jamovi

jamovi is a new “3rd generation” statistical spreadsheet. designed from the ground up to be easy to use, jamovi is a compelling alternative to costly statistical products such as SPSS and SAS.

jamovi provides a complete suite of analyses for (not just) the social sciences; t-tests, ANOVAs, correlation and regression, non-parametric tests, contingency tables, reliability and factor analysis. Need more analyses? then see the jamovi library – a library of additional analyses contributed by experts in their field.

jamovi is a fully functional spreadsheet, immediately familiar to anyone. Enter, copy/paste data, filter rows, compute new values, perform transforms across many columns at once – jamovi provides a streamlined spreadsheet experience, optimised for statistical data.

Love R? Check out jamovi's “syntax mode”, where the underlying R syntax for each analysis is made available. Just copy and paste this into R for a seamless transition. Alternatively, run R code directly inside jamovi with the Rj Editor.

jamovi's ease of use makes it ideal for introducing people to statistics, and it's advanced features ensure students will be well equipped for the rigours of real research when they graduate. Over 300 universities use jamovi to teach statistics – don't let your institution get left behind! Also check out the great video and textbook resources available.

jamovi is a community project, and invites contributions from people all over the world. Central to the jamovi ethos is that scientific software should be “decentralised”. Any one should be able to publish graphical accessible analyses, not just those with big grants and huge budgets.

Reproducibility shouldn’t be complicated, that’s why jamovi saves your data, your analyses, their options, and the results all in the one file. This file can be backed up, shared with colleagues, and at any time loaded back into jamovi – it’s like you never left.

Does jamovi not provide an analysis which is important in your field? Why not “roll your own”? jamovi makes it easy for R programmers to develop and publish their own analyses. Head on over to our developer’s hub for some easy to follow tutorials.