How to Analyze Qualitative Data from Library Surveys to Improve Services

Whether or not you conduct formal research studies, chances are you’ve gathered feedback at some point—through surveys, comment boxes, focus groups, or informal conversations. Distilling qualitative data into actionable insights can help you improve library services, demonstrate value, and meet user needs more effectively. 

I’m currently immersed in a formal research project that requires analyzing a substantial set of qualitative data from open-ended survey responses. In this post, I will share practical tips for analyzing qualitative data and applying what you learn. 

How to Turn Library User Feedback into Actionable Improvements 

If you’ve made the effort to collect library user feedback, you don’t want that valuable information to go to waste. To turn user feedback into an actionable plan for improving library services and offerings, you need to know how to interpret it effectively. Here’s a step-by-step overview you can follow to analyze qualitative user feedback. 

1. Start with a Thorough Review

First things first: read all the data to become familiar with it. This will help you start to identify themes and patterns before engaging in a more formal analysis.  

 2. Choose a Coding Approach

Next, you will want to go through the data again with a systematic approach. There are two common approaches to qualitative research coding: deductive and inductive.  

• Deductive Coding: Deductive coding involves applying predetermined codes to the data. It’s helpful when you want to apply a specific framework or if you want to see whether specific concepts are present in the data. For example, you may want to categorize all the data into a few pre-existing categories. In a library, your pre-existing categories might be Collections, Reference, Facilities, and Programming. 
• Inductive Coding: In contrast, inductive coding happens when you let themes emerge organically from the data. In other words, rather than starting with a framework or preconceived categories, you create categories based on the themes you discover during the analysis. This can be especially useful when you’re exploring open-ended feedback. 

3. Group Comments by Theme

Once you’ve grouped the data into themes, you will want to determine the frequency of comments related to each theme as well as identify the context and meaning of the themes. How often are users mentioning a particular issue—and what are they actually saying? 

For example, if requests for “quiet workspaces” come up repeatedly, is it about the availability, comfort, noise levels, or something else? The “why” behind the theme is just as important as how often it appears. 

Real-World Example: From Feedback to Priorities

Here’s what qualitative data analysis might look like in a real special library setting:

Let’s say you’ve recently surveyed your users about library resources, including an open-ended question about how the library can better serve the information needs of your organization. You received many written responses.  

You read all the responses to become familiar with the data, and then you use inductive coding to categorize the ideas into themes. You count how many responses fit into each theme that you see in the data.  

Based on the data, you decide to prioritize the most frequently mentioned ideas. This helps you respond to your user community’s important information needs and demonstrates the value of the library as you respond to the users’ ideas.  

Tips for Managing Qualitative Data Efficiently

Analyzing qualitative data can be time-consuming, especially with large data sets. Here are a few techniques to help you stay organized and efficient: 

• Use AI to identify themes. While I recommend reviewing the data yourself first and then checking the AI outputs to see if you agree or to determine if anything was missed, AI is very good at identifying themes. You do want to be aware of privacy concerns with uploading data. Check with your organization’s AI policies before uploading qualitative data.  
• Highlight with color codes. As you read the data, highlight similar ideas using different colors. You can do this in Adobe, Word, or Google Docs. This will help you keep track of how many of the same ideas are mentioned, and you can easily select quotes that align with a theme.  
• Try qualitative data tools. Use qualitative data analysis software, such as Dedoose, ATLAS.ti, NVivo, etc. There are many different options to help with everything from tagging to visualization. 

Turning Insights into Action

I hope you collect qualitative data and use it in your work. It can be a valuable resource to ensure you are meeting the needs of your community and users.  

Qualitative data provides powerful insight into how people experience your library services. Whether the feedback is from a full research project or a handful of open-ended comments, carefully analyzing it can help you make informed, user-centered improvements. 

Frequently Asked Questions

How can libraries use qualitative data to improve services?

Libraries can use qualitative data from user surveys and feedback to identify patterns, prioritize service improvements, and tailor programming to community needs. 

What’s the difference between inductive and deductive coding? 

Deductive coding uses pre-defined categories to classify data, while inductive coding lets themes emerge naturally from user responses.