Overview
jamovi looks like a friendly spreadsheet with stats menus, but every analysis runs on R underneath — and ‘syntax mode’ reveals the exact R code your clicks generated. That makes it uniquely good as a bridge: teach concepts by menu, transition to code by reading what the menu did.
Why researchers use it
- R under the hood — click-to-code transparency via syntax mode
- Module library — community add-ons for mediation, meta-analysis, and more
- Genuinely free — open source, no editions, no nags
Getting started
- Install jamovi; open your CSV — it edits in place like a spreadsheet.
- Run analyses from the menus; toggle syntax mode to see the R equivalents.
- Add modules from the library (e.g., GAMLj for mixed models) as needs grow.
The honest review
Strengths. The best teaching path from point-and-click to reproducible R, and the .omv file keeps data + analyses + output together for supervision meetings.
Limitations. Feature depth trails R itself; large datasets strain it; the click-first habit can delay rather than encourage the move to scripts if you let it.
When NOT to use this
Same ceiling as all menu tools: if the analysis is the contribution of your paper, do it in scripted R/Python for full reproducibility — jamovi is the bridge, not the destination.