We have developed a series of exercises to become familiar with the software jamovi.
Note: Since we continuously improve the tutorials, let us know if you discover mistakes, or if you have additional resources we can refer to. The source code is available via Github. If you want to be the first to be informed about updates, follow Rens on Twitter.
This tutorial introduces the basics of jamovi for beginners. Starting from jamovi installation, we explain the screen structure of jamovi, how to load a dataset, and how to explore and visualize data. Readers will further learn ways to perform such statistical analyses as correlation analysis, multiple linear regression, t-test, and one-way analysis of variance, all from a frequentist viewpoint. Given the integrative power between jamovi and R, one section is designed to help readers to make use of the best of both jamovi and R.
This tutorial explains how to conduct Bayesian analyses in jamovi with default priors for starters. With step-by-step illustrations, we perform and interpret core results of correlation analysis, multiple linear regression, t-test, and one-way analysis of variance, all from a Bayesian perspective. To enhance readers’ understanding, a brief comparison between the Bayesian and frequentist approach is provided in each analytic option. After the tutorial, we expect readers can perform basic Bayesian analyses and distinguish its approach from the frequentist approach.
This tutorial explains how to interpret the more advanced output and to set different prior specifications in conducting Bayesian regression analyses in jamovi. We guide you to various options in the options panel and introduce concepts including Bayesian model averaging, prior model probability, posterior model probability, inclusion Bayes factor, and posterior exclusion probability. After the tutorial, we expect readers can deeply understand the Bayesian regression and perform it to answer substantive research questions.