our education projects, one colleague with decades of experience remarked warily, “It just seems like we’re moving backwards here!”

Improvements in the software, however, allow us to merge new technology with “old school” methods. Digital scanners can now replace manual data entry, powered by software that is able to read completed questionnaires, and quickly format responses into a data set for subsequent analysis. Our team has been experimenting with a new digital scanning software called Gravic to easily and quickly enter data from paper-based surveys. The Gravic digital scanning tool introduces flexibility and opens a new option for data collection across our projects, but not without some drawbacks. In this post, we make the case for paper surveys combined with the Gravic software and then review the drawbacks.

Don’t waste evidence on the youth! Recent data highlights education and employment trends

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A recent New York Times article describes a major contemporary challenge facing governments: the world has too many young people. A quarter of the world’s population is young (ages 10-24), and the majority live in developing countries. Policy makers are struggling with high levels of youth unemployment in every country, but a key challenge in developing countries has been a lack of data on education and employment characteristics. To fill this evidence gap, FHI 360’s Education Policy and Data Center (EPDC) recently added country-level Youth Education and Employment profiles to the resources available on our website. In this post, I describe the data and how they were collected, and I give some examples of how these data can be used to inform policy making and program design.

7 takeaways from changes in US education grant programs

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I recently had the opportunity to attend a workshop on the U.S. Department of Education’s (ED) new Education Innovation and Research (EIR) grant competition. EIR is the successor to the Investing in Innovation (i3) grant program, which invested approximately $1.4 billion through seven competitions from 2010 to 2016 to develop, validate and scale-up evidence-based programs in education. Like i3, EIR implements a tiered award structure to support programs at various levels of development. This blog post summarizes my seven takeaway points from the workshop. These seven points highlight the main changes in the transition from i3 to EIR.

Gearing up to address attrition: Cohort designs with longitudinal data

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As education researchers we know that one of the greatest threats to our work is sample attrition – students dropping out of a study over time. Attrition plays havoc with our carefully designed studies by threatening internal validity and making our results uncertain. To gear up for our evaluation of the Pennsylvania State Gaining Early Awareness and Readiness for Undergraduate Programs (GEAR UP), we designed a three-pronged approach to handling sample attrition. We describe it here in case it can be helpful to others.

Null results should produce answers, not excuses

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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.