Also known as contingency table analysis or crosstabs, cross-tabulation is used to analyze categorical data. Read on to find out more about crosstab analysis and how to use it.
Cross tabulation (crosstab) is a useful analysis tool commonly used to compare the results for one or more variables with the results of another variable. It is used with data on a nominal scale, where variables are named or labeled with no specific order.
Crosstabs are basically data tables that present the results from a full group of survey respondents as well as subgroups. They allow you to examine relationships within the data that might not be obvious when simply looking at total survey responses.
With cross tabulation, you can examine your data in a variety of ways to achieve a deeper understanding of groups within your respondents.
Analyzing large datasets can be difficult. Finding relevant, actionable insights within large amounts of data is even more complicated. Crosstabs simplify and divide data into subgroups for ease of interpretation—they show percentages and frequencies that may change when contrasted with variables in other categories. By making datasets more manageable at scale, fewer errors will result.
Using crosstabs, you can examine the relationships between one or more variables, which leads to insights on a more granular level. These insights could go unnoticed without crosstabs, lost in a sea of data, or require additional work to reveal. Use multiple filters to dig even deeper into data to uncover more details.
Using crosstabs simplifies datasets so that you can make quick comparisons between them. This means faster insights for creating new marketing strategies guided by the data. You are also able to watch for global trends across survey responses and take action accordingly.
When you use crosstabs, datasets are simplified and divided into subgroups. The resulting clean data is in a more digestible format and easily viewed and used by research professionals and team members without analytics training.
Cross tabulation is typically used when you have information that can be divided into mutually exclusive groups, also known as categorical variables. It allows you to examine relationships within the data that may not be readily apparent. A crosstab report can show the connection between two or more survey questions from the study in market research studies.
Crosstabs are used across multiple industries and job functions. Examples of departments that benefit from crosstab analysis include:
HR departments can use cross tabulation to examine employee survey data about company culture, managerial guidance, employee engagement, and more. Using crosstabs will assist HR with determining departments that have particular problems or needs that they can address.
Market research teams can take raw data and make it more digestible for management decision-making with crosstabs.
Customer support teams can use crosstabs to evaluate customer satisfaction levels between long-term and new customers.
School administration can use instructor evaluation data from students and cross-tabulate it with the class subject, time of class, and other data to help improve the educational experience for students.
A Chi-square test is used to test data in a cross-tabulation table to determine whether or not the data is statistically significant. The results are statistically significant if the two categorical variables are independent (unrelated). Basically, the Chi-square test is a correlation test for categorical variables.
Here are some vocabulary words that can be used when it comes to crosstabs:
Employee satisfaction: The following is a cross tabulation example from SurveyMonkey that was created with data from an employee satisfaction survey. The survey used multiple-choice questions to ask employees:
The questions that define the groups are in the columns, and the questions used to compare the groups are in rows. This is the typical format of a crosstab report.
From this crosstab table, you can see that there is a relationship between employees who have been at the company longer and their level of satisfaction. Once you’ve identified this relationship, you can explore it further to find out the root cause of this relationship. From the data you have, you can’t definitively say that one variable is impacting the other. In other words, the data identifies a correlation between longer-term employment and employee satisfaction, but it does not imply causation.
Tip: Be careful when analyzing your data to ensure that you don’t confuse correlation with causation.
Purchase Intent: In this example, you want to know which gender of survey respondents is more likely to purchase your product.
Again, the data that defines the groups appear in the columns (male and female). And the question for comparison is in the rows (would you buy my cat perfume?).
If you only considered overall results without using crosstabs, you’d find that 54% of survey respondents report that they would be interested in purchasing your product. You wouldn’t have a clear picture of the purchase intent by gender, which would undoubtedly play a part in personalizing your marketing efforts.
With cross tabulation, you find that 45% of all respondents say they will definitely purchase your product and that 66% of those respondents are female. You can use this information to guide everything from product naming to packaging to messaging.
There are numerous insights to be uncovered with crosstabs. Here are just a few examples of questions that can be answered with crosstab reports.
How do customer satisfaction levels differ between new customers and customers who have purchased from you before?
What is the relationship between customer satisfaction level and whether they would recommend our product?
Would your most satisfied customers share their positive reviews on social media channels?
What do customers who are not satisfied with your product cite as their main complaint?
How do employees in different departments feel about our company?
Is there a relationship between office location and satisfaction?
Is there a difference between men and women with their intent to purchase my product?
Does age make a difference in our brand awareness?
How do students in certain programs of study feel about the availability of student resources?
Is there a relationship between a particular program of study and student satisfaction?
When you need to dig deeper and reveal data on a more granular level, crosstabs and filters are your best bet. Save time, reveal detailed insights, and get more from your data with SurveyMonkey and cross-tabular data. Visit our help section to find out how to create your own crosstab report.