3.3 Organising data: labelling and coding

After documenting your data in detail, you will need to organise it to make it accessible for analysis. This means labelling all data, so that you know where it is from and how it was collected.

At this stage you also start to code your data, so that you can start to think with your data, to listen to what your data is telling you.

Step 1: labelling Data

A label is given to each item of data (you label each tape recorded interview, each interview transcript, each field note, and so on) so that at any point during your research you and your research team can always see what it is and where it comes from. Every single piece of data you collect must be labelled.

Labelling means recording what kind of research data it is (interview transcript etc), where it was collected, when it was collected or written, and who it involves. Labels need to be clear enough for you and others to find when you are sorting through large amounts of data.

Below are two examples of work that has been clearly labelled.

Labelling Example 1

Community Mapping
10 October 2006
Deepak Koirala
With the assistance of community people of Madhawaliya CLC
In the Madhawaliya CLC

Labelling Example 2

Discussion of the role of Medias
Field Visit 20th July 2007
Date Notes Written 25th July 2007
Location- Tansen Municipality Hall
Author- GPA

Step 2: Coding data

A Code is your way of organising the data in terms of its subject matter. You will use many codes, some general, and some more specific. For instance, a general code might be 'education' and you could use it to identify data that is relevant to education. A more specific code might be 'informal education' which you use because the data refers to non-traditional education forms. An EAR researcher will go through their data in detail, coding it according to the types of issues that emerge.

For example, the codes 'education' and 'health' will probably be relevant to most research at some point. By coding data as 'education' or 'health' you are marking it in a way that means you can return to it later, knowing that this particular piece of data is about 'education' or 'health' (it could be about both, in which case you will have applied both codes to it). In this way the code will help you to identify relevant bits of data that you can pull together later to say something about 'education' and/or 'health'.

Coding is more than simply organising data. Coding also helps you to analyse it and work out what the data is telling you. As research develops you will define many codes, building up an increasingly detailed understanding of the data.

There are two important reasons for coding:

  1. You can organise your data through codes - It is easier to find data relevant to certain themes. For example, you can easily find all data related to 'education' if you have marked it  through coding.
  2. You can explore your date through codes - It is easier to think with your data - for example, you might want to answer the question 'what are local attitudes to education amongst different groups in this place?' Reading through and thinking about your data coded 'education', and perhaps 'local attitudes' will help you answer this question.

Once you have coded some of your data you can pull out all data that you have coded as 'education', or related codes such as 'training', 'learning', 'school' or 'college'. This is very useful because you can look across a range of data collected using different tools, and from a range of respondents. However, you will need to be able to see at this point where each piece of data comes from . This is why it is important that each piece of data is properly and clearly labelled.

It is not enough to collect together all data relevant to 'education' without being able to consider where each piece of data comes from and how it was generated. For example, it could be that the only place where education comes up is in interviews with school teachers. Whilst this is unlikely, it serves to demonstrate that unless you know that it is only school teachers who talk about 'education' in your research; you may conclude that these views about education represent the views of the wider community, which they may not.

So, if you extract a paragraph from your field note, or from an interview transcript always label it. In this way you will know who said what, who to attribute what opinion or action to, and you will be considering all data in the contexts in which it was generated and collected.
Author- GPA

Coding involves interpretation and exploration of your material. Different researchers may code the same material differently. Moreover, the same researcher will often change their codes over time, as their ideas, and their material, develops. The codes you use will depend on what is important to the people you are studying; what is interesting to you and your initiative; and also what you understand is going on.

Coding is an important step in the process of analysing your material and should be done as you gather your data and not at the 'end' of the research.