JASP: how to get started

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)

~under construction -> expected in 2-3 weeks

 

4. Advanced Bayesian regression in JASP

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. The WAMBS checklist for JASP

~under construction -> expected in 1-2 weeks