Step 1. Open JASP and open the .csv file holding the data (regression.csv) that’s included in this folder. Do so by clicking on File -> Open
Step 2. JASP is a relatively new software package, and the options for Bayesian regression analysis are still limited. Therefore, we will first do a frequentist regression analysis to get frequentist parameter estimates, after which we will execute a Bayesian Linear Regression to obtain Bayes Factors. The image below shows you where to find the regular linear regression.
Step 3. In the window that opens, you can easily move DVs and IVs to their respective boxes (much like SPSS). If you want two IVs in one regression block, you have to select both of them using CRTL-click (or CMD-click for Mac users), and then move them to the box on the left simultaneously.
Step 4. As you can see, the model output is created as soon as you add some variable to your regression model. In the window on the right you will get output similar to SPSS output tables. Clicking on the Statistics button in the window on the left will allow you to pick some more output options (e.g. confidence intervals and R-squared change if you are comparing several models).
Step 5. To do a Bayesian Linear Regression, click on the Regression dropdown menu and choose Bayesian Linear Regression. The process is the same as for regular linear regression analysis. This time, a table with Bayes Factors for several parts of the model and the complete model are created. Radio buttons in the window on the left allow you to pick a different type of Bayes Factor.
Note: As JASP get’s developed further, more options will become available for the Bayesian analyses.