Jamovi Desktop is a free, open-source statistics application with a spreadsheet-style workspace where analyses update live as you change variables, built for students, researchers, and analysts who need reliable results without writing code.
Jamovi Desktop is a statistical analysis application built on top of R, the language that already powers most academic statistics. You open a dataset as a spreadsheet, pick an analysis from a menu, and the results appear in the same window as they are computed. Every analysis becomes a panel you can reopen and edit, so changing a variable or a test option updates the output immediately instead of forcing you to rerun a script. Files are saved in the .omv format, which keeps the data and the analyses together, and common formats such as CSV, Excel, SPSS, Stata, and SAS import directly. The workflow suits anyone who needs defensible statistics but does not want to maintain R code to get them.
Reproducibility is the quiet strength of the design. Because analyses live alongside the data rather than in a separate output file, a colleague can open your .omv file and see exactly which test ran on which columns with which options ticked. The optional syntax mode exposes the underlying R commands for anyone who wants to audit or reuse them, and the Rj editor in Jamovi Desktop lets you write custom R when a built-in option does not exist. Add-on modules from the jamovi library extend the base install with extra procedures, so a psychology lab and a manufacturing team can run the same core program with different statistical toolkits. Teams that also handle spatial data often pair it with tools like Rust GIS.
Speed of iteration is where Jamovi Desktop earns its place. A typical session moves from import to descriptive statistics to a t-test or regression in minutes, because the analysis panel stays live: swap a dependent variable and the table, the assumption checks, and the chart redraw all update at once. Nothing is lost when you change your mind, and nothing has to be copied into a second document to see whether an effect survives a different specification. The interface also lowers the barrier for teaching. Students see the menu option, the output, and the R syntax side by side, which makes the logic of a test visible instead of hidden behind a command line. Output can be copied into Word or exported, so the same file serves the analysis and the write-up. Being free and open source means no licence cost for a classroom, a lab, or a small research group, and no vendor lock-in on the data files themselves.
Key capabilities and highlights.
Data sits in a familiar grid with named columns and declared measurement types. Recoding a value, pasting in extra rows, or switching a column from nominal to continuous instantly refreshes every analysis that depends on it, so Jamovi Desktop keeps the numbers on screen and the results in step instead of leaving you to reconcile them by hand.
Descriptive statistics, t-tests, ANOVA, correlation, regression, contingency tables, and their non-parametric counterparts are reached through plain menus. Each dialog returns the main result alongside assumption checks, effect sizes, and confidence intervals, which is precisely what a reader of your report will ask for before accepting the conclusion.
Every analysis stays open as a panel rather than a frozen block of output. Drag in a different grouping variable, tick an extra option, or exclude a subset and the table redraws on the spot, so comparing two model specifications takes a couple of clicks instead of two separate runs and a manual diff.
A toggle reveals the R commands behind any result. That helps when auditing a test before publication, when writing a method section that names the procedure precisely, or when learning how a menu choice maps onto real code without abandoning the analysis you are already working on.
When a built-in procedure does not cover your case, the Rj editor runs your own R code inside the same session with the loaded data in scope. Output returns as a normal analysis panel, so custom work saves, refreshes, and shares exactly like everything else in the project.
Optional modules add specialised procedures on top of the core install: meta-analysis, power and sample-size work, further factor-analytic options, and niche tests from particular fields. They install from inside the application and appear as ordinary menu entries, so the interface stays familiar as your requirements widen.
Build derived columns with formulas, collapse or relabel categories, apply filters that restrict an analysis to a subset, and transform skewed measures before testing. The raw data stays untouched, which means a reviewer can trace any reported number in Jamovi Desktop back to the source column it came from.
Results and charts copy into a word processor or export as image files, so finishing an analysis and drafting the results section happen in one pass. Table styling stays consistent across every output in the project, which saves the usual evening of retyping figures into a document and checking them twice.
Browse historical releases and previous builds.
Common questions about Jamovi Desktop.
Yes. The application is free and open source, and the add-on modules distributed through the jamovi library are free as well, so there is no licence cost for an individual, a classroom, or a small research group.
No. Every standard analysis is available through menus and dialog boxes. R only matters if you want to inspect the commands behind a result or write custom procedures in the Rj editor.
It reads its own .omv files plus common formats such as CSV, Excel workbooks, SPSS, Stata, and SAS data files, so an existing dataset usually needs no conversion before you can start analysing it.
An .omv file is the native project format. It stores the dataset and every analysis built from it in one place, so reopening the file restores your results exactly as you left them, options included.
It suits teaching well because students see the menu choice, the resulting table, and the equivalent R syntax side by side, which makes the reasoning behind a test visible rather than hidden behind a command line.
Yes. The jamovi library hosts optional modules covering additional statistical procedures and utilities. They install from inside the application and show up as extra menu entries without changing how the rest of the interface works.
You can, through the Rj editor. Code you write there runs in the same session, has access to the loaded data, and produces output panels that save with the rest of the project.
Inside Jamovi Desktop, analyses are stored as instructions tied to specific columns rather than as fixed text. Editing the data re-runs the affected tests automatically, so the saved file keeps reflecting what was actually computed.
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