Section author: Danielle J. Navarro and David R. Foxcroft

Getting started with jamovi

Robots are nice to work with.

—Roger Zelazny (Dismal Light, 1968)

In this chapter I’ll discuss how to get started in jamovi. I’ll briefly talk about how to download and install jamovi, but most of the chapter will be focused on getting you started with finding your way around the jamovi GUI. Our goal in this chapter is not to learn any statistical concepts: we’re just trying to learn the basics of how jamovi works and get comfortable interacting with the system. To do this we’ll spend a bit of time looking at datasets and variables. In doing so, you’ll get a bit of a feel for what it’s like to work in jamovi.

However, before going into any of the specifics, it’s worth talking a little about why you might want to use jamovi at all. Given that you’re reading this you’ve probably got your own reasons. However, if those reasons are “because that’s what my stats class uses”, it might be worth explaining a little why your lecturer has chosen to use jamovi for the class. Of course, I don’t really know why other people choose jamovi so I’m really talking about why I use it.

  • It’s sort of obvious but worth saying anyway: doing your statistics on a computer is faster, easier and more powerful than doing statistics by hand. Computers excel at mindless repetitive tasks, and a lot of statistical calculations are both mindless and repetitive. For most people the only reason to ever do statistical calculations with pencil and paper is for learning purposes. In my class I do occasionally suggest doing some calculations that way, but the only real value to it is pedagogical. It does help you to get a “feel” for statistics to do some calculations yourself, so it’s worth doing it once. But only once!
  • Doing statistics in a conventional spreadsheet (e.g., Microsoft Excel) is generally a bad idea in the long run. Although many people likely feel more familiar with them, spreadsheets are very limited in terms of what analyses they allow you do. If you get into the habit of trying to do your real life data analysis using spreadsheets then you’ve dug yourself into a very deep hole.
  • Avoiding proprietary software is a very good idea. There are a lot of commercial packages out there that you can buy, some of which I like and some of which I don’t. They’re usually very glossy in their appearance and generally very powerful (much more powerful than spreadsheets). However, they’re also very expensive. Usually, the company sells “student versions” (limited versions of the real thing) very cheaply, and then they they sell full powered “educational versions” at a price that makes me wince. They will also sell commercial licences with a staggeringly high price tag. The business model here is to suck you in during your student days and then leave you dependent on their tools when you go out into the real world. It’s hard to blame them for trying, but personally I’m not in favour of shelling out thousands of dollars if I can avoid it. And you can avoid it. If you make use of packages like jamovi that are open source and free you never get trapped having to pay exorbitant licensing fees.
  • Something that you might not appreciate now, but will love later on if you do anything involving data analysis, is the fact that jamovi is basically a sophisticated front end for the free R statistical programming language. When you download and install R you get all the basic “packages” and those are very powerful on their own. However, because R is so open and so widely used, it’s become something of a standard tool in statistics and so lots of people write their own packages that extend the system. And these are freely available too. One of the consequences of this, I’ve noticed, is that if you look at recent advanced data analysis textbooks then a lot of them use R.

Those are the main reasons I use jamovi. It’s not without its flaws, though. It’s relatively new[1] so there is not a huge set of textbooks and other resources to support it, and it has a few annoying quirks that we’re all pretty much stuck with, but on the whole I think the strengths outweigh the weakness; more so than any other option I’ve encountered so far.


[1]As of writing this in August 2018.