Transparent Analysis Plans

Over the past years, I have read quite a few guidance documents on how to do M&E. Looking back at this literature, one thing that strikes me is how little attention is given to data analysis, relative to data collection. There are gaps, both in (a) guidance on "how to do it"  and (b) how to be transparent and accountable for what you planned to do and then actually did. In this blog, I want to provide some suggestions that might help fill that gap.

But first a story, to provide some background. In 2015 I did some data analysis for a UK consultancy firm. They had been managing a "Challenge Fund" a grant making facility funded by DFID, for the previous five years, and in the process had accumulated lots of data. When I looked at the data I found sapproximately170 fields. There were many different analyses that could be made from this data, even bearing mind one approach we had discussed and agreed on - the development of some predictive models, concerning the outcomes of the funded projects.

I resolved this by developing a "data analysis matrix", seen below. The categories on the left column and top row referred to different sub-groups of fields in the data set. The cells referred to the possibility of analyzing the relationship between the row sub-group of data and the column sub-group of data. The colored cells are those data relationships the stakeholders decided would be analyzed, and the initials in the cells referred to the stakeholder wanting that analysis. Equally importantly, the blank cells indicate what will not be analyzed.

We added a summary row at the bottom and a summary column to the right. The cells in the summary row signal the relative importance given to the events in each column. The cells in the summary column signal the relative confidence in the quality of data available in the row sub-groups. Other forms of meta-data could also have been provided in such summary rows and columns, which could help inform stakeholders choice of what relationships between the data should be analyzed.

A more general version of the same kind of matrix can be used to show the different kinds of analysis that can be carried out with any set of data. In the matrices below, the row and column letters refer to different variables / attributes / fields in a data set. There are three main types of analysis illustrated in these matrices, and three sub-types:

• Univariate - looking at one measure only
• Bivariate - looking at the relationships between two measures
• Multivariate - looking at the relationship between multiple measures

But within the multivariate option there three alternatives, to look at:

• Many to one relationships
• One to many relationships
• Many to many relationships

On the right side of each matrix below, I have listed some of the forms of each kind of analysis.

What I am proposing is that studies or evaluations that involve data collection and analysis should develop a transparent analysis plan, using a "data analysis matrix" of the kind shown above. At a minimum, cells should contain data about which relationships will be investigated.  This does not mean investigators can't change their mind later on as the study or evaluation progresses.  But it does mean that both original intentions and final choices will be more visible and accountable.

Postscript: For details of the study mentioned above, see LEARNING FROM THE CIVIL SOCIETY CHALLENGE FUND: PREDICTIVE MODELLING Briefing Paper. September 2015

...and then a miracle happens (or two or three)

Many of you will be familiar with this cartoon, used in many texts on the use of Theories of Change

If you look at diagrammatic versions of Theories of Change you will see two type of graphic elements: nodes and links between the nodes. Nodes are always annotated, describing what is happening at this point in the process of change. But the links between nodes are typically not annotated with any explanatory text. Occasionally (10% of the time in the first 300 pages of Funnell and Rogers book on Purposeful Program Theory) the links might be of different types e.g. thick versus thin lines or dotted versus continuous lines. The links tell us there is a causal connection but rarely do they tell us what kind of causal connection is at work. In that respect the point of Sidney Harris's cartoon applies to a large majority of graphic representations of Theories of Change.

In fact there are two type of gaps that should be of concern. One is the nature of individual links between nodes. The other is how a given set of links converging on a node work as a group, or not, as it may be. Here is an example from the USAID Learning Lab web page. Look at the brown node in the centre, influenced by six other green events below it

 In this part of the diagram there are a number of possible ways of interpreting the causal relationships between the six green events underneath the brown event they all connect to:

The first set are binary possibilities, where the events are or are not important:

1. Some or all of these events are necessary for the brown event to occur.
2. Some of all of the events are sufficient for the brown event to occur
3. None of the events are necessary or sufficient but two or more of combinations of these are sufficient

The fourth is more continuous
4. The more of these events that are present (and the more of each of these) the more the brown event will be present
5. The relationship may not be linear, but exponential or s-shaped or more complex polynomial shapes (likely if there are feedback loops present)

These various possibilities have different implications for how this bit of the Theory of Change could be evaluated. Necessary or sufficient individual events will be relatively easy to test for. Finding combinations that are necessary or sufficient will be more challenging, because there potential many (2^5=32 in the above case). Likewise finding linear and other kinds of continuous relationships would require more sophisticated measurement. Michael Woolcock (2009) has written on the importance of thinking through what kinds of impact trajectories our various contextualised Theories of Change might suggest we will find in this area.

Of course the gaps I have pointed out are only one part of the larger graphic Theory of Change shown above. The brown event is itself only one of a number of inputs into other events shown further above, where the same question arises about how they variously combine.

So, it turns out that Sydney Harris's cartoon is really a gentle understatement of how much more we really need to specify before we can have an evaluable Theory of Change on our hands.