The end goal of collecting data is to eventually draw meaningful insights from said data. However, the transition from raw data to meaningful insights is not always a linear path. Data are prone to human-error and this guide will help you correct those errors, as well as provide tips on how to minimize these errors in the future.
Read MoreOne of the most common things I see when I work data in excel that others have compiled or analyzed is the under-use of Excel’s computational powers. In part, this has to do with setting up your data correctly to all Excel to do the work.
Read MoreWe’ve all been there – you get some data from a client or a survey you’ve run, and you can’t wait to start answering your evaluation questions. But you find one of your data columns is a complete mess because it was an open-ended text field.
Cleaning this messy data can be a day-ruining task - but this doesn’t have to be! I’m going to show you how to use OpenRefine to make this task a million times easier.
Read MoreIn qualitative approaches, we want to describe, to present details and nuances and interesting outliers. But as evaluators, we need to do more than just report what is—we need to comment on what it means. In familiar evaluation terms, moving from the “what” to “so what?”
This framework can help you to consistently “quantify” qualitative findings.
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