How to Code Qualitative Data for Research
Qualitative data coding is the process of generating and assigning codes to classify data extracts. You will then use these codes to derive themes and patterns for your qualitative analysis. A code is a label that explains the content of a piece of text. Coding and analysis can happen simultaneously, but it’s important to note that coding doesn’t necessarily involve identifying topics. Instead, it generally refers to the process of labeling and grouping similar data types together to make topics and data analysis more manageable. Good coding is the basis for high-quality analysis.
The benefits of quality coding
Increase potency: Qualitative coding provides organization and structure to the data so you can systematically review it to increase the validity of your analysis.
Reduce deviation: Qualitative coding allows you to be aware of potential deviations in the way data is analyzed.
Accurate representation of participants: Qualitative coding allows you to assess whether your analysis is representative of the participant base and helps you avoid overrepresenting any one person or group of people.
Enable transparency: Qualitative coding allows other researchers to systematically and methodically review your analysis.
How to code qualitative data?
The process of coding qualitative data varies widely depending on your research goals. But in general, first, it consists of choosing a place to do it. For example, a software for qualitative data coding is ATLAS.ti, and it involves a process of reading your data, applying code to pieces of code, performing various rounds of coding, grouping code into topics, and then performing interpretations that lead to results. You can start with the first round of coding to summarize or describe the excerpts, then do a second round of coding to add your own interpretive perspective.
There is no right or wrong way to encode a dataset, there are only a few approaches that are more or less suitable depending on your research goals.
Types of coding
Coding can be deductive or inductive. With deductive coding, you, as a researcher, start with a set of pre-established codes and apply them to your data set (e.g., a set of interview records). Inductive encryption, on the other hand, works in reverse because you create the set of codes based on the data itself – in other words, the codes emerge from the data.
Make your first batch of encrypted qualitative data
Your first step in encoding qualitative data is to read the data and assign codes to different code snippets. This step is quick and loose. Don’t worry too much about generating perfect codes because you will iterate and evolve the codes as you move through the second coding step and beyond.
The main encryption methods include: in vivo, process, open, descriptive, structural, and value coding.
Organize your code into categories and subcodes
After your first attempt at coding qualitative data, you can start grouping your code into categories. These categories can be organized in many ways. Within each category, you can group codes that are similar or related to a common topic or concept. Go through these categories and move the codes around until you find the right structure for your analysis.
Then do more rounds of qualitative data coding to review the codes and categories you’ve created so far. During this phase, you can rename, re-encode, merge code, and reclassify the work you’ve done so far. Although the first phase of data coding is quick and loose, these rounds of coding aim to re-analyze, find patterns, and get closer to developing theories and concepts.
Turn codes and categories into your ultimate story
After these rounds of coding qualitative data and after performing different coding styles, you take these codes and categories and use them to build your final story. Depending on your search intent, your final search results can take many forms: a theory, a set of findings, or a narrative. In this phase, you combine the creativity of structuring a story with the analytical nature of connecting your story to rules and theories based on qualitative data.
Validity and reliability in qualitative research
It is important to consider validity and reliability when conducting qualitative research, no matter what type of coding you practice. Here are some methods you can consider putting into practice to increase the credibility of your search results.
Peer Interview
Peer briefing is the process of working with one or more neutral and independent colleagues to build credibility for qualitative research.
Practice reflexes
Reflection involves testing your own judgments, practices, and belief systems during data collection. The purpose of the reflection is to identify any personal beliefs that may have inadvertently influenced the study.
Encoder reliability
The reliability of the encoder ensures that when multiple researchers encode a set of qualitative data, they come to the same conclusion.
Negative case analysis
Negative case analysis involves finding and discussing contradictory qualitative data emerging from your working hypothesis or theory.
End-to-end encryption method
End-to-end encryption is a structured and proven qualitative research method. It provides researchers with a systematic workflow to follow from the beginning of the study to the final writing of the paper.
Some end-to-end methods are also referred to as second-round encryption methods. Because studies may use one or more research methods, you may find situations in which you combine methods and approaches. Depending on the amount of qualitative data you need to analyze and the different constraints in your research, you can either code it by hand, using word processors and spreadsheets like Microsoft Word and Microsoft Excel, or use computer-aided qualitative data analysis software such as Delve.
There are some techniques that are more or less acceptable, as determined by the purpose of your research, and none of them is wrong. Take your time when starting this kind of work, help yourself with some of these tips, and you are good to go.
