Short answer: pick jamovi if you are coming from SPSS, you need to clean and recode your data inside the program itself, and you want a menu-driven route to SEM, network analysis or power analysis. Pick JASP if your work involves Bayesian statistics seriously: Bayes factors, robustness curves and posterior distributions. Everything else is close to a tie, because both are free, both work without writing a single line of code, and both comfortably cover the usual ground (mean comparisons, correlations, regression, ANOVA and factor analysis). The seven differences that actually decide are in the table below.
Jamovi vs JASP: the 7 differences at a glance
| What actually decides | JASP | jamovi |
|---|---|---|
| Bayesian analysis | Native across the whole menu: Bayes factors, robustness curves, posterior plots | Available through a module, with narrower coverage and fewer diagnostics |
| Cleaning and editing data | Built around the imported file: no working data editor, so serious cleaning happens outside and you re-import | Full spreadsheet: edit cells, recode, build computed and transformed variables |
| Add-on modules | Curated library maintained by the JASP team | Larger library, open to community authors (jAMM, semlj, jpower, Rj) |
| SEM and CFA | Yes, by writing the model in lavaan syntax | Yes, with a menu-driven builder, no syntax required |
| Power analysis | Not in the default menus; check the module library | The jpower module, the usual route for a thesis |
| Getting results into the manuscript | Tables closest to APA 7; copy into Word with good fidelity | Clean output; the smoothest route is export to HTML or PDF |
| Speed | Fine at typical sample sizes; slows with MCMC and large files | Lighter and faster for standard analyses |
Both module libraries move fast. If one specific analysis is going to decide your choice, open the library inside each program and confirm it is there before you commit a whole thesis to one of them.
Read the table as a tiebreaker, not as a scoreboard. Neither program wins on points, because for the analyses that fill most empirical papers they are genuinely equivalent. What you are looking for is the one row that matters in your project: if you are going to report Bayes factors, that row settles it for JASP; if your data arrives messy and you will be recoding for weeks, that row settles it for jamovi.
If you got here comparing JASP with SPSS, that is a different decision with a different answer. The short version: JASP and jamovi both do for free what SPSS charges a licence for, and both beat it on Bayesian analysis and on output that arrives closer to APA format. SPSS still wins on institutional inertia, on the sheer volume of teaching material written for it, and on having colleagues nearby who can help when something breaks. The full breakdown, with R in the picture too, is in SPSS vs R vs JASP.
That said, the question "should I use JASP or jamovi?" deserves more than a table. Each program has a user profile for which it is clearly superior, and knowing which one you are saves you from testing both from scratch and redoing your analysis halfway through.
The origins reveal different philosophies
JASP was born at the University of Amsterdam in 2013 with an explicit goal: make Bayesian statistics accessible to researchers who do not know R. The interface is designed so that a lifelong frequentist can, with one extra click, get the Bayesian equivalent of the analyses they already know. That defines everything else: JASP prioritizes statistical depth, especially in the Bayesian domain, over breadth of available analyses.
Jamovi arrived in 2018 as a direct fork of R + SPSS: the team wanted SPSS usability with the power of R underneath, at no licence cost. Its module system allows users to install community-developed analyses, which greatly expands the available capabilities. Jamovi prioritizes extensibility and a gentle learning curve for researchers coming from SPSS.
Interface and learning curve
Both programs have a spreadsheet on the left and results on the right, making them intuitive for SPSS users. However, there are practical differences.
JASP is built around a data file you import from outside (CSV, SPSS, Excel). It does not give you a working data editor, so if you spot a coding error mid-analysis the realistic move is to fix the source file and re-import. With clean data that costs you nothing. With the messy data most theses actually start with, that loop gets old fast.
Jamovi lets you edit data directly in the built-in spreadsheet, making the workflow more similar to SPSS. It also saves the entire project, data and analyses together, in a single .omv file, making it easy to share complete work with a thesis advisor or collaborator without anything getting lost.
Available analyses
This is where differences are most visible. For the analyses most common in any empirical discipline (mean comparisons, correlations, regression, ANOVA, factor analysis), both programs have solid coverage. Differences appear at the edges.
JASP excels at:
- Full Bayesian statistics (Bayes factors for t-tests, ANOVA, correlation, regression, contingency models). If you do not use it yet, this introduction to Bayesian statistics gets you oriented before you even open the program
- Meta-analysis with Bayesian integration
- Inference with credible intervals and visualized posterior distributions
- Bayesian robustness curves
Jamovi excels at:
- A module library open to outside authors: advanced repeated measures, network analysis, extended contingency tables. Both programs do SEM, but jamovi's jAMM and semlj let you specify the model from menus, while JASP expects you to write it in lavaan syntax
- Power analysis (the jpower module), handy when you are calculating the sample size for your study
- Factor analysis with a more flexible interface than JASP
- A gentle on-ramp to R: syntax mode prints the
jmv::call behind every analysis you click, and the Rj module lets you run your own R on the same data. Useful if you suspect you will eventually have to leave the menus behind - More complete effect size measures reported by default
Output and APA 7 compatibility
JASP generates nearly APA-ready tables. The notation is correct, p values follow conventions, and the output can be copied directly into Word with fairly good fidelity. Column widths and some formatting may need minor adjustments.
Jamovi also produces clean output, but its strength is direct export to HTML or PDF with decent formatting. For theses or reports that need to be integrated into a Word document, the most comfortable workflow in Jamovi is to export to HTML and then import into Word, or copy tables directly from the results window.
Neither eliminates the need to review output before pasting into a manuscript. Both have some formatting habits that require correction, such as decimal point vs. comma depending on the OS language, but overall both are close enough to APA standard that you do not need to reformat everything by hand.
And whichever of the two you end up in, the hard part is not producing the output but reading it: knowing which of those rows goes into the manuscript, which one is decoration and what the number actually means. If you get stuck there, you can paste the output and get it explained back to you in plain English, with the APA 7 sentence already written. The rest of the free statistics tools cover the pieces neither program formats for you: APA tables, effect sizes, reliability coefficients, forest plots.
Performance and stability
JASP can be slow with large datasets (several thousand rows) and with complex Bayesian analyses that involve MCMC sampling. With typical applied research sample sizes (100 to 500 cases), speed is perfectly acceptable. The program updates regularly and is stable.
Jamovi is generally faster and lighter. Being R-based, the modules leverage the full power of the language, and for standard analyses the response is nearly instantaneous. The stability of community modules varies: core modules are robust, but some third-party modules may behave unpredictably after version updates.
Which one to choose based on your profile
Choose JASP if: your research includes Bayesian statistics or you want to incorporate it; reviewers at your target journals ask for Bayes factors; or you are in an academic context where Bayesian inference is an expectation. If you want to see it running before deciding, this chi-square tutorial in JASP walks through the full workflow with real data.
Choose Jamovi if: you are coming from SPSS and want the smoothest transition; you want SEM, network analysis or power analysis without writing syntax; you need to clean and recode data within the program itself; or you want to drift into R gradually by reading the code behind each analysis.
For a standard questionnaire-based doctoral thesis (group comparisons, regression, EFA/CFA), Jamovi is usually the more practical choice due to its module flexibility and ease of editing data. For publishable research where Bayesian statistics is central, JASP has no real free competitor.
Want this comparison (SPSS included) in one glance to save or print? There's a free downloadable SPSS vs JASP vs jamovi poster, as a high-resolution PNG.
One warning before you submit anything: neither JASP nor Jamovi tells you whether the analysis you chose will survive peer review. Once you have the draft written, you can run it through the Q1 Reviewer, a free Reviewer 2 style pre-review that flags what a reviewer would object to about your analyses and your reporting before the journal does.
If the real question is broader and SPSS or R are still on the table, the SPSS vs R vs JASP comparison puts the same decision in a wider frame. And if you already know you will work with Jamovi in your thesis, the Jamovi guide for doctoral theses takes you step by step from installation to final reporting.