How to Analyze and Use Quantitative Data in Your Special Library: 3 Practical Tips

In a previous post, I discussed how to analyze and use qualitative data to support decision-making in your library. Today, we’ll turn our focus to quantitative data and explore three practical ways to analyze and apply it effectively.  

Many of you likely collect feedback via surveys that include Likert-scale, multiple-choice, rating scale, true/false, or yes/no questions. These are examples of quantitative questions that can be analyzed in several useful ways. 

1. Start with Descriptive Statistics

The most straightforward way to analyze most quantitative questions is by counting—this can also be known as descriptive statistics.  

For example, suppose your survey asks, “How often do you use the library?” with the following multiple-choice responses: 

• A) 0-5 hours/month 
• B) 6-10 hours/month  
• C) 11-15 hours/month  
• D) More than 15 hours/month.  

You would then count how many respondents selected each answer and report the totals. This provides a clear snapshot of library usage patterns based on actual numbers and specific data points. 

2. Visualize the Data

Create visuals of your data to make trends easier to identify and interpret. Tools like Excel or Google Sheets allow you to create pie charts, bar graphs, histograms, and more with just a few clicks. 

Here are some helpful resources for choosing the right type of chart or graph: 

• Google’s guide to choosing chart types: Explains which type of graph to use in different scenarios.  
• NCES Graphing Tutorial: A user-friendly tutorial from the National Center for Education Statistics. 
• Chart types in Microsoft Office: Overview of available chart types in Excel and other Office tools. 
• Google Sheets chart documentation: Guidance on charts and graphs for Google Sheets users. 

3. Consider Running a Regression Analysis

There are many statistical tests that can be run with survey data. A description of most statistical tests is beyond the scope of this blog post, but I want to mention a regression analysis. This is a test that helps you understand relationships between variables.   

As Google explains, “Regression analysis is a statistical method used to examine the relationship between a dependent variable and one or more independent variables.”  

You can run a regression analysis in Excel.  

Here are a few examples of when you might want to use a regression to:  

• Determine if there’s a relationship between training participation and the frequency of use of the library’s databases
• Identify predictors a particular tool usage (e.g., length of employment, role)
• Understand what influences satisfaction with the library (e.g., frequency of library communication, library usage within the past year, service responsiveness) 

From Analysis to Action

These are just three suggestions for analyzing quantitative data, and I hope they offer practical, actionable ideas you can implement in your setting. 

Once you’ve completed your analysis, the next crucial step is to use the results to guide decision-making. I recommend first reviewing the analyzed data on your own to reflect on any trends or patterns. Then, bring the data to a team or stakeholder group for discussion. Collaborative review can reveal new perspectives, validate your interpretations, and lead to more well-rounded conclusions. 

The next step is to act on the findings in a meaningful way. This could involve adjusting services to better meet user needs, reallocating resources to areas of higher demand, developing targeted outreach efforts, or refining programs based on feedback. The key is to let the data guide your decisions, ensuring that any changes you make are evidence-based and aligned with the needs and behaviors of your users.  

Ultimately, data analysis is a powerful tool for making informed, strategic decisions that support your goals and improve library services.