Musings on research, international development and other stuff


Make love not war: bringing research rigour and context together

I’ve just spent a few days in Indonesia having meetings with some fascinating people discussing the role of think tanks in supporting evidence-informed policy. It was quite a privilege to spend time with people who had such deep and nuanced understanding of the ‘knowledge sectors’ in different parts of the world (and if you are interested in learning more, I would strongly recommend you check out some of their blogs here, here and here).

However, one point of particular interest within the formal meetings was that research quality/rigour often seemed to be framed in opposition to considerations of relevance and context. I was therefore interested to see that Lant Pritchett has also just written a blog with essentially the same theme – making the point that research rigour is less important than contextual relevance.

I found this surprising – not because I think context is unimportant – but because I do not see why the argument needs to be dichotomous. Research quality and research relevance are two important issues and the fact that some research is not contextually relevant does not in any way negate the fact that some research is not good quality.

How not to move a discussion forward

To illustrate this, let’s consider a matrix comparing quality with relevance.

Low Quality High Quality
Low contextual understanding The stuff which I think we can all agree is pointless Rigorous research which is actually looking at   irrelevant/inappropriate questions due to poor understanding of context
High contextual understanding Research which is based on deep understanding of context   but which is prone to bias due to poor methodology The good stuff! Research which is informed by good contextual understanding and which uses high quality methods to investigate   relevant questions.

Let me give some examples from each of these categories:

Low quality low contextual understanding

I am loath to give any examples for this box since it will just offend people – but I would include in this category any research which involves a researcher with little or no understanding of the context ‘parachuting in’ and then passing off their opinions as credible research.

High quality, low contextual understanding

An example of this is here – a research study on microbicides to prevent the transmission of HIV which was carried out in Zambia. This research used an experimental methodology – the most rigorous approach one can use when seeking to prove causal linkages. However the qualitatitve research strand which was run alongside the trial demonstrated that due to poor understanding of sexual behaviours in the context they were working in, the experimental data were flawed.

Low quality, high contextual understanding

An example of this is research to understand the links between investment in research and the quality of university education which relies on interviews and case studies with academics. These academics have very high understanding of the context of the university sector and you can therefore see why people would choose to ask them this questions. However repeated studies show that academics almost universally believe that investment in research is crucial to drive up the quality of education within universities while repeated rigorous empirical studies, reveal that the relationship between research and education quality is actually zero.

High quality, high contextual understanding

An example here could be this set of four studies of African policy debates. The author spent extended periods of time in each location and made every effort to understand the context – but she also used high quality qualitative research methods to gather her data. Another example could be the CDD paper I have blogged about before where an in-depth qualitative approach to understand context was combined with a synthesis of high-quality experimental research evidence. Or the research described in this case study – an evaluation carried out in Bolivia which demonstrates how deep contextual understanding and research rigour can be combined to achieve impact.

Some organisations will be really strong on relevance but be producing material which is weak methodologically and therefore prone to bias. This is dangerous since – as described above – poor quality research may well give answers – but they may be entirely the wrong answers to the questions posed. Other organisations will be producing stuff which is highly rigorous but completely irrelevant. Again, this is at best pointless and at worst dangerous if decision makers do not recognise that it is irrelevant to the questions they are grappling with.

In fact, the funny thing is that when deciding whether to concentrate more on improving research relevance or research quality… context matters! The problem of poor quality and the problem of low contextual relevance both occur and both reduce the usefulness of the research produced – and arguing about which one is on average more damaging is not going to help improve that situation.

One final point that struck me from reading the Pritchett blog is that he appears to have a fear that a piece of evidence which shows that something works in one context will be mindlessly used to make the argument that the same intervention should be used in another. In other words, there is a concern that rigorous evidence will be used to back up normative policy advice. If evidence were to be used in that way, I would also be afraid of it – but that is fundamentally not what I consider to be evidence-informed policy making. In fact, I disagree that any research evidence ever tells anyone what they should do. Thus, I agree with Pritchett that evidence of the positive impact of low class sizes in Israel does not provide the argument that class sizes should be lowered in Kenya. But I would also suggest that such evidence does not necessarily mean that policy makers in Israel should lower class sizes. This evidence provides some information which policy makers in either context may wish to consider – hence evidence-informed policy making. The Israeli politicians may come to the conclusion that the evidence of the benefit of low class sizes is relatively strong in their context. However, they may well make a decision not to lower class sizes due to other factors – for example finances. I would still consider this decision to be evidence-informed. Conversely, the policy makers in Kenya may look at the Israeli evidence and conclude that it refers to a different context and that it may therefore not provide a useful prediction of what will happen in Kenya – however, they may decide that it is sufficient to demonstrate that in some contexts lower class sizes can improve outcomes and that that is sufficient evidence for them to take a decision to try the policy out.

In other words, political decisions are always based on multiple factors – evidence will only ever be one of them. And evidence from alternative contexts can still provide useful information – providing you don’t overinterpret that information and assume that something that works in one context will automatically transfer to another.


An idiot’s guide to research methods

I want to start by excusing the title – I am, of course, not implying that you are idiots. I have however been carrying out a lot of interviews recently and I noticed that many people – even some people working in the area of research and international development – get a bit muddled up about different types of research methods. In particular, quite a few people think that ‘quantitative’ research is synonymous with ‘experimental’ research. Not so. In fact, as I have discussed before, qualitative/quantitative and experimental/non-experimental are two different axes. For ease, I have created a wee table to demonstrate this below.

Sorry, I know that this is a deeply nerdy blog post and I promise to try to make the next one more entertaining. On the other hand, at least if you are going to have a job interview with a mean person like me who might ask you to describe different types of research, you now have a handy crib sheet! You are welcome.

research methods*Non-experimental approaches are also described by some (including DFID – see here) as ‘observational’. However (with thanks to @UCLanPsychology for pointing this out to me), be aware that this can cause confusion since some people use the term ‘observational’ to describe a way of collecting information (in contrast to other ways such as self-reporting or direct measurement) in both experimental and non-experimental approaches.