Coding data for qualitative analysis

Ross Woods, 2022-24

Thematic coding is an increasingly common way to analyze documentary data, usually transcripts of interviews or focus groups. As a qualitative methodology, it gives researchers a way to interpret data.

Thematic coding is derived from a research approach called grounded theory. In essence, this is a method of using a comprehensive set of examples to identify patterns, from which the researcher can create a theory. The theory is justified by the range of real examples.

Thematic coding is really only a systematic way of analyzing data to reach a conclusion. It has become increasingly popular in recent years, partly because it looks like a set of steps. However, the latter stages are less procedural and require more thought. It is not actually a set of steps, but is more like a set of phases that can overlap. For example, you can start transcribing and analyzing data as soon as it is collected.

Thematic coding has several advantages. First, the researcher simply has to follow the method. Second, it gives a way to systematically analyze lots of data, such as when writing a longer thesis or a dissertation. Third, the researcher can use it as in stages, giving an opportunity to adapt the method as needed, and perhaps hold more interviews. Fourth, it is easier if teh researcher uses voice-to-text software to transcribe interviews.

It also has several disadvantages. Although it is quite flexible, it probably doesn’t allow much scope for innovation. If you do not use transcription software, it is very time-consuming to transcribe interviews by hand, or quite expensive if you have to pay someone else to do it for you.

A series of stages

Stage 1: Continue keeping a diary

You should already be keeping a written diary of your methodolology, including what you did, why you did it, your methods, and your observations. Your description is essential to your accountability. In principle, it must to be detailed enough to enable someone else to follow your method.

Add records of your reflections to your diary. You can start to informally analyze data it as soon as you start collecting it. You should take notice anything relevant to your research question, for example:

1. Questions on what seems to be important or curious
2. Observations
3. Emerging patterns
4. Questions that aren’t answered yet
5. Outliers and anomalies
6. Specific “turns of phrase,” interesting quotations that best encapsulate a strand of meaning in the data.
7. Contradictions and things that don’t seem to make sense,
8. Suspicions that things are not quite what they appear to be.

In your diary, you should also write down the reasons why you interpreted the data the way you did. The description will probably be quite simple at first, but any later changes or elaborations will be significant because they indicate a better interpretation of the data.

💡 If you are writing a dissertation:

• Follow the methodology plan in your proposal. Consequently, you could eventually copy the relevant parts of your diary into your methodology plan, and edit it into a harmonious, flowing document in past tense. It is then the methodology chapter in your dissertation.
• Write your notes in full sentences so that you can understand them even after you've forgotten the actual situation in which you wrote them. (A list of unexplained topics is not helpful.)
• Bring your diary to meetings with your dissertation supervisor and be prepared to discuss anything in it.

Stage 2: Decide whether or not you will use deductive themes

You can can start formulating deductive themes very early in the whole process, even before before you collect any data. It is quite permissible to derive a set of deductive themes from your literature review or your statement of the research question. However, it would be a mistake to use deductive themes exclusively because other unexpected but significant themes emerge later on in the data.

The other alternative is to use inductive themes, that is, themes that emerge in the data, that you identify as you assign codes and themes. You can even modify your system of inductive themes during data-gathering and analysis in order to get themes that better represent your data.

Stage 3: Let your system evolve

Some of your ongoing analysis might affect your data gathering. Qualitative research is often iterative, and this method allows you to improve your data collection and analysis as you progress:

• If you use free informal interviews with lots of open-ended questions, you are not limited to a previously-defined list of questions. As you hold more interviews, you might find ways to improve your questions or add more questions. For example, you might want to add follow-up questions on themes that occur frequently and appear to be more significant. You might also be able to delete questions that show no promise of rich, useful data.
• As you continue coding, you should be able to improve your definitions of each theme and to improve your system of themes. Don’t let that disappoint you; it means you have a better understanding of the data. If your themes are more specific and better defined, they might occur less often, but your data will be more finely grained and probably help you reach better conclusions. You might add other themes, or combine or separate themes. If you delete themes, the reason is because you better understand the data and can better determine whether data is relevant or irrelevant to your topic. If you abandon a topic, write down the reason for doing so.
• A theoretical sample is a sample that evolves according to findings as they emerge. If a particular line of inquiry is fruitful, the researcher can add more repondents that will help to further explore it. If you want to use a theoretical sample, you should specify it in your proposal.

Stage 4: When to stop collecting data

One of these methods will help you when to decide to stop collecting data:

1. You have reached “data saturation” when you have gathered data such that gathering more would not improve, strengthen, nor add to your conclusions.
2. In some kinds of research, such as ethnography, it is usually possible to keep collecting more and more data. In these cases, the criterion is your research question. You can stop collecting data when you have enough to confirm your conclusions.
3. In other cases, you can stop when you have obtained data from everybody in your sample or from everybody in your population.

• ❓How good does my data have to be?
• The data must have enough rich detail to lead to confirmed conclusions, and lots of analysis will not make a poor data-set adequate. Your data is still inadequate if most occurences are only short comments with little information. In that case, you might still need to gather more data, that is, hold more interviews or focus groups to get more detail.

• ❓How much data do I need? When have I finished collecting data?
• The short answer to this question is: “when you have enough good data to identify and confirm a pattern in the data that will serve as a conclusion and answer your research question.” The point when you have this much data is called “data saturation.” You have reached data saturation when you have gathered data such that gathering more would not improve, strengthen, nor add to your conclusions.
• Data saturation is usually reached with fewer than 20 interviews, although some supervisors or institutions have rigid rules requiring a certain number of interviews, often either 20 or 25. As a common rule of thumb, however, it is normally enough to conduct three consecutive interviews that produce no new data.*
• You have enough rich data when you can identify patterns in the data that are useful as conclusions, and can confirm them based on the data available. Some researchers compile quasi-statistical records of occurances of themes, but the richness of the data is more important.

Stage 5: Transcription

You can start transcription as soon as you have collected data. Transcribe it word-for-word into documents, although you might be able to exclude anything clearly irrelevant to your research purpose. Warning: Some things that look irrelevant at first might appear more relevant later on when you understand the data better.

Most researchers prefer to use transcription voice-to-text software or external services to do transcriptions. A few, however, prefer to do it manually because it brings the very close to the data, even though it is horrendously time-consuming.

Stage 6: Familiarize yourself with the data

Start reading and re-reading all your data while it is still coming in, and make diary notes of any other questions arising. (If you transcribe manually, this will come very easily.)

When you become very familiar with your data, it might look very little even when you have enough. Don’t worry.

Stage 7: Select quotations

You might have started collecting quotations, but now you can treat it as an extra stage. Using direct quotations from respondents in your final report has two particular benefits:

1. Your readers will see the lives of real people whom you have interviewed. This personalizes your research report and makes it easier to read.
2. It shortens the distance between respondents and reader. This helps to prevent an analysis that is largely an artificial construct that you have created.

Stage 8: Coding

If possible, start coding as soon as you have transcriptions and are familiar with the texts, while you are still collecting data.

Mark all parts of the text that are relevant to your research question with a color-code or symbol. These might be “recurring patterns, terms, or visual elements.” (Naeem et al. p. 2.) On each part of the text that you marked, put a brief label of single word or a short phrase that says what is going on. These labels are your codes. Coding is itself paart of analysis, because you are sorting raw data into structured meaning.

You now have a patchwork of the meanings of everything in your data that is relevant to theory development. It is also simpler and briefer than the full text of raw data.

It is good practice to have someone else check your coding. It will help prevent or minimize personal bias in interpreting data.

• 💡 The simplest way is to color-code documents by hand, usually in a word processor, but paper might be easier for some people. Although time-consuming, hand-coding is still a good option because you get a better idea of what is going on in the data as you work through the details. Otherwise, you can use software, like Zotero, which is free and online; some institutions use it as their standard method. Just check that it will do what you want for your particular research project.

Stage 9: Assign themes

Group related codes together and represent them with a theme, that is, an overarching idea that represents what is happening. Themes are a higher level of abstraction.

Stage 10: Check your themes

Do your themes accurately represent the theoretical ideas in your data and codes?

Stage 11: Develop a Conceptual Model

When your have created a system of themes, compare different occurrences and look for patterns in the data. By this stage, you should be able to see patterns; the sooner you spot the patterns and confirm them, the faster you make progress. You will find that you read the transcripts again and again, and become very familiar with them.

What are the relationships between codes and themes? You can use diagrams or models to represent the relationships among these concepts. (Naeem et al. p. 4.) Can you accurately define those relationships and demonstrate them from your data?

Avoid these mistakes

Some mistakes are easy to make if you make incorrect assumptions about your respondents:

• A common comment might represent many respondents, but it might not be very insightful. Similarly, a rarely occurring comment might be very insightful.
• A short comment might be valuable.
• A long comment might not be valuable.
• An inarticulate respondent might be very insightful.
• An unintelligent or poorly educated respondent might still make a very helpful contribution.
• An unexpected response or theme does not mean it is an aberration. On the contrary, it might be valuable.

Questions

How many themes?

There is no rule about specific numbers of themes. The principle is that you need enough to represent the data accurately and to help you reach sound conclusions. If the number of codes hinder and confuse your analysis, you should ask whether the number of them is the cause of the difficulty.

How can I know that the data will answer my research question?

It will if your questions gather data that addresses the research question. (This is why alignment is so valuable.)

However, the answer might not be the answer you anticipate. Some students think that their data is wrong if it leads to conclusions that they didn't expect.

How can I code qualitative data from my interviews so that I work smarter, not harder?***

Organizing large amounts of data is possible with a computer, but it might not be the best way for everybody. Besides, if you make a mistake with a computer you might not notice it or might not be able to reverse it. Advice so far:

1. Many students use software to do coding and this works for them. If so, keep backups of your original data files. Make progressive backups every time you make major changes.
2. Many students like to print them out on paper, use color coding, and perhaps even put them on a wall as a huge chart.
1. Some people simply prefer to work with paper.
2. Printouts help them to visualize the entire dataset. It might also help them to feel more familiar with the data and perhaps to identify patterns in it. At the very least, seeing the whole thing at once will make you feel very satisfied.
3. Paper printouts might even be necessary if you still cannot identify patterns in the data after coding with software.

__________
* A mathematical proof of data saturation is unlikely because qualititative data is not appropriate for a mathematical proof.
** Ross Woods, 2020, '24, derived from Strauss and Corbin, 1990, pp. 99-107.
*** With thanks to Rιchαrd Scοtt Bαskαs, Rαιnεε Βrγαnt, Lγndα Dανis.

Muhammad Naeem, Wilson Ozuem, Kerry Howell, and Silvia Ranfagni. A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research International Journal of Qualitative Methods Volume 22:1–18 (2023) DOI: 10.1177/16094069231205789

Ross Woods, 2020, '24. Toolkit of research methods.

Anselm Strauss and Juliet Corbin. 1990. Basics of Qualitative Research: Grounded Theory and Procedures and Techiniques (Newbury Park, Ca.: Sage Publications).