Crosstabulation—it's the ultimate way to turn the chaos of survey data into valuable insights. Whether you're a researcher, marketer, or data scientist, mastering crosstabulation (or crosstabs) can transform the way you analyze and present your data.

What Is Crosstabulation?

Let's start with some definitions. A tabulation is data in a table (i.e., rows and columns). A crosstabulation, therefore, is where these tabulations intersect.

In simple language, crosstabulation is about relationships. It's a statistical method that shows how two or more categorical variables intersect, effectively revealing patterns and trends in the data.

In a table where rows and columns represent different variables (e.g., customer age groups, and satisfaction levels), each cell shows how often these variables intersect. For example, when analyzing a survey about the best Cola brands, the researcher could easily see how many males preferred Pepsi Max. This is a quick and effective way to get a snapshot of the connections in your data.

Although it is a fairly straightforward form of analysis in theory, crosstabulation opens up a whole world of practical use cases. For example, a retail business might use a crosstab to see how satisfaction ratings differ between age groups.

The results might show a sharp dropoff in CSAT for customers in a specific age group, showing the business where it needs to invest and improve.

Why Crosstabulation Matters

Crosstab analysis is all about finding significant relationships in your data. It helps you:

  • Spot trends: Which product features do younger customers love? Which do older customers ignore?
  • Compare groups: See how behaviors differ across demographics like gender, income, or location.
  • Simplify reporting: Show stakeholders clear, impactful relationships without drowning them in raw data.
  • Drive decisions: Back your strategies with data-driven insights rather than assumptions.

This isn't just about crunching numbers—it's about telling a story with your data.

Getting Started: The Basics of Crosstabulation

Creating a crosstab is relatively straightforward, particularly if you have the right tools. Follow these steps:

  1. Choose your variables: Choose two or more categorical variables with a logical relationship, such as "Customer Age" and "Satisfaction Level."
  2. Gather and clean your data: Ensure your dataset is complete, accurate, and properly coded for analysis.
  3. Build your crosstab: When it comes time to build your crosstab, consider using Displayr. Its drag-and-drop functionality makes creating effective crosstabs effortless.
  4. Analyze the results: Look for patterns and relationships. Are there categories with high or low counts? What stands out?

Pro Tip: Use percentages alongside raw counts to make comparisons clearer. Instead of saying, "Twenty customers aged 18-24 were satisfied," say, "80% of customers aged 18-24 reported satisfaction."

Note: A crosstab is not the same as a pivot table. Pivot tables (typically created in Excel) rotate data by moving the columns or rows to enable different views of the same data.

Crosstabulations Top Tips

There are two sides to working with crosstabs for researchers - creating effective crosstabs based on your survey data and then actually reading and understanding the data. When it comes to building out your crosstabs, there are some simple dos and don'ts that should help guide you.

In terms of what you should do:

  • Pick meaningful row and column fields: Make sure they're intuitive and align with your analysis goals.
  • Don't overdo it with fields: Too many options overwhelm; stick to the essentials for clarity.
  • Balance density: Sparse crosstabs are tough to interpret; overly dense ones bury insights.
  • Nail your headers: Clear, concise labels make all the difference.

On the other hand, some common pitfalls to avoid are:

  • Ignoring significance tests: Use statistical tests like chi-square to validate your findings.
  • Forgetting the audience: Simplify your tables and use visuals to make insights clear.
  • Implying causation: Remember, crosstabs show relationships, not causation. Correlation doesn't equal causation!

Making Sense of Crosstabs

When it comes to actually interpreting crosstabs, there are a few guidelines to keep in mind in order to truly get the most out of your data. The crosstab below shows us the purchase intent for a specific product by gender. We can see that each column represents a sub-group, while NET shows us the entire sample.

This shows us that 20% of women would definitely buy the product, while only 16% of males would do the same. Meanwhile, 18% of all people surveyed (i.e., the NET) would probably buy it.

Crosstabulation example

Advanced Crosstabulation

The table above is very effective in that it clearly shows the purchase intent for a specific product by gender. However, it is also quite one-dimensional. If you look at the graph below, you can see there crosstabs do not have to be this plain.

advanced crosstab example

This crosstabulation answers multiple questions (purchase intent by gender and age) by introducing banners across the top. It also uses a TOP 2 BOX and BOTTOM 2 BOX to better summarize the data for a quick glance. You can also notice the addition of letters next to certain numbers to show statistical significance.

The two crosstabulations we have shown in this blog both serve a different purpose - but it is important to note there is no right and wrong answer. You should always choose the method and level of complexity that is best suited for your data and the crosstab software you have available.

Turning Data Into Actionable Insights

Displayr makes it easy to automate the creation of crosstabs of all shapes and sizes. You can merge cells, create nets, add complicated banners, or remove statistics with a few clicks.