Entries tagged with “interpretation”.

Well, the response to part one was great – really good comments, and a few great response posts.  I appreciate the efforts of some of my economist colleagues/friends to clarify the terminology and purpose behind RCTs.  All of this has been very productive for me – and hopefully for others engaged in this conversation.

First, a caveat: On the blog I tend to write quickly and with minimal editing – so I get a bit fast and loose at times – well, faster and looser than I intend.  So, to this end, I did not mean to suggest that nobody was doing rigorous work in development research – in fact, the rest of my post clearly set out to refute that idea, at least in the qualitative sphere.  But I see how Marc Bellemare might have read me that way.  What I should have said was that there has always been work, both in research and implementation, where rigorous data collection and analysis were lacking.  In fact, there is quite a lot of this work.  I think we can all agree this is true . . . and I should have been clearer.

I have also learned that what qualitative social scientists/social theorists mean by theory, and what economists mean by theory, seems to be two different things.  Lee defined theory as “formal mathematical modeling” in a comment on part 1 of this series of posts, which is emphatically not what a social theorist might mean.  When I say theory, I am talking about a conjectural framing of a social totality such that complex causality can at least be contained, if not fully explained.  This framing should have reference to some sort of empirical evidence, and therefore should be testable and refinable over time – perhaps through various sorts of ethnographic work, perhaps through formal mathematical modeling of the propositions at hand (I do a bit of both, actually).  In other words, what I mean by theory (and what I focus on in my work) is the establishment of a causal architecture for observed social outcomes.  I am all about the “why it worked” part of research, and far less about the “if it worked” questions – perhaps mostly because I have researched unintended “development interventions” (i.e. unplanned road construction, the establishment of a forest reserve that alters livelihoods resource access, etc.) that did not have a clear goal, a clear “it worked!” moment to identify.  All I have been looking at are outcomes of particular events, and trying to establish the causes of those outcomes.  Obviously, this can be translated to an RCT environment because we could control for the intervention and expected outcome, and then use my approaches to get at the “why did it work/not work” issues.

It has been very interesting to see the economists weigh in on what RCTs really do – they establish, as Marc puts it, “whether something works, not in how it works.”  (See also Grant’s great comment on the first post).  I don’t think that I would get a lot of argument from people if I noted that without causal mechanisms, we can’t be sure why “what worked” actually worked, and whether the causes of “what worked” are in any way generalizable or transportable.  We might have some idea, but I would have low confidence in any research that ended at this point.  This, of course, is why Marc, Lee, Ruth, Grant and any number of other folks see a need for collaboration between quant and qual – so that we can get the right people, with the right tools, looking at different aspects of a development intervention to rigorously establish the existence of an impact, and the establish an equally rigorous understanding of the causal processes by which that impact came to pass.  Nothing terribly new here, I think.  Except, of course, for my continued claim that the qualitative work I do see associated with RCT work is mostly awful, tending toward bad journalism (see my discussion of bad journalism and bad qualitative work in the first post).

But this discussion misses a much larger point about epistemology – what I intended to write in this second part of the series all along.  I do not see the dichotomy between measuring “if something works” and establishing “why something worked” as analytically valid.  Simply put, without some (at least hypothetical) framing of causality, we cannot rigorously frame research questions around either question.  How can you know if something worked, if you are not sure how it was supposed to work in the first place?  Qualitative research provides the interpretive framework for the data collected via RCT4D efforts – a necessary framework if we want RCT4D work to be rigorous.  By separating qualitative work from the quant oriented RCT work, we are assuming that somehow we can pull data collection apart from the framing of the research question.  We cannot – nobody is completely inductive, which means we all work from some sort of framing of causality.  The danger is when we don’t acknowledge this simple point – under most RCT4D work, those framings are implicit and completely uninterrogated by the practitioners.  Even where they come to the fore (Duflo’s 3 I s), they are not interrogated – they are assumed as framings for the rest of the analysis.

If we don’t have causal mechanisms, we cannot rigorously frame research questions to see if something is working – we are, as Marc says, “like the drunk looking for his car keys under the street lamp when he knows he lost them elsewhere, because the only place he can actually see is under the street lamp.”  Only I would argue we are the drunk looking for his keys under a streetlamp, but he has no idea if they are there or not.

In short, I’m not beating up on RCT4D, nor am I advocating for more conversation – no, I am arguing that we need integration, teams with quant and qual skills that frame the research questions together, that develop tests together, that interpret the data together.  This is the only way we will come to really understand the impact of our interventions, and how to more productively frame future efforts.  Of course, I can say this because I already work in a mixed-methods world where my projects integrate the skills of GIScientists, land use modelers, climate modelers, biogeographers and qualitative social scientists – in short, I have a degree of comfort with this sort of collaboration.  So, who wants to start putting together some seriously collaborative, integrated evaluations?

Those following this blog (or my twitter feed) know that I have some issues with RCT4D work.  I’m actually working on a serious treatment of the issues I see in this work (i.e. journal article), but I am not above crowdsourcing some of my ideas to see how people respond.  Also, as many of my readers know, I have a propensity for really long posts.  I’m going to try to avoid that here by breaking this topic into two parts.  So, this is part 1 of 2.

To me, RCT4D work is interesting because of its emphasis on rigorous data collection – certainly, this has long been a problem in development research, and I have no doubt that the data they are gathering is valid.  However, part of the reason I feel confident in this data is because, as I raised in an earlier post,  it is replicating findings from the qualitative literature . . . findings that are, in many cases, long-established with rigorously-gathered, verifiable data.  More on that in part 2 of this series.

One of the things that worries me about the RCT4D movement is the (at least implicit, often overt) suggestion that other forms of development data collection lack rigor and validity.  However, in the qualitative realm we spend a lot of time thinking about rigor and validity, and how we might achieve both – and there are tools we use to this end, ranging from discursive analysis to cross-checking interviews with focus groups and other forms of data.  Certainly, these are different means of establishing rigor and validity, but they are still there.

Without rigor and validity, qualitative research falls into bad journalism.  As I see it, good journalism captures a story or an important issue, and illustrates that issue through examples.  These examples are not meant to rigorously explain the issue at hand, but to clarify it or ground it for the reader.  When journalists attempt to move to explanation via these same few examples (as far too often columnists like Kristof and Friedman do), they start making unsubstantiated claims that generally fall apart under scrutiny.  People mistake this sort of work for qualitative social science all the time, but it is not.  Certainly there is some really bad social science out there that slips from illustration to explanation in just the manner I have described, but this is hardly the majority of the work found in the literature.  Instead, rigorous qualitative social science recognizes the need to gather valid data, and therefore requires conducting dozens, if not hundreds, of interviews to establish understandings of the events and processes at hand.

This understanding of qualitative research stands in stark contrast to what is in evidence in the RCT4D movement.  For all of the effort devoted to data collection under these efforts, there is stunningly little time and energy devoted to explanation of the patterns seen in the data.  In short, RCT4D often reverts to bad journalism when it comes time for explanation.  Patterns gleaned from meticulously gathered data are explained in an offhand manner.  For example, in her (otherwise quite well-done) presentation to USAID yesterday, Esther Duflo suggested that some problematic development outcomes could be explained by a combination of “the three I s”: ideology, ignorance and inertia.  This is a boggling oversimplification of why people do what they do – ideology is basically nondiagnostic (you need to define and interrogate it before you can do anything about it), and ignorance and inertia are (probably unintentionally) deeply patronizing assumptions about people living in the Global South that have been disproven time and again (my own work in Ghana has demonstrated that people operate with really fine-grained information about incomes and gender roles, and know exactly what they are doing when they act in a manner that limits their household incomes – see here, here and here).  Development has claimed to be overcoming ignorance and inertia since . . . well, since we called it colonialism.  Sorry, but that’s the truth.

Worse, this offhand approach to explanation is often “validated” through reference to a single qualitative case that may or may not be representative of the situation at hand – this is horribly ironic for an approach that is trying to move development research past the anecdotal.  This is not merely external observation – I have heard from people working inside J-PAL projects that the overall program puts little effort into serious qualitative work, and has little understanding of what rigor and validity might mean in the context of qualitative methods or explanation.  In short, the bulk of explanation for these interesting patterns of behavior that emerges from these studies resorts to uninterrogated assumptions about human behavior that do not hold up to empirical reality.  What RCT4D has identified are patterns, not explanations – explanation requires a contextual understanding of the social.

Coming soon: Part 2 – Qualitative research and the interpretation of empirical data