The letter in today's picture almost wrecked my presentation in Oxford last week - and reminded me how easy it is to get quantitative papers spectacularly wrong. What happened? While re-writing my talk two weeks ago (basically extending earlier work from Lucknow to the whole of Uttar Pradesh), I noticed some odd phenomena in my dataset. And freaked out. Oxford is not just any place to be invited to speak at. I had to know quite exactly what had gone wrong. After some fairly tense hours, I discovered it: the "Š", a letter which apparently crept into some electoral rolls of Eastern UP (and no, these are not written in latin script, but in devanagari). I have no idea why they were there in the first place, and there seemed to be no system - but as soon as my name-matching algorithm stumbled across them, it crashed. And left my dataset corrupted. Luckily, I was able to solve the problem (the final dataset arrived literally five minutes before my presentation), and could ditch the "I am truly sorry but my talk just dissolved in a data nightmare" embarassment. Close call!

Similar to earlier data trouble, I thus realized again how easy it is to spectacularly fail in quantitative research. You get one calculation wrong, a data row slips elsewhere - and your analysis is blown. It's much harder to fail equally grand in qualitative research. This is no argument in the quantitative-qualitative debate (which I find silly most of the time anyway). But if you deal with numbers as part of your research: be careful. Very careful. There might be a lurking "Š" around, waiting to destroy your fancy arguments at the most inconvenient hour...

Once you have observed something, once you have interviewed somebody, how do you get from this pile of data to a compelling research report? Today's blog sums up the gist of a lecture I recently gave on this issue as part of my field methods teaching at SIT Study Abroad. It looks at four stages in turn: explorative analysis, draft writing, confirmative analysis and editing. These stages are, of course, most likely circular activities, through which you go back and forth until you a) arrive at a decent report or b) simply run out of time. Which is the chief reason why it is advisable to start the circle as soon as possible, not letting the pile of data become so scaringly tall in the first place...

Thanks to all who offered their help in reply to my last post about visualizing typological data. Most of you agreed that the recent hype about visualization and infographics almost completely neglects qualitative small-n data in favour of quantitative large-n sets. There are various reasons for this state of affairs: for one, much of this hype is driven by the fact that large government statistics are being put in the public domain (or by the availability of equally large private statistics generated through web 2.0). Secondly, small-n data lends itself nicely to narrative writing, rendering visualization a less pressing requirement. Lastly, such qualitative data tends to be far more complex than statistics, and its complexity can not easily be reduced through statistical generalization.

Reducing the complexity of small-n data is not impossible, however - and usually takes the form of typologies. The need to visualize these, and particularly to visualize them in an interactive way, arises from the fact that such typologies often suggest a rigidity which is never there in the data (as I wrote here). The reason for this deception is basically that the underlying cluster analyses - be they statistically aided or intuitive - always generate an x-fold typology if you ask them to so - even if the dissimilarities between types are marginal in comparison to their similarities. The irreducability of original data behind typologies is therefore what I would love to visualize, to give readers of my upcoming book1 a hands-on feeling for the flexibility of the typology of Muslim peace activists which I propose therein.

This post concludes the tripartite series of lecture summaries from the fieldwork methods class which I co-teach this term at SIT New Delhi.1 It addresses several issues faced by foreigners who do research in India (or elsewhere) as opposed to domestic scholars who research their own culture - and simultaneously problematizes this terminology. The lecture moves from the practical to the conceptual, picking four potential trouble areas: language, field relations, othering, and categories. The remaining posts of this series are here:

Description, interpretation, evaluation
Research questions, interview questions
Doing research as a foreigner

  • 1. More on this class here...

This post is the second in a tripartite series of lecture summaries from the fieldwork methods class which I co-teach this term at SIT New Delhi.1 Before Azim Khan spoke about interviewing in more practical detail in the second half of our lecture, I framed the practical issues he raised by looking at the difference between research questions and interview questions. This post sums up my key points from this framing exercise; the rest of the series is here:

Description, interpretation, evaluation
Research questions, interview questions
Doing research as a foreigner

Let's first look at today's picture, taken at one of my earlier attempts to interview Maulana Khaled Rashid in Lucknow. All the practicalities are sorted: tea is being served, the Hadith commentaries provide a nice background, cameras and mikes are set up (the latter are not mine, of course - it was the time of the Salman Rushdie controversy, and I had to share my appointment with a dozen journalists). But the central piece is missing: the Maulana, with whom I hoped to have a conversation.

  • 1. More on this class here; this particular lecture (namely its emphasis on the advantage of clear epistemological bases) was inspired by Roulston, K, DeMarrais, K, Lewis, JB. (2003). Learning to interview in the social sciences. Qualitative Inquiry, 9(4), 643–668.