Short answer: pick Jamovi if you are coming from SPSS, you need to edit your data inside the program itself, and you want extra modules (SEM, network analysis, power analysis) or the equivalent R code for every 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 differences that actually decide are seven, and you have them summarized in the table below.
That said, the question "should I use JASP or Jamovi?" deserves more than a one-line verdict. 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.
In JASP, data is imported as an external file (CSV, SPSS, Excel) and cannot be edited directly within the program. If you spot an error in the data mid-analysis, you have to exit, fix the source file, and re-import. This can create friction during exploratory analysis.
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:
- Module system: advanced repeated measures, network analysis, SEM via jAMM and semlj modules, extended contingency tables
- 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
- R integration: you can view the equivalent R code for any analysis with one click (the Rj module), ideal for learning or customizing
- 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.
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; your work involves SEM, network analysis, or power analysis; you need to edit data within the program itself; or you want to gradually learn R by seeing the equivalent code.
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.
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 you need to decide between SPSS, R, JASP, and Jamovi for a specific project, the broader four-tool comparison can help you contextualize the choice. 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.