Then click the + button in the main ribbon.Set the development path to the CircularStatistics repo.Download, fork, or clone the CircularStatistics repo.If you would like to contribute to the module, you can use JASP as a development tool: To look at it, just open the ElNino.jasp file in JASP. A commented example analysis for this data set can be found here. A toy data set to play with is the ElNino.csv. Just press the information button in the submenus. A documentation of the functionalities can be found in the GUI itself. This can be done in every submenu separately. Most important is that you specify the period of your data for each analysis. You can use the submenus of Circular Statistics to run descriptive analysis (such as plotting) or hypothesis tests.
You can now load a dataset in JASP as usual and start the analysis. That's it! The module and all it's R dependencies will be installed automatically.
PrerequisitesĪn installed JASP version >= 0.11 (it needs to support dynamic modules).
The underlying R code is based on the package circular by U. It provides basic methods in the JASP GUI such that analyses can be run without the need for programming. It can be installed additionally to the JASP core module. Plots like this may be fairly basic for some, but for me this is a massive first step in feeling more confident with calculating and presenting statistical analyses in my work.This is a circular statistics module for JASP. Within not very much time, I found myself able to run analyses and produce plots that had previously given me a headache. In addition, I found this guide for students which is super helpful. What’s more, the support materials on their website are excellent, and offered in a range of formats including text, GIFs and YouTube videos.
I’m very happy to say that I found it very easy to use.
Tasked with demonstrating how to use JASP with little time to prepare, I put this claim to the test. Their website boasts of “an intuitive interface that was designed with the user in mind”. JASP is a free, open-source statistics tool supported by the University of Amsterdam. The only new aspect was a practical activity using a piece of software called JASP, which I had not heard of. The lecture was ‘introductory’ enough that I felt perfectly comfortable with teaching the content. It was an introductory lecture about quantitative research methods for language analysis. This week, I was asked to cover a lecture for a colleague who had to go off work at short notice.
But sometimes you just want to be able to sit and get on with something yourself. Another solution is to work with someone who CAN do what you can’t do (and, ideally, offer them something in return!). But I have always worried that the learning curve would be so steep that I may ‘waste’ too much time trying and failing to get things to work. I know other researchers who have done just that and seem to be much more confident when complex statistical analysis is required. I have long been aware of the best solutions to my woes: one is to find the time to knuckle down and learn how to understand and use stats software like R. I have solid foundational knowledge, thanks to an A-level in maths, but my confidence in whipping up pretty graphs and analysing complex datasets is low. When it comes to linguistic analysis, I am a self-confessed statistics-phobe, and, for me, any instance in my research where complex statistical analysis and visualisation is required has always been the most challenging part of the process.