Analyzing survey results can be daunting, especially while facing a ton of data and responses. At the same time, it’s really important, as analyzing data can drive informed decision-making and improvements.
This blog post will delve into the best practices for analyzing survey results effectively. Whether conducting customer satisfaction, employee feedback, or any other type of survey, you’ll learn how to sift through the data noise, identify trends, and derive actionable insights.
Ready to become a survey analysis pro? Let's dive in!
How to analyze survey data: 7 best practices
Have a hypothesis you’re comparing data to
A hypothesis is essentially an educated guess about the outcome of the survey. It’s a prediction of what you think you’ll find. For example, if you think studying more leads to better grades, your hypothesis might be, "Studying more increases test scores."
A hypothesis will tell you precisely what to measure or track. Things you measure are called variables. In the example, "studying more" is the independent variable (what you change or manipulate), and "test scores" are the dependent variable (what you measure as a result).
It’s easy to get lost in data. Your job when analyzing surveys is to see how changes in the independent variable cause changes in the dependent variable. Sometimes, a hypothesis isn’t so clear, and you may even have to conduct preliminary research to form a hypothesis.
Learn about the three main types of research surveys that can help you form a hypothesis.
Understand causation vs. correlation
Understanding the difference between causation and correlation is crucial when analyzing survey results. Causation and correlation both describe the relationship between the variables of your survey, but they mean different things.
Causation means that one factor directly influences the other. For example, if you find that improving customer service response times leads to increased customer satisfaction in a survey, you may conclude a causal relationship. So, better response time causes more satisfied customers.
On the other hand, correlation means that two variables may occur together, but one doesn't necessarily cause the other. For example, there’s a positive correlation between higher levels of education and higher income.
This correlation suggests that more education is associated with higher income, but it doesn't prove that education directly causes higher earnings. Other factors like career choice, experience, and economic conditions influence income, too.
Present survey results better with Advanced Summary
Forming a hypothesis and playing around with variables is an integral part of surveying. Once you have your survey data on a spreadsheet, applying formulas or pivot tables can help draw out insightful results.
However, with Google Forms, working with complex formulas and pivot tables isn’t the easiest thing to do. A simple Google Sheet add-on tool that can save you time and effort here is Advanced Summary.
Advanced Summary allows you to easily present your data in powerful visuals like charts and diagrams. Simply pick the questions you want, and Advanced Summary will align the appropriate variables to answer them. There are also filters to help you parse through data easier than natively.
Analyze quantitative data before qualitative
When analyzing survey results, it's better to begin with quantitative data before tackling qualitative aspects. Quantitative data offers numerical insights, helping you identify trends and calculate critical metrics. It’s a foundational step for understanding the survey's overall results and providing a kind of numerical backbone for your analysis.
Once you've grasped the quantitative aspects, you can explore open-ended responses to layer in qualitative data. This will provide depth and context to your quantitative findings.
Learn more about the difference between quantitative and qualitative data.
Separate responses by demographic:
While you may be able to identify specific variables for your hypothesis, another way to categorize your survey responses is by demographic characteristics. Common demographics include:
- Age
- Gender
- Racial or ethnic background
- Location or geography
- Income or occupation
- Education level
- Marital status
- Family size
By separating data based on these variables, you can identify distinct patterns and trends within different demographic groups. For instance, in an employee engagement survey, analyzing responses based on age group can reveal the following insights:
- Variations in engagement levels among different age groups
- Differences in communication preferences among different age groups
- Different perspectives on work-life balance depending on position
Automatically share responses with Form Publisher
Honest responses are critical for drawing conclusions. Sharing an individual's response with them gives respondents a sense of ownership over their input. This transparency enhances trust and encourages honest and thoughtful responses. It also reduces the chances of misunderstandings or misinterpretations of their answers.
You might also be working with others and may want to share all responses with your collaborators. That’s not easily done all the time, but there’s a way to automatically share responses with others, and that’s Form Publisher.
Form Publisher is a simple add-on to the popular survey and form creation platform: Google Forms. It allows Google Forms responses to be transformed into individual response documents.
Organize and store away responses for later
Another great step in data management is organizing the received responses and storing them carefully for future reference. Proper organization allows for efficient retrieval, analysis, and comparison of responses over time. It also safeguards against data loss, ensuring no insights are accidentally discarded.
Luckily, if you’re using Google Forms, the responses are saved there unless both the form and corresponding Google Sheet is deleted. Also, with Form Publisher, all created response documents are organized and stored away on your Google Drive for later use.
Analyzing survey results is a breeze with Google Forms add-ons
Analyzing survey results becomes remarkably convenient with the help of these best practices and Google Forms add-ons. Add-ons like Advanced Summary and Form Publisher boost functionality to streamline the entire data analysis process.
Advanced Summary lets you present your survey data in visually appealing and meaningful ways by referencing different variables in the data set. Form Publisher allows you to send personalized and customized response summaries to your respondents and carefully store your responses for later use. If you’re in the results analysis phase of your survey, try Advanced Summary and Form Publisher today and see the difference for yourself!