You know that feeling when you open a puzzle box and all the pieces are scattered everywhere?
That's what raw, unsorted data in UX research feels like.
But once you get down to coding it, everything falls into place, and suddenly the bigger picture starts to make sense.
But hey, data coding in UX research is not as technical as it sounds.
It's just a way of organizing and analyzing qualitative data so you can make meaningful conclusions from it.
In short, coding turns messy data into clear, actionable insights.
To make it simpler for you, this guide will walk you through the basics of coding data in UX research, including practical tips and methods to speed up your process and find valuable insights.
What is data coding in UX research?
Data coding in UX research refers to a systematic way of organizing qualitative data, which may include interview transcripts, survey responses, or customer feedback, into categories or themes. That way, researchers can find patterns, trends, and meaningful insights to guide design decisions.
For example, you are looking at some feedback from a user testing session. One respondent will say, "The app is slow to load"; another will say, "It takes forever to open."
By coding these responses, you could group them into a theme like "loading speed issues" which allows you to recognize this as a repeated problem that you need to attend to.
Key methods of data coding in UX research
Data coding is the process of categorizing raw data and establishing relationships. Let's break down the key methods used to turn this data into actionable insights.
1. Open coding
Open coding is the first step in data analysis where you break down the data into broad categories. This will help establish common patterns in the responses to understand the underlying themes. You can opt for thematic tagging to bifurcate data based on recurring themes or patterns.
Example
You’re going through interviews with users about an app’s navigation. Some responses mention "confusing," others say "easy," and some say "intuitive." You’d group these under categories like “confusion” and “ease of use” to better understand the user experience.
Related read: Your Go-To Guide to Thematic Analysis
2. Axial coding
Axial coding is an even more advanced form of coding whereby you look into the relationships among the categories you have placed. You now link pieces together to understand their relationships and their interactions with one another.
Example
From an open coding of "confusion," you see that the sources of "confusion" point to unclear labeling or poor layout. This can now enable linking problems to certain features that may need improvement in your design.
3. Selective coding
Selective coding allows you to really focus on the most meaningful themes that answer your core research questions. It means zeroing in on what's truly important for the design improvements or user experience insights you seek.
Example
Going through the different issues, you notice that a larger number of users expressed frustration with one of the navigation features. Choosing this as one of the main themes allows you to create something more important and, therefore, to bring more change to your users.
4. In Vivo coding
In vivo coding uses participants' exact words or phrases as codes, preserving their voice and perspective. This type of research method is particularly valuable when understanding user terminology and mental models.
Example
If multiple users describe a feature as "confusing," "overwhelming," or "cluttered," these exact terms become codes, helping maintain the authentic user perspective.
5. Process coding
Process coding focuses on actions, interactions, and consequences in the data, using gerunds (-ing words) to capture ongoing processes and behaviors.
Example
Observing users interacting with a prototype might yield codes like "scrolling repeatedly," "hesitating before clicking," or "abandoning task."
How to code data in UX research (step-by-step guide)
Now that you understand what coding is, let's break down the process. Here is a simple, step-by-step guide on how to code your research data and extract valuable insights.
Step 1: Gather your data
Start by collecting qualitative data through methods such as interviews, surveys, or observations. The more diverse and detailed your data, the richer your analysis will be.
Step 2: Prepare the data for coding
Clean and organize the data before coding. This involves transcribing interviews, sorting through raw notes, and ensuring all information is clearly recorded for easier analysis. Also, plan for both deductive codes (based on research questions) and inductive codes (emerging from the data)
Step 3: Determine the coding method
Organize your data with the help of these three types of coding methods:
- Open coding: This breaks data down into broad categories.
- Axial coding: This develops connections between categories.
- Selective coding: This involves focusing on key themes that address your research questions.
Step 4: Apply the codes
Once you’ve identified your categories, manually apply the codes to your data. This can be done by reading through the responses and tagging relevant portions or using user research tools like Looppanel to streamline the process.Looppanel's AI-driven platform tags themes in your data automatically, organizing responses in a research repository by key topics such as pain points or user needs. This helps you focus on critical insights without getting bogged down by repetitive manual work.
Step 5: Analyze and synthesize findings
Now read through the data again, this time with the codes in place, searching for patterns and major insights. Look for trends and connections across the coded data that will help you answer your research questions and identify areas for improvement.
Looppanel can take off the load for you with features such as automatic thematic tagging and the generation of executive summaries that bring out the most vital trends. This way, you will be able to quickly identify the critical insights and focus on areas that require attention, saving time and effort in your analysis.
Manual coding vs. AI tools: a quick comparison
Data coding can be carried out either manually or with the support of AI-driven tools.
Best Practice: Consider a hybrid approach that leverages both human insight and computational efficiency. Use tools like Looppanel to manage and organize data while relying on human judgment for interpretation and nuanced understanding.
Conclusion
Data coding is a backbone of UX research that turns raw data into insights to drive design decisions. The open, axial, and selective methods for coding allow for the identification of patterns and trends to gain maximum insights for your UX research and make informed decisions. But the manual process might seem never-ending with a high chance of errors and increased costs. This is why researchers have been leveraging AI. With AI-powered tools like Looppanel, coding becomes faster and more accurate. Features like automatic notes, AI tagging, and 90%+ accurate transcripts will enhance your workflow. Request a demo today to see how Looppanel can streamline your research process!
Frequently asked questions (FAQs)
1. How to do data coding in research?
Data coding is the process of categorizing and tagging data to identify patterns.
2. Does UX research require coding?
Coding is helpful in UX research to organize and make sense of qualitative data like interviews or surveys. Not always a must, but is really helpful.
3. How do you synthesize UX research findings?
Synthesizing findings involves the analysis of coded data for patterns, identifying the key insights, and summarizing them to answer the research questions effectively.
4. How do you code data thematically?
Thematic tagging is the identification of themes or patterns that emerge in qualitative data, such as interviews or surveys. Looppanel automatically thematically tags data, recognizing key patterns and categories saving hours of manual work.
.jpeg)
.svg)
