The basics of research are seemingly clear. Read a lot of articles, see what’s missing, and conduct research to fill the gap in the literature. Wait a minute. What is that? ‘See what’s missing?’ How can we see something that is not there?
Imagine you are videoconferencing a colleague who is showing you the results of their project. Suddenly, the screen and sound cut out for a minute. After pressing some keys, you manage to restore the link; only to have your colleague ask, ‘What do you think?’. Of course, you know that you missed something from the presentation because of the disconnection. You can see that something is missing, and you know what to ask for to get your desired results, ‘Sorry, could you repeat that last minute of your presentation, please’. It’s not so easy when we’re looking at research results, proposals, or literature reviews.
While all research is, to some extent, useful, we’ve seen a lot of research that does not have the expected impact. That means wasted time, wasted money, under-served clients, and frustration on multiple levels. A big part of that problem is that directions for research are often chosen intuitively; in a sort of ad-hoc process. While we deeply respect the intuition of experts, that kind of process is not very rigorous.
In this post, we will show you how to ‘see the invisible’: How to identify the missing pieces in any study, literature review, or program analysis. With these straight-forward techniques, you will be able to better target your research in a more cost-effective way to fill those knowledge gaps to develop more effective theories, plans, and evaluations.
The first step is to choose your source material. That can be one or more articles, reports, or other study results. Of course, you want to be sure that the material you use is of high quality. Next, you want to create a causal map of your source material.
We’re going to go a bit abstract on you here because people sometimes get lost in the ‘content’ when what we are looking at here is more about the ‘structure’. Think of it like choosing how to buy a house based on how well it is built, rather than what color it is painted. So, instead of using actual concepts, we’ll refer to them as concepts A, B, C… and so on.
So, the text might say something like: ‘Our research shows that A causes B, B causes C, and D causes less C. Oh yes, and E is also important (although we’re not sure how it’s causally connected to A, B, C, or D)’.
When we draw causal maps from the source material we’ve found, we like to have key concepts in circles, with causal connections represented by arrows.
Figure 1. Abstract example of a causal map of a theory
There are really three basic kinds of gaps for you to find: relevance/meaning, logic/structure, and data/evidence. Starting with structure, there is a gap any place where there are two circles NOT connected by a causal arrow. It is important to have at least two arrows pointing at each concept/circle for the same reason we like to have multiple independent variables for each dependent variable (although, with more complex maps, we’re learning to see these as interdependent variables).
For example, there is no arrow between A and D. Also, there is no arrow between E and any of the other concepts. Each of those is a structural gap – an opening for additional research.
You might also notice that there are two arrows pointing directly at C. Like having two independent variables and one dependent variable, it is structurally better to have at least two arrows pointing at each concept.
So, structurally, C is in good shape. This part of the map has the least need for additional research. A larger gap exists around B, because it has only one arrow pointing at it (the arrow from A to B). Larger still is the gap around A, D, and E; because they have no arrows pointing at them.
To get the greatest leverage for your research dollar, it is generally best to search for that second arrow. In short, one research question would be: What (aside from A) has a causal influence on B? Other good research questions would be (a) Is there a causal relationship between A and D? (b) Is there a causal relationship between E and any of the other concepts? (c) What else besides A helps cause B? (d) What are the causes of A, D, and E?
Now, let’s take a look at gaps in the data, evidence, or information upon which each causal arrow is established.
From structure to data
Here, we add to the drawing by making a note showing (very briefly) the kind of data supporting each causal arrow. We like to have that in a box – with a loopy line ‘typing’ the evidence to the connection. You can also use different colors to more easily differentiate between the concepts and the evidence on your map. You can also write the note along the length of the arrow.
Figure 2. Tying the data to the structure
From data to stakeholder relevance
Finally, the gap in meaning (relevance) asks if those studies were done with the ‘right’ people. By this, we mean people related to the situation or topic you are studying. Managers, line workers, clients, suppliers, those providing related services; all of those and more should be included. Similarly, you might look to a variety of academic disciplines, drawing expertise from psychology, sociology, business, economics, policy, and others.
Which participants or stakeholders are actually part of your research depends on the project. However, in general, having a broader selection of stakeholder groups results in a better map. This applies to both choosing what concepts go on the map and also who to contact for interviews and surveys.
Visualizing the gaps
All of these three gaps – gaps in structure, data, and stakeholder perspectives – can (and should) be addressed to help you choose more focused directions for your research – to generate research results that will have more impact. As a final note, remember that many gaps may be filled with secondary research; a new literature review that fills the gaps in the logic/structure, data/information, and meaning/relevance of your map so that your organisation can have a greater impact.
Figure 3. Visualizing the gaps (shown in green)
Some deeper reading on literature reviews may be found here:
- Practical Mapping for Applied Research and Program Evaluation (SAGE) provides a ‘jargon free’ explanation for every phase of research:
- This paper uses theories for addressing poverty from a range of academic disciplines and from policy centers from across the political spectrum as an example of interdisciplinary knowledge mapping and synthesis:
- Restructuring evaluation findings into useful knowledge:
This approach helps you to avoid fuzzy understandings and the dangerous ‘pretence of knowledge’ that occasionally crops up in some reports and recommendations. Everyone can see that a piece is missing and so more easily agree where more research is needed to advance our knowledge to better serve our organisational and community constituents.