A cross tabulation (or crosstab) report is used to analyze the relationship between two or more variables. The report has the x-axis as one variable (or question) and the y-axis as another variable. This type of analysis is crucial in finding underlying relationships within your survey results. (or any type of data!)
Generally, survey results are presented in aggregate – meaning, you only see a summary of the results, one question at a time. Cross tabulation takes this one step further and enables you to see how one or more questions correlate to each other. This type of analysis can reveal a relationship in your data that is not initially apparent.
If you were a store owner and asked the question "Do you like our products?" to customers, would the answers help improve your business? Of course! But without cross tabulation, it would be hard to gain further insights.
The data table below is from a sample survey in this scenario. You can see people generally do not like this company’s products. But does that tell the whole story? No, it does not!
Data table WITHOUT cross tabulation
|Answer Choice||Total Survey Responses||Percent of Responses|
|I do not like them||22||47%|
|I love your products!||20||43%|
|They are okay||5||11%|
Here is the same data below, but now we'll create a cross tabulation report with the respondents’ gender (asked in a prior question). A graph is also incorporated to better visually represent the data.
The question in our example, "Do you like our products?", has been cross tabulated, with gender from another question.
In the table, the total compared responses (47), matches the total survey responses (47). All data has been accounted for and is properly separated by gender. We can now look at the relationships in our data. We can see right away that it appears males tend to love the store's products and females tend to dislike the store's products. This result can also be seen in the graph above.
A feature unique to SurveyKing, is the ability to toggle response counts with response percentages. This makes your data table clean and easy to interpret.
Only with cross tabulation, were we able to see that males liked the company's products. Without this, a marketing team might have seen the survey results and thought, "Wow our products aren't doing well, maybe we need to re-brand". But as it turns out, a specific gender did like their products, and the marketing team would now be able to focus on growing that customer base.
Cross tabulation makes it simple to interpret data! The clarity offered by cross tabulation helps deliver clean data that be used to improve decisions throughout an organization.
No advanced statistical degree is needed to interpret cross tabulation. The results are easy to read and explain. This is makes it useful in any type of presentation.
The possibilities of cross tabulation are endless. Here's a few examples of common uses on SurveyKing.
Conducting employee engagement, employee satisfaction, and exit interview surveys, can identify problem areas in specific departments or job roles. Along the same line, managers can send surveys to customers to gauge customer satisfaction, and make improvements by department or region as needed.
When sending course and instructor evaluation surveys to students, administrators will often cross tabulate results with class subjects, the time of the class, and other metadata to discover weakness in curriculum to improve the education experience for students.
Conducting any type of satisfaction or feedback survey can be cross tabulated with metadata / demographics as shown in the prior interactive example. The results provide clean actionable data used to improve products and guide the focus of marketing campaigns.
From the results screen click on "Compare". This will bring up a screen where you can choose the segments you want to look at for the report. Choose from the dropdown the criteria question you want to compare. Select your answer choice(s) then click "Add Comparison".
The following questions are compatible with this report type.