Contingency table - Cross tabulation

Cross tabulation (crosstabs) builds a contingency table: a grid that shows how often each combination of answers to two questions appears. In Responsly you use this under Analysis → Crosstabs to see relationships between segments, attitudes, ratings, or other closed-ended fields—often faster than scanning separate charts for every pair.

Crosstabs help you make decisions from collected data by surfacing trends and correlations between answers. They are a standard step in survey and market research analysis.

How to create a crosstab in Responsly

Run a survey with at least two closed questions (for example Choice or Scale).
Open your form and go to Analysis → Crosstabs.
Select the two questions you want to cross.
Click Generate table to view the contingency table for that pair.

Cross tabulation view in Analysis → Crosstabs

A control below the table lets you download the generated table to Excel for filtering, pivoting, or including in reports. For other export options from Analysis, see How do I export survey data?.

How crosstabs fit into broader analysis

Use crosstabs when you already know which two dimensions you want to compare (for example satisfaction by region, or product interest by role). For workflow tips on interpreting and presenting survey results, see How to analyze survey data.

FAQ

What is cross tabulation in Responsly?

Cross tabulation (crosstabs) builds a contingency table that shows how answers to one closed-ended question relate to answers to another—useful for spotting patterns, segments, and correlations in your results.

Which question types can I use in Crosstabs?

Use at least two closed questions, such as Choice or Scale. Crosstabs compare categories or scale bands across two variables at once.

How do I export a crosstab from Responsly?

After you generate the table, use the download control below it to save the crosstab to Excel for further analysis or sharing.

Why use a contingency table instead of only summary charts?

A contingency table shows the joint distribution of two answers—who chose both A and X—so you can see overlap that single-question summaries might hide.