jamovi vs JASP: Choosing a Free, Open-Source Alternative to SPSS

jamovi and JASP are the two serious free, open-source alternatives to SPSS. Both give you a point-and-click interface, both run R underneath, both produce publication-ready tables, and both cost nothing. If you are trying to get off an SPSS licence — or equip a lab or a course without one — these are the two realistic options, and the choice between them comes down to a small number of genuine differences.

This guide compares them on the things that actually determine which one you should install, including the reproducibility and data-management questions that generic software reviews skip.

What they have in common

It is worth being clear about the overlap first, because it is large:

• Both are free and open source. No licence, no seat count, no renewal.
• Both are built on R. The analyses you click through are executed by R packages, so the underlying computation is the same well-tested code the R ecosystem uses.
• Both offer a spreadsheet-style interface deliberately familiar to SPSS users.
• Both produce APA-style output tables that can go into a manuscript with minimal reformatting.
• Both read SPSS .sav files, which matters enormously if you are migrating existing datasets or inheriting a colleague’s data.
• Both update results live — change the data or an option and the output re-renders, rather than re-running a syntax block.

For a large fraction of standard analyses — t-tests, ANOVA, regression, chi-square, correlation, reliability — either will do the job and you will not notice much difference.

The real differences

	jamovi	JASP
Origin	Founded by developers who previously worked on JASP	Developed at the University of Amsterdam
Signature strength	Extensibility — a module library you install from inside the app	Bayesian analysis, offered as a first-class equivalent to nearly every classical test
R access	The Rj module lets you write and run R syntax inside jamovi	Primarily menu-driven; R is under the hood rather than exposed
Extending it	Community modules via the jamovi library; analyses are backed by the jmv R package, so a jamovi analysis can be reproduced in R	Modules ship with the application and are toggled on
File format	.omv	.jasp
Best fit	Labs that may need to extend beyond the built-in analyses, or drop into R	Teaching and research where Bayesian inference is wanted alongside the classical result

The Bayesian question, which is the actual differentiator

JASP’s distinguishing feature is not that it has Bayesian methods — it is that it offers a Bayesian counterpart to almost every frequentist test, in the same interface, at the same level of effort. You run a Bayesian t-test the same way you run a classical one.

That matters for a practical reason most software comparisons miss. The barrier to using Bayesian methods in applied research has rarely been conceptual — it has been that doing so meant learning a new toolchain (Stan, JAGS, brms) for what might be a single analysis in a single paper. JASP removes that barrier.

When you would actually want a Bayes factor

The honest answer is: in specific situations, not as a default.

• You want to quantify evidence for a null. A non-significant p-value does not distinguish “evidence of no effect” from “not enough data to tell.” A Bayes factor does. If your finding is a null result you intend to publish, this is the strongest reason to reach for it.
• You want to monitor evidence as data accumulates. Optional stopping is a serious problem for p-values and is not, in the same way, for Bayes factors — though this needs care and pre-registration rather than being a free pass.
• A reviewer asks how strong the evidence is, not merely whether it crossed a threshold.

Reporting one credibly

If you report a Bayes factor, report it properly: state the direction (BF₁₀ or BF₀₁ — these are reciprocals and confusing them inverts your conclusion), state the prior you used and why, and include a robustness check showing how the conclusion holds across reasonable alternative priors. JASP produces these robustness plots directly. A Bayes factor reported without its prior is roughly as interpretable as a p-value reported without the test.

Reproducibility and data management

This is where the choice has consequences beyond convenience, and where both tools differ from a script-based workflow in ways worth understanding before you commit a project to one.

The good news: both save the data, the analyses, and the output together in a single file. Reopening it reproduces the whole analysis. That is genuinely better than the classic SPSS failure mode of a .sav file plus a folder of pasted output with no record of which options were clicked.

The limitation: that single file is the record. A GUI click-path is not a diff-able, version-controllable artefact the way an R or Python script is. If your project needs an auditable analysis history — or if a journal or funder expects analysis code deposited alongside data — a point-and-click file is a weaker deliverable.

Two practical mitigations:

• jamovi’s jmv backing is the stronger story here. Because analyses map to jmv R functions, a jamovi analysis can be expressed as R code, which gives you a path to a scriptable, depositable artefact without redoing the work in a different tool.
• Deposit the project file and the raw data separately. A .omv or .jasp file is not a durable archival format and may not open in a decade. Archive the underlying data in an open format (CSV plus a codebook) alongside it.

If you are writing a data management plan that promises reproducible analysis, say which of these you are doing. “Analysis performed in JASP” is not on its own a reproducibility statement.

The institutional case

The reason departments look at these tools is usually budget. SPSS site licences are a recurring institutional cost, renewed annually, typically scaled by seat count — and that cost falls on departments that often have no way to grow it. Both jamovi and JASP remove it entirely.

Two things to weigh honestly before proposing a switch:

• Teaching materials are the real switching cost, not the software. Course notes, assignments and screenshots built around SPSS menus all need rewriting. Budget staff time for that, because it is the part that stalls migrations.
• Check what your collaborators and target journals expect. Neither tool is obscure, but if a multi-site project’s analysis plan specifies SPSS output, unilaterally switching creates friction at exactly the wrong moment.

Neither of these is a reason not to switch. They are reasons to plan it at a semester boundary rather than mid-project.

Choose jamovi if…

• You want the option to run R syntax without leaving the application.
• You expect to need analyses beyond the standard set, and want to install modules for them.
• Reproducibility via a scriptable path matters to you, and the jmv mapping is attractive.

Choose JASP if…

• You want Bayesian analysis available routinely, not as a special project.
• You are teaching, and want classical and Bayesian approaches side by side in one interface.
• You prefer a curated set of well-implemented analyses over an extensible library.

Choose neither if…

• Your analysis is genuinely complex — multilevel models with unusual structures, custom likelihoods, simulation. Use R or Python directly.
• You need a fully auditable, version-controlled analysis pipeline for a regulated or high-stakes context. Use a script.
• Your institution already provides SPSS and your workflow depends on it. Free is not automatically cheaper once retraining is counted.

Frequently asked questions

Is jamovi or JASP better?

Neither is better overall. JASP is the better choice if you want routine Bayesian analysis; jamovi is the better choice if you want extensibility and access to R syntax. For standard analyses they are close to equivalent.

Are jamovi and JASP really free?

Yes. Both are free and open source, with no licence fee, seat limit, or renewal.

Can they open SPSS files?

Both read SPSS .sav files, which makes migrating existing datasets straightforward.

Do they use R?

Both run R underneath. jamovi additionally exposes R directly through its Rj module, and its analyses are backed by the jmv R package.

Can I use them for publication?

Yes. Both produce APA-style output, and analyses run through well-established R packages. Report the software and version in your methods section as you would for any statistical tool.

Are they suitable for a reproducible analysis workflow?

Partly. Both bundle data, analyses and output into one reopenable file, which is better than SPSS’s typical practice. But a GUI click-path is not version-controllable like a script. If you need a depositable analysis artefact, jamovi’s jmv mapping gives the clearer route to R code, and you should archive raw data in an open format regardless.