Concern’s 2016-2020 Strategic Plan: fun analysis with NVivo

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In the last post I provided a technical analysis of Concern’s 2016-2020 Strategic Plan.  To do this, I also poked around at the plan using NVivo, a programme I use for conducting qualitative analysis.  This post shows you some of the results and explains their implications.


NVivo contains a broad suite of tools, some of which can be quite fun and quite useful.  You can perform a ‘word frequency’ query to see which words were used most frequently, and turn those results into a word cloud.  This is the word frequency query I generated from the 2016-2010 strategic plan:

Strategic plan word cloud

This is a quick way to understand what a piece of text is about.  In this case, the words ‘Concern’, ‘poverty’, ‘humanitarian’, ‘poverty’, and ‘development’ stand out.

You can also download the word frequency query results and do some basic analysis of the words in excel:

word frequency analysis

The screenshot shows some information about the top 29 words used in the strategic plan.  At 71 mentions, ‘Concern’ is the most widely used word, followed by ‘development’, ‘work’, and ‘people’.  This makes sense: the document is about Concern, which is an organisation that does development work with people.  I’ve categorised the words by what parts of speech they fall under, and whether they are referring to a specific actor, intervention, or problem. You can access the word query results here.


These two assessments can be used to understand what is and is not being talked about within a piece of text, so help provide some quantitative evidence as to a text’s focus.  I can state that the document talks a lot about Concern (the first word on the list), the type of work it does (development is second on the list, poverty is fifth, and humanitarian and emergencies are 12th and 13th).  A few previous issues related to gender, equality, and rights fail to make the top 29: in fact out of all the related words, inequality is the only related word to be used more than 5 times, being used 6 times across the document.   This suggests inequality and rights falling off Concern’s focus.


This function provides a big picture view of the text, but you can also use NVivo to dig deeper.  From a quick read of the plan, I identified conflict, fragility, and the private sector as three areas the might warrant further investigation.  I performed a ‘text search’ query for each of these terms, a function used to bring up all the times a term appears in a document.  I then used the word tree function, a sub-function of the text search query, to visualise these sentences.  Let’s look at ‘conflict’:



The conflict word tree shows conflict is used broadly. It is described as a challenge (with sentence fragments like ‘homes in the wake of context’ and ‘displaced by conflict’) and an organisational intervention (‘conflict competency’, ‘conflict management competency’, ‘conflict mitigation’).  This supports my observation that Concern is focusing more on conflict mitigation.

The ‘fragile’ word tree shows something different:

fragile word tree

Fragility is similar to conflict in that it is also used as a description of a context, with Concern working in fragile areas and fragile states, but differs from conflict in that is not a focus of organisational work or area of specific programmatic improvement.  From this, I think it is safe to say that while fragility is important, Concern will not be devoting specific efforts to build competencies in managing fragility specifically, but might instead incorporate it as a broader programming requirement.

I also identified Concern’s relationship with the private sector as an evolving area.  Let’s look at the private sector word tree:

private sector word tree

The word tree includes terms like ‘expand private sector funding’, ‘building new partnerships’, and ‘collaborating with new development actors’, all of which indicate a growing focus on the private sector.  However, the private sector is often noted in relation to other actors including the government, communities, ‘UN, NGOs, global platforms, donors’, and ‘national governments, civil society institutions and researchers’. For me this suggests that while the private sector is an important actor, it is 1) one of many other actors Concern considers important and/or 2) somewhat of an ‘unknown’ actor, with which Concern is unclear as to how to specifically engage with.


Word trees are a fun tool for drilling down into a specific word or phrase, which can be useful after you have the general gist of a piece of text, and word frequency can be a good tool for getting that general gist.  However, these two tools assist in but do not substitute the actual work of analysis. Interpreting the meaning of word frequency and text search queries requires an understanding of how words relate to the broader context of the text and the issue at hand.  This requires background research, a broad reading of related subjects, and detailed and repeated reading of the text under analysis: in other words, classic qualitative research. NVivo can only help a researcher with analysis, it cannot perform the analysis itself.

One Response

  1. Thanks for this Aaron – it’s great to see that word clouds and word trees helped you get a feel for what’s happening in the data. Having fun and exploring your material with a creative eye is often overlooked in the qual literature…but I do agree that these tools work best when combined with a classic qualitative approach.

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