Cross-Tabulation Calculator
Cross-Tabulation Calculator
Calculate cross-tabulation tables with counts, percentages, chi-square tests, effect sizes, and residual-based significance flags from CSV or XLSX data.
Open your data file
CSV or XLSX. Files are parsed in the browser before the workspace opens.
Drop your spreadsheet here
CSV or XLSX · up to 50 MB
What this is useful for
- Calculate a contingency table from two categorical variables.
- Check whether row and column variables are associated.
- Report chi-square, p-value, degrees of freedom, and Cramer's V.
- Export tables for research notes, client reports, or internal analysis.
Workflow
01
Upload CSV or XLSX
Open a spreadsheet or start from a sample dataset. The file is parsed in the browser.
02
Choose variables
Assign row and column variables, then add filters, weights, missing-value handling, or recodes.
03
Export the result
Review counts, percentages, statistics, charts, and optional AI interpretation before exporting.
What the calculator returns
For each selected row and column variable, the calculator returns observed counts, row percentages, column percentages, total percentages, row and column totals, and a grand total. It also computes the chi-square family of statistics and several association measures used in categorical-data analysis.
For 2 by 2 tables, the workspace can include Fisher's exact test and odds ratio output when the data is unweighted. Weighted tables are treated as descriptive and include explicit warnings for inferential interpretation.
When to use cross-tabulation
Use cross-tabulation when both variables are categorical: region by satisfaction, segment by product preference, exposure by outcome, or department by engagement level. If one variable is continuous, bin it first or use a different method.
FAQ
Is this the same as a contingency table calculator?
Yes. Cross-tabulation tables and contingency tables both summarize counts across two categorical variables.
Does the calculator include chi-square?
Yes. It includes Pearson chi-square, degrees of freedom, p-value, likelihood-ratio G-test, and effect-size measures.
Can I use weights?
Yes. Numeric weight variables are supported, with warnings that weighted inferential statistics should be treated as approximate unless the weights are valid frequency weights.