In research, a code is a ‘label.’ It is generally a word—but can also be a short phrase—that symbolically assigns an important, essence-capturing, and/or evocative attribute of a piece of data. That piece of data can be a text, a line from a text, a word, or even a visual piece of data such as an image, video, etc.
Codes are assigned to such kinds of data that constitute interview transcripts, participant observation field notes, journals, documents, literature, artefacts, photographs, videos/images, websites, e-mail correspondence, etc.
The term coding deduced from the Greek meaning 'to discover,’ refers to a process in research where codes are assigned to the data. The codes are labels. And the parts of data they are assigned are responses gathered from respondents/participants.
Note: In research, 'participant' and 'respondent' are not the same, even though in some places, they might be used interchangeably. The former is used when the research tool is a survey, questionnaire, and the like. But the latter is used when a data collection instrument like an interview has been used.
The product catalogues that grocery stores, as well as other types of market stores, use mostly have a list of categories. Those ‘categories’ in themselves are labels. To keep it simple, one can say those labels or categories are codes. And each of those codes represents the items that are categorized under that label alone.
For instance, the category of 'toiletries' will contain only those products that are related to personal care. Similarly, the category of 'disposables' will only contain plastic, disposable items within it and not, let's say, fruits or vegetables.
Now that the basic principle behind coding is clear, it can be redefined as a transitional process between data collection and a more extensive data analysis. Once the responses have been gathered, they are then analysed and coded.
Two main sub-processes constitute coding itself and they are decoding and encoding.
Before coding any piece of data, it is important to keep the following two characteristics in mind:
In larger, complete data sets, some or even many of the same codes are used repeatedly throughout. This is both natural and intentional. It is natural in the sense that there are many repetitive patterns of action and consistencies in human affairs. And it is deliberate because one of the coder’s primary goals is to find these repetitive patterns of action and consistencies in human affairs as documented in the data.
Coding follows three steps:
Codes are grouped to develop categories. When one searches for patterns in coded data to categorize them, they might sometimes group things not just because they are exactly or very much alike, but because they might also have something in common. Paradoxically, that commonality might consist of differences, even.
For example, every individual from a specific region might have a strong opinion about who should be leading that specific region. The fact that everyone has an individual opinion about that issue is a commonality. But as for whom everyone believes should be leading the country, that is where differences might occur.
Therefore, while assigning categories to codes, the following trends in coding should be kept forefront (Hatch, 2002, p. 155):
The majority of qualitative researchers will code their data both during and after data collection. This is an analytic tactic, for coding is analysis (Miles & Huberman, 1994, p. 56).
There are two very popular ways to go about the process of coding. They are:
In a priori coding, codes are developed beforehand; they are pre-determined. But in open coding, as the name suggests, a researcher comes up with codes at the time of data analysis. They are not predetermined. Prior knowledge, read literature, and theories all help in the development of a priori codes, categories and themes.
Furthermore, a priori coding:
Step #1: Read and analyse previous literature.
Step #2: Develop codes accordingly.
Step #3: Look for chunks within data that fit a given code. Apply the code. Leave the rest of the data which cannot be assigned a code.
On the other hand, open coding is very extensive and time-consuming. It involves assigning codes, revisiting them to identify categories (axial coding) and then developing themes from there on out. Prior assumptions about the topic are left behind; the data in front of the researcher does the thinking. It guides the formation of codes itself.
It is also called emergent coding for as the name suggests, codes ‘emerge’ at the time of data analysis. No piece of information from within the data is left out. This way, every kind of response—especially in the case of interviews, reports, etc.—gets acknowledged and reported later during data interpretation.
Open coding is analytical and inductive. It codes using two main tasks: by making comparisons and by asking questions. This is why grounded theory is mostly termed as the ‘constant comparative method of analysis’ (Glaser & Strauss, 1967).
Step #1: 1. Convert the data into small, discrete ‘chunks.’
Step #2: Assign an appropriate label for each chunk.
The same code can be used again for similar chunks.
Suppose a study aims at answering the following question:
“What happens to undergraduate students’ environmental practices when they are engaged in repeated discussions on lifestyles and environmental problems?”
To answer the question, data is gathered from undergraduate students who had participated in repeated discussions on lifestyles and environmental problems. Then coding begins. It can either be done via open coding or via a priori coding. Both are discussed below:
In this case, a researcher might want to code certain words, phrases, or sentences from the respondent's answers based on the concept they closely reflect. They can be based on the following codes:
Thinking of1 and talking about2 our
consumer patterns3 made me understand4 the relationship between consumption and environmental impact. I have decided to buy5 only those things which are necessary6.
2, 3 Talk on consumer patterns
4 Understanding the environmental impact
5, 6 Decisions about purchasing necessary items
Questioning and discussions7 on our
lifestyles8 has really raised our awareness about our own actions9 which impact the environment. We had never experienced such discussions in our schools or colleges.
7 Discussion on lifestyles
8, 9 Awareness about personal environmental actions
A priori coding
In this case, the researcher might decide to code the data about undergraduate students engagement in repeated discussion on environmental issues in terms of the following themes:
Thinking of and talking about our consumer patterns made me understand the relationship between consumption and environmental impact. I have decided to buy only those things which are really necessary. I will save energy and plant trees.
Questioning and discussions on our lifestyles have really raised our awareness about our own actions and globalization. We had never experienced such discussions in our schools or colleges.
Awareness about globalisation