Crosstab Analysis 101: Make It Work For Your Data

Any market researcher knows how important crosstab analysis is. It's a quick way to compare the results of one or more variables against another, serving as a fast track to valuable insights.

However, effective crosstab analysis is not as simple as putting two variables into a table. That's why we've put together this guide to show you when and where to use crosstabs, how to report your results, common pitfalls to avoid, and powerful software tools you can use.

What is Crosstab Analysis?

'Crosstab' is short for 'crosstabulation.' The definition of a crosstab/crosstabulation is largely in the name. It's essentially a table that shows how different variables intersect with one another. Crosstab analysis, therefore, is all about examining different variables from a dataset to find patterns, trends, and relationships.

For example, if you want to see if customer satisfaction is different between men and women, you could create a crosstab with gender in rows and satisfaction level (satisfied/neutral/dissatisfied) in columns. The table will then show how many men and women fall into each satisfaction category:

• Rows: Independent variables (like gender, age, region)
• Columns: Dependent variables (like satisfaction level, product preference)
• Cells: Frequency counts or percentages showing how the categories intersect
• Totals: Row and column totals help you see the overall distribution

Why Use Crosstab Analysis?

One of the main reasons to use crosstabs is to break down large volumes of data into smaller, digestible chunks that highlight key patterns and trends. This means you can make faster, better decisions based on the relationships between variables. Some more reasons for using crosstabs include:

• Simplified Insights: They make complex data easy to read and understand.
• Identify Trends Quickly: Spot patterns and correlations that would be hard to see in raw data.
• Compare Groups: See how different groups (e.g., age, gender, location) compare across various factors.
• Spot Outliers: Quickly identify unusual data points or outliers that could be important.

For example, a retail company might use a crosstab to compare customer satisfaction by store location, helping them identify which stores need more attention. Or a healthcare provider could analyze patient outcomes by treatment method to see which ones work best for specific demographics.

Real-World Examples of Crosstabs

Crosstabs are incredibly versatile. Businesses, researchers, marketers—or anyone working with categorical data— can use crosstab analysis to find insights in their data. For example, crosstab analysis is routinely used in:

• Market Research: To compare how different age groups, regions, or genders respond to a product or service.
• Human Resources: To compare how employees in different departments and office locations feel about the company they work for.
• Retail: To find out how shopping habits differ between customer segments—like identifying which age group buys a specific product most often.
• Healthcare: To analyze patient outcomes across different treatment methods and demographic factors.
• Education: To look at student performance by teaching method or subject.
• Political Polling: To understand how voting preferences vary across different demographics.

And this is only scratching the surface. Crosstab analysis has endless applications. Anywhere you deal with data categories, it can help you make sense of it all.

How to Perform Crosstab Analysis in Excel

If you haven't used a bespoke solution (like Displayr), you might have still had some experience with crosstabs via Excel or Google Sheets. Creating a crosstab in these platforms is pretty straightforward, thanks to Pivot Tables. They let you summarize data quickly and efficiently.

Although similar, crosstabs and pivot tables are two completely different tools for researchers. Pivot tables can tend to be more limited in their function and do not deliver the same clarity and focus of crosstab analysis.

Here’s how to build a crosstab in Excel:

1. Get Your Data Ready: Make sure the data is clean and organized in a table format. You'll want clear columns for each variable you plan to analyze (like Gender, Satisfaction Level, etc.).
2. Insert a Pivot Table:
   • Highlight your data.
   • Go to the Insert tab, then click Pivot Table.
   • Choose to place the pivot table in a new sheet for a clean setup.
3. Set Up the Pivot Table:
   • Drag one variable (e.g., Gender) into the Rows area.
   • Drag the other variable (e.g., Satisfaction Level) into the Columns area.
   • Drop a third field (e.g., Customer ID or Count of Satisfaction Scores) into the Values area to count how many people fall into each category.
4. Refine the Table:
   • You can easily show percentages by right-clicking the values and choosing Show Values As > % of Row Total or % of Column Total.
   • Use Conditional Formatting (in the Home tab) to make the numbers pop—color-code high and low values for easy analysis.
5. Analyze Your Findings: Now, you’ve got a table that clearly shows how your variables intersect. Do men tend to be more satisfied than women? Does satisfaction vary by age? The crosstab will show you exactly where the differences lie.

Hint: use filters to narrow down your data and sort your rows to bring the most important categories to the top.

How To Do Crosstab Analysis in Displayr

Creating effective crosstabs is Displayr's bread and butter. It is as simple as dragging a second Variable Set onto a Summary Table, provided you have an appropriate data set.

Method 1: From the Table Menu

1. Click Table > Table from Dataset in the toolbar to create a new table.
2. In the Object Inspector, select a variable for the Rows and another for the Columns. Tip: To show the count of observations in each cell, select Data > Statistics - Cells > n.

Method 2: From the Data Sets Tree

1. Drag your Rows variable from the Data Sources tree onto the page.
2. Drag your Columns variable onto the table or into the Columns field in the Object Inspector.Optional: Click Switch rows and columns to swap them.

Customizing Your Crosstab Analysis

Adjust the table’s appearance by modifying the following:

• Variable Set Properties: Structure, value attributes, and data reduction.
• Object Inspector: Data, statistics, filters, weights, and appearance.

Example of a crosstab in Displayr.

As you can see, creating a crosstab in Displayr is far less time-consuming than Excel. In Excel, you have to select the data, create or insert a pivot table, select what to include in the pivot table, select what values to count, and more. In Displayr, it's as simple as dragging the Variable Set onto the Summary Table. This means you can analyze more sets of data, with greater accuracy.

Significance Testing Crosstabs

Crosstabs are a great way to show key insights and noteworthy patterns. But the reality is, a lot of the time, a crosstab is going to provide you with rather uninteresting findings. So how do you filter out the gold from the rest?

This is where the chi-square test comes in. The chi-square test helps you assess whether the relationships you observe in your crosstab are likely due to chance, or if they represent real, meaningful associations between the variables.

Here’s how it works: The chi-square test compares the observed frequencies (the actual data in your crosstab) to the expected frequencies (what you would expect if there were no relationship between the variables). A low p-value (typically < 0.05) means that the relationship is statistically significant.

To run a Chi-Square test in Displayr:

1. Go to Anything  > Advanced Analysis Test > Chi-Square Test of Independence.
2. Under Data > Variable 1, select the nominal or ordinal variable containing the data that is to be predicted.
3. Under Data > Variable 2, select the nominal or ordinal variable containing the data that you are using as the predictor
4. OPTIONAL: Select Variable names to display variable names in the output instead of the variable labels
5. OPTIONAL: Select More decimal places to display more decimal places of precision in the output.

The Chi-Square Test in Displayr shows statistical significance.

Displayr also automates significance testing, using colors to highlight when results are statistically higher or lower, and showing the strength of relationships between the variables (the P-Value) with arrows. You can also instantly summarize the table with a few clicks.

How to Report Your Crosstab Results

Once you've built your crosstabs and run any necessary statistical tests (like the chi-square), it's time to interpret and report your findings. Here’s how to make sure your audience gets the most out of your analysis:

• Spot Key Patterns: What are the most significant relationships between your variables? Look for trends, correlations, and any outliers.
• Check for Statistical Significance: Ensure that the patterns you’re seeing are statistically significant before making any big conclusions. As mentioned above, a chi-square test is an effective way to do this.
• Use Visuals: Don’t just present tables—turn your findings into charts or graphs to make them easier to understand, especially for non-technical audiences.
• Keep It Simple: Focus on the key takeaways, and avoid overwhelming people with too much detail.
• Context Matters: Always provide context around your results. For example, if you're reporting on customer satisfaction, explain what the findings mean for your strategy.

How to Visualize Your Crosstab Analysis

We've spoken mostly about creating one crosstab at a time in this blog. The reality is, however, that market researchers deal with crosstabs by the thousands. This is why significance testing is so important.

When dealing with a multitude of crosstabs, there is the opportunity to summarize the different tables with data visualizations.

A heat map can summarize all of your crosstabs, with different shades showing the degree of statistical significance (see below).

Common Pitfalls to Avoid

Crosstabs are powerful, but like any tool, they come with a few potential issues:

• Too Many Variables: It’s tempting to throw in lots of variables, but this can make your crosstabulation hard to read and analyze. Stick to the most relevant variables.
• Small Sample Sizes: If some of your crosstab cells have too few data points, it can distort the results. Try combining categories or increasing your sample size.
• Mismatched Scales: Make sure your categories are consistent across variables. For example, if you're measuring age, use the same age ranges for both variables.
• Overinterpreting Results: Remember, correlation does not imply causation. Just because two variables are related doesn’t mean one causes the other.

Crosstab Analysis Best Practices

The beauty of crosstab analysis is that it allows you to analyze multiple different variables at the same time. But just because you can analyze lots of data doesn't always mean you should. For best results (especially if you're a beginner), focus on ensuring your data is clean and choosing relevant variables.

Another best practice to keep in mind is using column percentages. This way, you can gain a clearer insight into the relationship between variables and avoid overinterpreting correlations. You should also remember to handle missing data carefully—consider imputation methods or treating missing values as a separate category to maintain the integrity of your analysis.

Start Your Crosstab Analysis Today

Displayr offers a complete solution for crosstab analysis, so market researchers can spend less time analyzing and more time delivering insights. Its intuitive, drag-and-drop interface allows for quick crosstab creation, and real-time updates ensure smooth adjustments.

Book a demo and try it today.