Cross TabulationEnterprise
Cross tabulation compares response results across a chosen reference variable to reveal differences between groups. For example, you can analyze satisfaction by occupation or recommendation intent by age group.
Basic Structure of Cross Tabulation
A cross tabulation has two components.
Reference variable
The question used as the basis for comparison. (e.g., gender, age group, occupation) → This answers "Who are we comparing?"
Analysis variable
The question being compared across the reference variable. (e.g., price satisfaction, purpose of use, recommendation intent) → This answers "What are we comparing?"
How to Set It Up
Step 1. Set the Reference Variable
- From the question list on the left, drag the question you want as a basis for comparison into the 'Reference variable' area.
- Example: Occupation
Step 2. Add Analysis Variables
- Add the questions you want to compare to the 'Analysis variable' area.
- Examples:
- Purpose of service use
- Satisfaction with price
- Willingness to recommend to others
How to Interpret Results
Example
(Reference variable: Occupation, Analysis variable: Price satisfaction)
| Occupation | Satisfaction % |
|---|---|
| Office worker | 78% |
| Student | 60% |
| Self-employed | 82% |
→ You can see that students report relatively lower price satisfaction.
This is how cross tabulation helps reveal differences between groups.
Higher / Lower
Use the Higher / Lower buttons at the top of the results to
quickly find groups that score above or below the overall average.
- Higher: highlights groups above the overall average
- Lower: highlights groups below the overall average
This helps you quickly surface key insights when writing reports.
Notes
- Accurate comparisons require a sufficient number of responses in each group.
- When a group has too few responses, be cautious about interpreting the results.
- Cross tabulation shows differences; root-cause analysis requires further investigation.