As others have mentioned, what jamovi does, it does well. However, its ability to manage data is quite limited at the moment (see list below). I've written an extensive review of jamovi, which you can read here: http://r4stats.com/articles/software-reviews/jamovi/. You can read my reviews of all the menu-based GUIs for R here: https://r4stats.com/articles/software-reviews/. The R GUI that seems to be in the lead at the moment is BlueSky Statistics: http://r4stats.com/articles/software-reviews/bluesky/. I'm a bit of a stat package addict, so I'd be happy to answer questions about any of those.

Here's the conclusion of my jamovi review:

Conclusion

jamovi is a gem of a package, one that looks so good I asked the developers if they had an artist or user interface designer on the team. They don’t, but clearly, they have put a lot of thought into how to make the software beautiful and easy to use. They have also chosen their options carefully so that each analysis includes what that a researcher would want to see.

Their creation of the jmv package is a bold move, one that promises to greatly simplify the number of separate packages a coder would need to learn, though in so doing they challenge the existing way of working with R. Just as the tidyverse set of commands is controversial, the jmv package is also likely to ruffle some feathers.

As nice as jamovi is, it also lacks significant features, including: the ability to see and save data management syntax; the ability to handle date/time variables; the ability to perform many more fundamental data management tasks; the ability to save new variables such as predicted values or factor scores; the ability to save models so they can be tested on hold-out samples or new data sets; and the ability to rerun the analysis on new data sets using the GUI [the jamovi developers just added this latter feature, so I'll update the review].

You can try both of them and judge. I favor Jamovi due to its GUI design, practicality and ease of use. JASP has the advantage of extra Bayesian analysis option.

Thanks. But I was trying to get an opinion before downloading either one. Mine is an Intel Atom laptop with limited resources.

Jamovi is my default pick too

Jamovi is limited but covers all of the basics. I judged it acceptable for teaching purposes. It seems pretty easy to use but doesn’t measure up to my favorite professional packages.

At this point, use both. JASP does Bayesian analyses, network analysis, and SEM while JAMOVI does not do this (yet). On the other hand, JAMOVI does HLM, confirmatory factor analysis, simple mediation and moderation, equivalence testing, sample size estimation, simple main effects, and some extensions to R. Both can read SPSS and CSV files but JAMOVI can also read JASP work spaces.

I've tried both packages on MAC, Windows, and Linux ad they work perfectly. As they're both free, you've got nothing to lose except disk space.

I'm using versions 9.0+ of both and I imagine great things to come.

Jamovi has been getting better with each update. I have been able to replicate all of my analyses in spss and r into jamovi. Now, it has included functions for computing and transformation of variables. I hope that they include functions for better data manipulation but that can be done easily in r.

I like Jamovi. It seems to have a better sense of how analysis should be done than some of the alternatives. I mean, it integrates descriptive plots, effect sizes, and checking model assumptions in the analysis. It also has a very attractive output. I think this approach will have the best educational value going in to the future.

There of course will be some things that will drive me crazy. Like they advocate using a test for homogeneity when conducting anova. This is simply bad practice.

Also, they list some nonparametric tests under parametric analogues (Mann-Whitney under t-tests). I think this is wrong headed pedagogically.

But, I know.... If I want to be an influence, I should be working with their development team.

It is worthwhile to note that both packages have been greatly developed with new analyses and features since the first answer. Therefore please try out the latest releases of both. JASP also has a bigger development team, who are highly responsive to requests through their GitHub page.

I usually work with R. But I've recently jumped onto Jamovi, and it's a wonderful software for very popular analysis like regressions, factor analysis and other stuff.

Update: I've downloaded the latest (current) version 0.9.5.6 of Jamovi and would like to gladly report that Yes, as Ray Garza said, Jamovi is improving by each passing day.

I particularly liked the option to create a transform function once and use it afterwards for other variables.

For me, the best is JASP. It's very easy and useful; furthermore, the tables are made acording to APA guidelines.

As others have mentioned, what jamovi does, it does well. However, its ability to manage data is quite limited at the moment (see list below). I've written an extensive review of jamovi, which you can read here: http://r4stats.com/articles/software-reviews/jamovi/. You can read my reviews of all the menu-based GUIs for R here: https://r4stats.com/articles/software-reviews/. The R GUI that seems to be in the lead at the moment is BlueSky Statistics: http://r4stats.com/articles/software-reviews/bluesky/. I'm a bit of a stat package addict, so I'd be happy to answer questions about any of those.

Here's the conclusion of my jamovi review:

Conclusion

jamovi is a gem of a package, one that looks so good I asked the developers if they had an artist or user interface designer on the team. They don’t, but clearly, they have put a lot of thought into how to make the software beautiful and easy to use. They have also chosen their options carefully so that each analysis includes what that a researcher would want to see.

Their creation of the jmv package is a bold move, one that promises to greatly simplify the number of separate packages a coder would need to learn, though in so doing they challenge the existing way of working with R. Just as the tidyverse set of commands is controversial, the jmv package is also likely to ruffle some feathers.

As nice as jamovi is, it also lacks significant features, including: the ability to see and save data management syntax; the ability to handle date/time variables; the ability to perform many more fundamental data management tasks; the ability to save new variables such as predicted values or factor scores; the ability to save models so they can be tested on hold-out samples or new data sets; and the ability to rerun the analysis on new data sets using the GUI [the jamovi developers just added this latter feature, so I'll update the review].

I prefer to use JASP. It is very simple and has a very intuitive interface and allows for a great diversity of analysis.

Both are extremely intuitive. I think JAMOVI is becoming a wonderful statistics package. In a few seconds, you can run tests that in R would take you half an hour. It has multiple modules you can install to run more specific statistical models. The graphs you can create are very high quality. However, for some reason they can't be properly edited so unfortunately most of the time you need to take them to Illustrator afterwards.

I'd say you can use JAMOVI for everything but for Bayesian stats, in which case you can use JASP. Both are extremely easy to use. In my opinion, though, JAMOVI is superior than JASP and will surpass it in every sense soon enough as it's now incorporating Bayesian tests too. But, again, the only significant limitation I find with JAMOVI and JASP is graph edition. JAMOVI has some very nice graph themes, but it definitely needs more themes and by all means more edition options! You usually end up taking graphs to Illustrator afterwards, which is a pain.

Anyway, I absolutely recommend both, but JAMOVI is superior in my opinion.

great to hear about JAMOVI .Can any one please share JAMOVI instructor manual and learning material to share and teach students with examples and sample files .

Thanks in advance

Did you have a look at jamovi's web site? Try community resources.

D Ravindran

Jamovi User guide:

https://www.jamovi.org/user-manual.html

E-book on 'learning statistics with Jamovi' by Navarro and Foxcroft:

https://drive.google.com/file/d/1wSttS-IlhRDtlRNUfATYBSERQteg8jh5/view

Jamovi playlist in Datalabcc youtube channel:

https://www.youtube.com/watch?v=Ej9e8lzaeDE&list=PLkk92zzyru5OAtc_ItUubaSSq6S_TGfRn

I found https://www.jamoviguide.com/going-further.html for online tutorial and https://drive.google.com/file/d/1wSttS-IlhRDtlRNUfATYBSERQteg8jh5/view for freebook "Learning Statistics with Jamovi"

Thanks a lot to Jamovi. It is very helpful

With the new update of jamovi you can perform Bayesian analysis and complex moderate mediations.

I've used both JASP and jamovi, as well as SPSS, R, Python, StatCruch, etc. I use R for anything complicated but for everything else I love jamovi. jamovi and JASP were both developed by many of the same people, which explains their resemblance. Also, both were designed to resemble the menus and output of SPSS, which makes the transition easy.

JASP is designed with a strong emphasis on Bayesian statistics but jamovi is now able to do all of those same procedures by adding one of the free "modules." JASP is built on slightly older technology and Mac users like myself have to install X11 first, which is a slight pain. jamovi is built on HTML5, which means that it doesn't require any extra installations and that an online version is theoretically a simple issue (and the developers have said that's on their to-do list).

At the moment, JASP has a much richer set of sample datasets and allows for markdown commenting and text in output. I imagine jamovi will get those eventually.

While both programs run on R underneath the hood, jamovi has huge advantage in that it makes the code available if you run commands in "syntax mode." Those commands can then be run in regular R if you install the jmv package. jamovi also has a module that allows you to run any R code in the jamovi environment. These both make it a fabulous bridge between the two approaches.

I also believe that jamovi provides more built-in procedures for psychological research, such as psychometrics and confirmatory factor analysis. Also, the data management and recoding options have been greatly enhanced, to the point that they're better than SPSS.

In terms of learning jamovi, there are several resources, but I want to share the set of videos that I created for my own company, datalab.cc. You can see them at datalab.cc (https://datalab.cc/tools/jamovi) or on YouTube (https://www.youtube.com/watch?v=Ej9e8lzaeDE&list=PLkk92zzyru5OAtc_ItUubaSSq6S_TGfRn, a link was shared earlier). The videos are also available on LinkedIn Learning (https://www.linkedin.com/learning/introduction-to-jamovi), although that's a subscription service.

I strongly encourage my students and my colleagues to try jamovi unless and until they know that they need something more sophisticated. It's my go-to tool.

Bart

Nice set of videos. I’ve used R for about 5 years, recently discovered jamovi. Is there any reason to use JASP in addition to jamovi?

John T Cathey good question. I think JASP is still better at Bayesian analysis. It feels it was made for it and that the frequentist analyses included are just add-ons, while Jamovi feels the opposite way to me: it was made with the purpose of simplifying stats for people, and recently they have started adding Bayesian analyses to it. I think if they keep developing at this pace, in a year there won't be any advantage to use JASP if you already use Jamovi. But for now, I think JASP is still a bit better for Bayesian analysis than Jamovi.

I'm using both at this stage but I suspect Jamovi will come out on top due to its ability to have others develop modules to expand functionality. It also seems to be developed at a faster rate with the developers also very active on their forum. JASP lately added a modules function but I'm not sure whether that would be open to outsiders for adding new functions.

JASP includes SEM using lavaan syntax and has recently added a very good mediation analysis function. Jamovi has a GLM Mediation Analysis module (jAMM) which is well-documented and more powerful (e.g. conditional models) than that of JASP, although I suspect one could do much the same with the lavaan syntax option in the latter.

For me, both could still improve their data manipulation functionality, including ease of use. I'm also not too impressed with the way Jamovi generates tables for exporting/copying to Word. It creates lots of (unnecessary) small cells for spacing purposes which become cumbersome when formatting according to a specific reference style or journal requirement. JASP provides much cleaner tables.

Jamovi. What particularly do you see emerging?

I've included Jamovi and JASP examples in the new edition of my stats textbook ( https://www.macmillanihe.com/page/detail/Basic-Statistics-for-Psychologists/?K=9781137607461). I recommend adding Jamovi to your course if you want to teach stats with R. Makes it much more digestible for your colleagues in the department, who do not yet know R and fear they will no longer be able to help their students with the analysis of their data. Everyone with knowledge of SPSS can learn Jamovi in one or two hours.

Why does researcher prefer to use Jamovi instead of SPSS to analyze the data? How about PLS-SEM? I personally prefer PLS-SEM because of the friendly-user features in the program.