Friday, March 04, 2016

Why we should also pay attention to "what does not work"

There is no shortage of research on poverty and how people become poor and often remain poor.

Back in the 1990s (ancient times indeed, at least in the aid world :-) a couple of researchers in Vietnam were looking at the nutrition status of children in poor households. In the process they came across a small number of households where the child was well nourished, despite the household being poor. The family's feeding practices were investigated and the lessons learned were then disseminated throughout the community. The existence of such positive outliers from a dominant trend was later called "positive deviance" and this subsequently became the basis of large field of research and development practice. You can read more on the Positive Deviance Initiative website

From my recent reading of the work done by those associated with this movement the main means that has been used to find positive deviance cases has been participatory investigations by the communities themselves. I have no problem with this.

But because I have been somewhat obsessed with the potential applications of predictive modeling over the last few years I have wondered if the search for positive deviance could be carried out on a much larger scale, using relatively non-participatory methods. More specifically, using data mining methods aimed at developing predictive models. Predictive models are association rules that perform well in predicting an outcome of interest. For example, that projects with x,y,z attributes in contexts with a,b, and c attributes will lead to project outcomes that are above average in achieving their objectives.

The core idea is relatively simple. As well as developing predictive models of what does work (the most common practice) we should also develop predictive models of what does not work. It is quite likely that many of these models will be imperfect, in the sense that there are likely to be some False Positives. In this type of analysis FPs will be cases where the development outcome did take place, despite all the conditions being favorable to it not taking place. These are the candidate "Positive Deviants" which would then be worth investigating in detail via case studies, and it is at this stage that participatory methods of inquiry would then be appropriate.

Here is a simple example, using some data collated and analysed by Krook in 2010, on factors affecting levels of women's participation in parliaments in Africa. Elsewhere in this blog I have shown how this data can be analysed using Decision Tree algorithms, to develop predictors of when womens' participation will be high versus low. I have re-presented the Decision Tree model below
In this predictive model the absence of quotas for women in parliament is a good predictor of low levels of their participation in parliaments. 13 of the 14 countries with no quotas have low levels of women's participation. The one exception, the False Positive of this prediction rule and an example of "positive deviance", is the case of Lesotho, where despite the absence of quotas there is a (relatively) high level of women's participation in parliament. The next question is why so, and then whether the causes are transferable to other countries with no quotas for women. This avenue was not explored in the Krook paper, but it could be a practically useful next step.

Postscript: I was pleased to see that the Positive Deviance Initiative website now has a section on the potential uses of predictive analytics (aka predictive modelling) and they are seeking to establish some piloting of methods in this area with other interested parties

1 comment:

  1. Yes, good idea to learn from what has not worked, whether in developing a predictive model or planning a way to improve on what others have done.