Quantitative analysis

Run descriptive statistics, group comparisons, and cross-tabs on your survey data, and save reproducible analyses.

Quantitative analysis lets you explore structured survey data: inspect descriptive statistics, compare groups, and build cross-tabs. Analyses can be weighted, filtered, and saved so you can rerun or export them later.

analysisquantitativestatisticsreport10-20 minutesIntermediateResearchersAnalystsProduct teams

Steps

Open quantitative analysis
Click Quantitative analysis in the sidebar's Analysis section to see every study you have available, or pick Quantitative from the switcher on a survey's Results tab to open the same workspace scoped to that survey. You can also import an external CSV dataset directly into its own quantitative workspace.
Review your data and variables
In the Data & variables tab, check how each column was typed (numeric, categorical, multiselect, or text) and exclude any rows you don't want counted in the analysis.
Choose an analysis method
In the Analysis tab, pick a variable to analyze and a method: Descriptive statistics (mean, median, min, max, standard deviation, and frequencies), Group comparison (break the same statistics down by a second variable), or Crosstab (cross the variable against a group variable).
Filter and weight
Narrow the analysis with filters on any variable, and optionally apply response weights so results reflect a target population rather than raw counts.
Save and reproduce analyses
Save a named analysis to return to its settings later. Saved analyses are flagged as stale automatically if the underlying data changes, so you know when to rerun them before exporting.

Quantitative analysis is a standalone, cross-survey workspace reached from the sidebar's Analysis section, and it is also embedded in a native survey's Results tab alongside Report and Qualitative analysis.

Three analysis methods are available: Descriptive statistics for a single variable, Group comparison to break those statistics down by a second variable, and Crosstab to cross two variables against each other.

Response weighting lets you correct for over- or under-sampled groups so summary statistics better reflect your target population.

Saved analyses are reproducible: each one stores its definition (variable, group, weight, filters, and excluded rows) and is marked stale if the dataset changes, prompting a rerun before you export or present it.

You can analyze responses from an existing survey or import an external CSV dataset that has no parent survey.