Building a conceptual framework is one of the first steps to take when building a composite indicator or a scoreboard. This needs to be done carefully, so let’s walk through it.
Let’s first clarify what a conceptual framework actually is. As we have seen previously, composite indicators usually target multidimensional concepts. A conceptual framework is essentially a mapping of your concept, identifying dimensions, sub-dimensions, and so on.
To take an example, see the conceptual framework below of the Times Higher Education World University Rankings.
In this framework, the developers identified five dimensions which comprise in their opinion the “excellence” of a university: Teaching, Research, Citations, International Outlook and Industry Income. Each of the dimensions is populated with a group of indicators which aim to represent that dimension. Notice that this framework has three levels – the indicator level, the dimension level, and the index level (we will discuss how indicators are used to calculate scores for higher levels shortly).
Other indexes are more complex. The Lowy Institute Asia Power Index framework, pictured below, is comprised of eight “measures” which are in turn comprised of 30 “submeasures” and 131 underlying indicators. This framework has four levels.
So how do we actually arrive at a conceptual framework? In theory (and we’ll get to some practical issues in a moment) a conceptual framework is a deconstruction of a multidimensional concept.
The first step is to understand as well as possible what the concept you are aiming to measure actually means, and what it is made up of. This can be done by reviewing any existing (academic) literature on the topic, by talking to experts on the topic (e.g. in a workshop if possible) and surveying any existing indexes or measurement frameworks that measure your concept or something similar to it. If you yourself are already an expert on your concept then this certainly helps!
By doing this, some main components of your concept should become clear: let’s call these dimensions . Now each dimension has to be carefully examined – does it have clear sub-dimensions? If so, these can also be added to your conceptual framework. The sub-dimensions themselves may have sub-sub-dimensions, and so on.
But when to stop deconstructing? There are a few considerations. First, we want each component of our lowest level of the framework to be reasonably measurable with a (smallish) group of indicators. The whole point of the deconstruction process is to break down our complex concept into smaller measurable chunks.
Next, like many things in life there is a trade off between accuracy and complexity. Perhaps having seven levels in your framework might be the strictest representation of the concept, but it would probably be bewildering to users! To the extent possible, try to aim for simplicity.
Last, consider that dimensions and sub-dimensions will themselves have scores as a result of aggregating indicators. If you think users would be interested in these scores, then this might be a good reason to keep them. If they are less relevant or useful, maybe the concept does not need to be so finely deconstructed.
The idea of deconstruction is a common technique in modelling a complex system – finite element analysis, for example, allows structural analysis of very complex engineering designs by breaking them into smaller, more manageable elements.
So this all sounds fantastic, but in of course in reality it is not that straightforward! There are plenty of practical issues that get in the way of your dream framework.
In the first place, it is not always so easy to get a clear and agreed definition of what your concept actually means. In some cases there may be a widely-accepted definition (e.g. “sustainable development” is reasonably encapsulated by the UN’s Sustainable Development Goals), but often this is not the case. Different sources will define the concept, and its components, differently. Even experts will often disagree. This is simply a manifestation of the fact that complex concepts are, well, complex. And complexity brings uncertainty.
A further problem is that even when the main components of a concept are known, there may be different conceivable ways to group them, depending on the functionality of the index. To take an example, a few years ago I was working on the ASEM Sustainable Connectivity Indexes, which aim to measure sustainable international connections between Asian and European countries. We grappled for rather a long time with how to capture this: should only sustainable forms of connectivity be included? What comprises “sustainable” in this context? Social sustainability often runs against environmental sustainability. At one point we considered a framework distinguishing between “Connectivity enablers” and “Connectivity outcomes”. In the end, after many rounds of consultation with experts, we settled on a simpler framework mapping the main components of connectivity, with a separate “sustainability” index.
The point here is that there were many potential ways of decomposing the same concept!
Last, but absolutely not least, is the issue of data availability. While we can certainly arrive at a “theoretical” conceptual framework using the ideas above, if we want to actually populate this with indicators, we will probably have to make adjustments. Quite often, data is lacking for key components of the framework. There is little point including a sub-dimension for which little or no data is available, and the final conceptual framework of the index will almost always be shaped by which indicators are practically available. That said, pointing to data gaps can also be a useful strategy for spurring future data collection.
At the end of the day, the final framework will be a compromise between what is theoretically desired, and what is practically possible.
Having picked the concept into tiny pieces, we can now put it back together.
We begin with the lowest level in the framework (let’s call them sub-dimensions). Each sub-dimension must be measured with a group of indicators which offers a reasonable representation of that “chunk” of our concept. The process of indicator selection is a tricky business which will be dealt with in another post, but we’ll skip lightly over it for the minute.
Using each group of indicators, we can build a composite indicator which gives a measurement for each sub-dimension. In practice, this is done by normalising indicators (bringing them onto a common scale) and then aggregating them (usually by taking some form of weighted mean). Again, both of these topics require some separate explanation which we’ll deal with in other posts.
Now, our sub-dimension composite indicators can themselves be aggregated to give higher-level composite indicators for each dimension. Finally, the dimensions are aggregated to give the index.
A conceptual framework is a map of our concept. This is usually a hierarchical map, which follows a deconstruction of the concept where dimensions are broken down into sub-dimensions, and so on. After deconstructing the concept, we reconstruct it with indicators, up the index. The idea being, of course, that the index should be a reasonable measure of the multidimensional concept.
As with many things, the final framework is a trade off between various considerations, including:
- Accuracy –mapping the concept as thoroughly as possible
- Simplicity – creating a map that is still interpretable by users and not overwhelming
- Usability – the end result should include components that are actually of interest to users
- Measurability – the components of our map should be measurable, otherwise they cannot be included in the index
- Acceptability/credibility – the map should agree with expert opinion and established literature on the topic
Look out for more blog posts on these topics and please contact us if you need help with your composite indicator!