A Brief Read About Coding Qualitative Data

Researchers get data from open-ended surveys or focus groups. These provide more actionable insights than using numerical values. 

You might find these more helpful yourself. But, too often, you’d find yourself with tons of free-text answers. And, these are sometimes hard to sort and analyze. Thus, you’d have to spend hours on spreadsheets to get new insights from these responses.

It will be easier to turn them into quantifiable data to understand them better. Even if some formulas are complex, the results are always definite. 

Qualitative data analysis requires a different approach without numerical statistics. But, you can observe and interpret it by coding qualitative data (here’s how). 

Qualitative Research/Data Analysis

Qualitative data is non-numerical and unstructured data gathered from qualitative research methods. It is usually done through open-ended surveys and focus group discussions.

The information collected comes in the form of typed or free-text and recordings. Key themes and insights come from the interpretation and examination of these data.

Doing content analysis is the most common method of qualitative data analysis. It involves the process of categorizing information to classify, summarize, and tabulate it.

For example, a researcher has to review texts. He has to group them into categories to analyze open-ended survey responses. From there, he will assign a code to each category. 

Coding Qualitative Data 

Coding is a fundamental part of qualitative research. Researchers do it by labeling and organizing data to determine themes and patterns. It aims to examine free-form or unstructured data in a systematic manner. 

In essence, codes can be words or phrases that classify a recurring idea in the data. Note that codes should be meaningful and connected to the free-text responses. Coding open-ended survey responses might look like the following:

Customer Service
Participant AnswersCodes
The customer service representative is nice and helpful.Positive Feedback
Their responses are usually timely.Positive Feedback 
Issues can’t be fixed immediately. Negative Feedback

With these codes, researchers can analyze recurring ideas to make comprehensive insights. Although the process can be tedious, it reduces the amount of data accounted for in the analysis. 

Automated Coding

In automated coding, researchers can use software to analyze and code qualitative data. It capitalizes on artificial intelligence (AI), natural language processing, and machine learning. So, creating codes and determining patterns are easier. Moreover, it eliminates data biases to come up with a more accurate analysis. 

Manual Coding

With manual coding, researchers have to go through the data and assign codes by hand. It is timely and helpful in simplifying the entire analysis. It also enables them to reduce the amount of data and choose only those relevant in the final analysis. But before doing so, they have to choose between deductive and inductive coding. 

Deductive Coding

Before doing the analysis, researchers deal with predefined codes first. These come from questions or research frameworks. 

For example, the question can be why consumers check in a specific hotel. The predefined codes may include the price, quality of service, and name of the hotel. Once these are complete, researchers will go through the data and assign codes. 

Inductive Coding 

Inductive coding requires the creation of codes from scratch based on the data. This appears to be a spontaneous method.  This is because researchers allow ideas to emerge from the data itself. It does not start with preconceived ideas about the information. It is more complicated but less prone to research biases. 

Deductive and inductive coding usually go hand in hand. The analysis starts with predefined codes before inductive modification. Codes are then added as the analysis develops. 

Qualitative data analysis can be tedious. It can’t be easily quantified since responses are descriptive at most. Moreover, maintaining objectivity can be challenging due to the absence of numerical data. But, coding streamlines the analysis and guarantees accuracy.