Coding Qualitative Data

Analyzing quantitative research data is relatively straight-forward. No matter how complicated the formulas and calculations might be, the results are always quantifiable. Analyzing qualitative data is different. While quantitative data can be analyzed statistically and calculated into averages, means, and other numerical data points, qualitative data analysis assesses opinions or feelings that cannot be represented by a numerical statistic. Examining and interpreting qualitative data requires a more complex process. In this post we detail the process for coding qualitative data

Qualitative Data Analysis

Qualitative data can be defined as non-numerical and unstructured data that is collected from qualitative research methods, such as open-ended survey questions, in-depth interviews, focus groups, direct observation, and content analysis (video, picture or document). The data collected from these methods is typically in the form of typed text, transcripts or recordings that must be examined to identify key themes and insights. The process of examining and interpreting qualitative data is known as qualitative data analysis.

The most common type of qualitative data analysis is content analysis. Content analysis refers to the process of categorizing text, verbal or behavioral data to classify, summarize and tabulate the data. For example, to analyze focus group data, researchers might review transcripts or recordings and group similar sentiments together into categories. They would then assign each of those categories a “code.”

Coding Qualitative Data

Coding is an integral part of qualitative data analysis. Coding can be defined as the labeling and organizing of qualitative data to identify themes and patterns. The purpose of coding is to provide structure to free-form data so that it can be examined in a systematic way.

A code can be a word or a phrase that represents a recurring theme or idea in the data. The code name should be meaningful and capture the essence of the free-form response or observation. For example, coding the open-ended responses to a survey question might look like the following:

Assigning codes helps capture what each qualitative response is about. Researchers can then analyze those codes and begin to build on the themes and patterns that surface to gain comprehensive insights into the data. Although the process of developing and assigning codes can be laborious and time-consuming, ultimately it helps reduce the amount of data that must be reviewed or taken into account in the final analysis.

Automated Coding of Qualitative Data

There are two methods of coding qualitative data: automated coding and manual coding.

Automated coding uses qualitative data analysis software to quickly analyze and code qualitative data. The software leverages machine learning, artificial intelligence (AI), and natural language processing to determine themes and create codes without any advance setup or pre-planning. The algorithms learn as they go.

Some of the perceived benefits of automated coding include the elimination of researcher bias, the ability to process large amounts of data, and significant time-savings. Manual coding remains popular, however, due to its perceived higher accuracy.

Manual Coding of Qualitative Data

Manual coding requires researchers to read through their data and manually develop and assign codes and themes. Although manual coding is time-consuming, it can help streamline the overall analysis process. Creating codes requires the researcher to decide which data is relevant and why, reducing the amount of data that must be considered in the final analysis.

Before starting coding, researchers have to decide if they want to use a deductive or inductive coding method.

Deductive Coding

In deductive coding, researchers start with a predefined set of codes or a codebook developed before analyzing the research data. This set could be based on the research questions or an existing research framework or theory. For example, if the research question is why a consumer purchased a specific product, the researcher might predefine a list of codes that includes price, quality, brand, etc. With this list in mind, the researcher would then read through the research data and simply assign the predefined codes.

Inductive Coding

Inductive coding involves building a list of codes or a codebook from scratch based on the research data. Rather than starting with a plan for what the codes should be, researchers allow the themes and theories to emerge from the data itself.

Inductive coding is often more difficult but can be less prone to bias than deductive coding, because the researcher does not start the analysis process with any preconceived notions about what they might read or hear.

In practice, research studies often combine deductive and inductive coding, starting with a predefined list of codes but then inductively modifying and adding to that list as analysis ensues.

Steps for Manually Coding Qualitative Data

Coding is an important step in moving from the raw data to the findings. There is no right or wrong way to code a set of data, and the process can vary significantly depending on the data collected and the objective of the research.

In general, however, it involves some variation of the following steps:

1. First pass: Researchers first read through or listen to all the data and assign codes to general phrases, ideas or categories that surface. The codes might represent the participant’s own words, a label, description, definition, or category name. The purpose of this round is to gain an overall understanding of what the data is about. This step should be relatively fast and easy since the researcher will be evolving and updating the codes in the rounds to follow.
2. Line-by-line coding: In the second pass through the data, the researcher should comb through the data line-by-line, refining the list of codes and adding detail. While the first pass at coding is fast and loose, this second round is about reanalyzing, renaming, merging codes, finding patterns, and getting closer to developing theories and concepts.
3. Creating categories and themes: After line-by-line coding, it is time to start grouping codes together into categories and developing themes. Codes might be grouped together according to similarity or if they pertain to the same topic or general concept. The researcher then looks through the categories, keeping a close eye out for any themes or patterns that emerge across the data set. Within these themes lies the overall narrative of the research.

Conduct Qualitative Data Research and Analysis with GeoPoll

GeoPoll has experience designing, administering and analyzing quantitative and qualitative research studies around the globe. Our research methods include surveys with closed-ended and open-ended question capabilities, mobile-based focus groups, concept testing, and more. We use both automated and manual coding as part of our qualitative research analysis process. To learn more about GeoPoll’s capabilities, please contact us today.