The way market researchers analyze surveys is changing. There's more data, less time, and a host of new tools to choose from. Despite all of the changes, the goal of survey analysis remains the same - transform raw data into valuable insights with speed and accuracy.

This is your ultimate guide to survey analysis in 2025. We'll discuss everything you need to know about survey data, all of the different ways you can analyze it, and how to leverage modern tools to achieve results that make your competitors jealous.

Table of Contents

Types of Survey Data

In order to find those actionable insights, it’s essential you understand what type of data you’ve gathered in your survey and respectively, how to best analyze it. Some examples of survey data include:

  • Nominal Data: Data that categorizes or names things without any numerical significance. Examples include gender, brand preference, or marital status. 
  • Ordinal Data: Data that has a defined order or ranking, but the intervals between values are not consistent. A classic example is a Likert scale (e.g., 1 = strongly disagree to 5 = strongly agree). 
  • Interval Data: Numeric data with equal intervals between values but no true zero point. Temperature in Celsius is an example, as the difference between 10 and 20 degrees is the same as between 50 and 60 degrees. 
  • Ratio Data: Similar to interval data, but with a true zero point, which allows for a full range of statistical analyses.

Quant vs Qual: Blurring Lines in 2025

Quantitative data involves numerical values, which can be measured and analyzed statistically. Examples include age, income, or satisfaction ratings. Qualitative data, on the other hand, consists of descriptive information and is non-numeric, such as open-ended responses, interview transcripts, or observations. 

Typically, quantitative analysis involves statistical methods like averages, percentages, or correlations to identify patterns in the data. Qualitative analysis requires categorization and thematic evaluation of text or other non-numeric information, often using techniques like verbatim coding or sentiment analysis. 

Quantitative and qualitative data serve a distinctly different purpose. Quant provides the accuracy and scalability that comes from working numbers, while qualitative offers insight into emotions and motivations that inform people's decision-making. Traditionally, researchers have had to make a trade-off between the pros and cons of each approach.

However, advances in AI-powered text analytics tools have meant researchers can easily integrate both quantitative and qualitative insights without draining resources. By analyzing large volumes of open-ended (qualitative) responses, the AI can automatically assign a numeric value to certain answers (such as sentiment score), ensuring researchers can leverage statistical tools to find patterns in the data.

Determining Your Research Question

Before you begin any analysis, it’s essential you have thought through and decided on your research question. A research question is exactly what it sounds like: a question that states the problem you are trying to solve with your survey. 

A well-defined research question will guide you in choosing the right analysis methods, the tools you use, and how you interpret the results. It ensures that your analysis is focused and aligned with the specific goals of your survey. 

The best research questions are:

  • Clear and Concise: The question should be straightforward and easy to understand.
  • In-Depth: It should be specific enough to provide detailed insights but broad enough to allow for analysis. 
  • Realistic: Ensure that the question can be answered within the scope of the data available. 
  • Not Yes/No: The question should invite detailed responses and analysis. 
  • Use Analytical Language: The question should be framed in a way that directs toward data-driven insights. 

Preparing The Data

Your ability to effectively analyze survey data will depend heavily on the data itself. If your data is plagued with incorrectly formatted responses or duplicate answers, you will see inaccurate results. And if your data is in particularly bad shape, you will find analysis impossible.

There are many different tools and techniques you can use to clean your data but to simplify this process, it involves;

  • Looking for problems in the data
  • Removing these problems
  • Tidying the data so that it is easier to work with

Cleaning data is anything that you do to improve the quality of the data you are working with. It might be merging small categories, recoding values, or deleting responses.

How to Clean Survey Data

For a complete guide on cleaning data, check out our latest ebook.

Weighting

Data weighting is a crucial step in survey analysis that ensures your sample accurately represents the broader population. In simple terms, a weight is assigned to each respondent based on their relative importance in the dataset. This process corrects imbalances caused by sampling bias or non-response.

For example, if your survey sample consists of 40% female respondents, but women make up 50% of the actual population, you would apply a weight to female responses to better reflect reality. The weight factor is calculated by dividing the target population percentage (50%) by the sample percentage (40%), resulting in a factor of 1.25. While this is a straightforward example, real-world studies often involve multiple variables, making the weighting process more complex.

Despite adding time and complexity, weighting is essential because it improves the reliability of survey insights. Since researchers can’t survey an entire population, they rely on a sample to infer broader trends. However, without weighting, sample biases can distort findings. For instance, if you're surveying registered voters, your goal is to reflect the views of all voters—not just those who responded to your survey. By applying the appropriate weights, you ensure that your results more accurately represent the population as a whole.

Crosstab & Stat Testing

Crosstabs are the most popular tool for analyzing survey data because they provide a quick and structured way to compare responses across different groups. Instead of manually filtering data one group at a time—such as looking at purchase intent for men first and then for women—crosstabs allow you to see how different categorical variables intersect.

Each column in a crosstab represents a different category, such as gender or age group, and shows the percentage of respondents who selected each survey response. This makes it easy to identify patterns, like whether younger respondents are more likely to say they will buy a specific product compared to older ones.

Beyond just observing percentages, crosstabs also help determine if differences between groups are statistically meaningful or just due to chance. Statistical significance tests indicate whether a pattern is reliable enough to report or if the sample size is too small to draw conclusions. For example, a difference in purchase intent between income levels might seem interesting, but if there’s no statistical significance, it’s likely not a real trend.

Because surveys often require analyzing multiple demographic factors, crosstabs streamline this process, making them an essential tool for survey data analysis.

Illustration of Displayr Crosstabs

Find insights fast with crosstab analysis.

Cluster Analysis

Cluster analysis is a technique used to group similar data points together based on certain characteristics. It identifies patterns or clusters within the data and is useful for segmenting respondents. This type of analysis works well for both quantitative and qualitative data. 

For instance, you could use cluster analysis to segment survey respondents based on their answers to questions about product preferences, creating distinct groups that can be targeted with specific marketing strategies. 

Text Analysis

Text analysis is used to extract insights from open-ended responses in surveys, such as customer feedback or review data. It involves identifying common themes, patterns, or sentiment in text data. This is most useful for qualitative data, especially when respondents provide detailed written answers. 

AI-powered text analysis tools have made this process faster and more reliable by automatically categorizing responses, detecting sentiment, and identifying emerging trends. These tools help researchers process large volumes of text without manual coding, reducing bias and improving efficiency.

Additionally, text analysis can be combined with quantitative data, allowing researchers to cross-tabulate themes with demographics or other survey variables. This approach provides deeper insights, helping businesses and researchers understand not just what respondents say, but the context and meaning behind their words.

Factor Analysis

Factor analysis is a statistical method used to identify underlying relationships between variables by grouping together correlated variables. It's especially useful when you want to reduce the complexity of large datasets by finding latent factors that explain patterns in the data. Factor analysis is often applied to survey data to identify key dimensions or themes in responses.

In Displayr, factor analysis is easily accessible, allowing you to identify these factors and understand the structure of your dataset, all with an intuitive interface. 

Regression Analysis

Regression analysis helps understand the relationship between a dependent variable and one or more independent variables, often used to make predictions. It's commonly used in surveys to predict outcomes, like understanding how different factors (e.g., satisfaction, age, income) influence a target variable (e.g., likelihood to purchase).

Displayr offers powerful regression analysis tools, allowing you to run both simple and multiple regression models, interpret results, and visualize relationships easily, all within the same platform. 

Illustration of Displayr Survey Analyzer

Turn data into insights.

Reporting

Once survey data is analyzed, the next step is communicating the insights in a clear and impactful way. Effective reporting transforms raw numbers into meaningful stories, ensuring that findings are both accessible and actionable. These key elements contribute to high-quality survey reporting:

  • Visualizing Survey Data – Charts and graphs are essential for making data digestible. Raw numbers can be overwhelming, but visual representations help highlight trends, comparisons, and patterns that might otherwise go unnoticed. The best visualizations simplify complexity, allowing stakeholders to grasp key insights at a glance.
  • Dashboards for Dynamic Insights – Traditional static reports have limitations. Dashboards provide a more interactive way to explore survey findings, enabling real-time filtering and drill-down capabilities. This approach empowers users to extract the insights most relevant to their needs rather than relying on pre-aggregated summaries.
  • Automation for Efficiency and Accuracy – Manually updating reports is time-consuming and prone to errors. Automated reporting ensures visualizations and presentations always reflect the latest data, eliminating the need for repetitive updates. This allows researchers to focus on interpretation rather than maintenance, ultimately leading to faster and more reliable decision-making.

Finding the Right Survey Analysis Tool

Analyzing surveys is anything from a simple process. It takes time, knowledge, and experience to ensure you are getting the right insights from your data.

Fortunately, dedicated survey analysis tools - like Displayr - have the smarts built in. Whether you need to perform statistical tests or generate crosstabs, Displayr does the heavy lifting for you. And with interactive dashboards and real-time updates, you can spend less time reporting and more time interpreting results.

If you're looking for a faster, smarter way to analyze your survey data, try Displayr's survey analysis tool free and transform the way you uncover insights.