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<a href="http://www.iq.harvard.edu/blog/sss/archives/2010/09/">&laquo; September 2010</a> |

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<p><b>24 October 2010</b></p>
<h3 id="a001355" class="Bigger">Stories and statistics</h3>
<p>Lately I've been thinking a lot (and writing a little) about ways to combine the qualitative and quantitative empirical traditions in political science, so I was quite interested to read a <a href="http://opinionator.blogs.nytimes.com/2010/10/24/stories-vs-statistics/?hp">new post</a> on the philosophy blog at the New York Times written by mathematician John Paulos.  He contrasts the logic of story-telling with the logic of statistics to draw out some interesting implications for how each mode of understanding colors the ways we think about the world.</p>
<p>In a sentence that could have come out of a "scope and methods" text, Paulos identifies the fundamental difference between literary and statistical traditions: "The focus of stories is on individual people rather than averages, on motives rather than movements, on point of view rather than the view from nowhere, context rather than raw data."  I think this is an accurate description of how <a href="http://pan.oxfordjournals.org/content/14/3/227.short">two empirical cultures</a> in social science have developed, but I disagree that this divide is inherent.</p>
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This may be unorthodox, but I don't see statistics as inherently "quantitative" or focused on the "general" rather than the "particular".  I see statistics as a relatively young field attempting to develop answers to the question "how should I go about formulating my beliefs about the world now that I've observed some part of it."  Eventually, statistics will need to offer advice on how to update our picture of the world after observing <em>any</em> type of information -- not just information that comes from randomized experiments, fits neatly in rectangular matrices, or involves enough "N" for some central limit theorem to hold.</p>
<p>Narrative research seems ideally suited to work with the types of information that traditional statistics has largely ignored.  Why then should statistics take up the task? Narratives are rich with data but researchers using narrative methods have little advice on how to make inferences from these data.  In the richest of literary narratives this ambiguity enhances the text, allowing the reader to reach many conclusions about the meaning and implications of a work.  In empirical social science, this ambiguity can become a liability.  If statisticians spent more time developing ways of making appropriate inferences from data in these settings -- frankly the most common settings that we face -- it might lessen this ambiguity by offering a clear set of rules for mapping complex narrative data to inference.</p>
<p>My hunch is that the people who work with data that lends itself to narrative research already have ideas about the best practices for making valid inferences from these data.  Perhaps we should be more interested in learning to speak statisticians' language so that we can suggest these insights to them and they in turn can suggest refinements for us.  This exchange would help statisticians develop a science of inference and help us develop knowledge of social phenomenon.</p>
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Posted by <a href="http://www.iq.harvard.edu/blog/sss/archives/author/richard-nielsen-1/">Richard Nielsen</a> at <a href="http://www.iq.harvard.edu/blog/sss/archives/2010/10/stories_and_sta.shtml"> 8:10 PM</a>

| <a href="http://www.iq.harvard.edu/blog/sss/archives/2010/10/stories_and_sta.shtml#comments">Comments (1)</a>
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<p><b>22 October 2010</b></p>
<h3 id="a001354" class="Bigger">Workflow Agonistes</h3>
<p><a href="http://usesthis.com/">The Setup</a> is a site dedicated to interviewing nerdy folk about what software/hardware they use to do their jobs. It has mostly been web designers and software developers, which is interesting, yet removed from academics. Thus, I was glad to see them interview <a href="http://kieran.healy.usesthis.com/">Kieran Healy</a>, a <a href="http://www.kieranhealy.org/">sociologist at Duke</a>. The whole thing is worth a read if you are interested (like me) in these sorts of things, but here is a bit of his advice:</p>
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<p>Workflow Agonistes: I've written about this elsewhere, at greater length. Doing good social-scientific research involves bringing together a variety of different skills. There's a lot of writing and rewriting, with all that goes along with that. There is data to manage, clean, and analyze. There's code to be written and maintained. You're learning from and contributing to some field, so there's a whole apparatus of citation and referencing for that. And, ideally, what you're doing should be clear and reproducible both for your own sake, when you come back to it later, and the sake of collaborators, reviewers, and colleagues. How do you do all of that well? Available models prioritize different things. Many useful tricks and tools aren't taught formally at all. For me, the core tension is this. On the one hand, there are strong payoffs to having things organized simply, reliably, and effectively. Good software can help tremendously with this. On the other hand, though, it's obvious that there isn't just one best way (or one platform, toolchain, or whatever) to do it. Moreover, the people who do great work are often the ones who just shut up and play their guitar, so to speak. So it can be tricky to figure out when stopping to think about "the setup" is helpful, and when it's just an invitation to waste your increasingly precious time installing something that's likely to break something else in an effort to distract yourself. In practice I am only weakly able to manage this problem.</p>
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<p>Also good advice:</p>
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<p>I try to keep as much as possible in plain text.</p>
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<p>On his site, Kieran has more <a href="http://www.kieranhealy.org/resources.html">guidance on choosing workflows</a> for social science research. Sidenote: he has one of the best looking academic websites I have seen. </p>
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Posted by <a href="http://www.iq.harvard.edu/blog/sss/archives/author/matt-blackwell/">Matt Blackwell</a> at <a href="http://www.iq.harvard.edu/blog/sss/archives/2010/10/workflow_agonis.shtml"> 9:53 PM</a>

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