How ICCM Got Me Thinking About Experimental Design

We have all probably had a time when we thought to ourselves,”I have to tweet hard since most people won’t see/remember one particular tweet.”  I would generally agree, but there was one tweet that stuck in my mind from the International Conference on Crisis Mapping this past weekend at the World Bank.  It got some traction, but still was in a sea of other tweets – so here I am four days later thinking about why it stuck with me.  Here’s the tweet:

This got my attention for two reasons.  The first was excitement that there was interest in hearing more about what has happened in the last few years since Haiti.  As the tweet points out, a great deal has changed and lessons have been learned.  The second was that it got me thinking about comparative and experimental research to capture the impact of the changes we’ve seen the last two years while we continue to drive the space forward.

I want to focus on the second reason for why this tweet got my attention.  I’ve been taking a refresher course in my Ph.D. program on experimental design, and I found myself both excited and concerned by the content of the tweet.  While we have to be keeping the new projects and events at the forefront, those of us in the political science departments of the world (like me) have to revisit the details of events from Haiti and make sure we continue to try to identify comparative variables across cases.  What were the key variables in the Haitian earthquake information environment that we can proxy across cases since 2010 and test statistically and qualitatively?  When we do this testing, do we see positive changes?  Any changes at all?  This kind of data analysis can help the tech community in their work designing the exciting mapping and data tools featured at ICCM.

It’s not within the scope of this post to identify all the variables across these cases.  What I want to focus on instead is the fact that this tweet puts onus on the social science community to be looking back at the Haiti example, identify the variables, and to dig into the quasi-experimental space to see what the changes are over time as new tools have entered the emergency response space since the 2010 earthquake.  If the tech community is driving us forward, the social science research community needs to be just as active collecting data and comparatively testing changes case to case.  If we can do that, we’ll see some amazing things coming down the tech4good pike.

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