We have developed a series of exercises to become familiar with the software JASP.
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.
1. JASP for beginners
This tutorial introduces the fundamentals of JASP for starters. We guide you from installation to interpretation of results via data loading and data management. After the tutorial, we expect readers can easily perform correlation, multiple linear regression, t-test, and one-way analysis of variance and draw conclusions from outputs in JASP.
2. Bayesian Analyses with Default Priors
This tutorial illustrates how to perform Bayesian analyses in JASP with default priors for starters. We deal with basic procedures to do Bayesian statistics and explain ways to interpret core results. In each analytic option, a brief comparison between Bayesian and frequentist statistics is presented. After the tutorial, we expect readers can perform correlation analysis, multiple linear regression, t-test, and one-way analysis of variance, all from a Bayesian perspective, and understand the logic of Bayesian statistics.
3. Bayesian Analyses with Informative Priors (using JAGS)
This tutorial illustrates how to perform Bayesian analyses in JASP with informative priors using JAGS. Among many analytic options, we focus on the regression analysis and explain the effects of different prior specifications on regression coefficients. We also present the Shiny App designed to help users to define the prior distributions using the example in this tutorial. After the tutorial, we expect readers can understand how to incorporate prior knowledge in conducting Bayesian regression analysis to answer substantive research questions.
4. Advanced Bayesian regression
This tutorial illustrates how to interpret the more advanced output and to set different prior specifications in performing Bayesian regression analyses in JASP. We explain various options in the control panel and introduce such concepts as Bayesian model averaging, posterior model probability, prior model probability, inclusion Bayes factor, and posterior exclusion probability. After the tutorial, we expect readers can deeply comprehend the Bayesian regression and perform it to answer substantive research questions.
5. WAMBS Checklist in JASP (using JAGS)
This tutorial illustrates how to follow the When-to-Worry-and-How-to-Avoid-the-Misuse-of-Bayesian-Statistics (WAMBS) Checklist in JASP using JAGS. Among many analytic techniques, we focus on the regression analysis and explain the 10 points for the thorough application of Bayesian analysis. After the tutorial, we expect readers can refer to the WAMBS Checklist to sensibly apply the Bayesian statistics to answer substantive research questions.