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Bayesian Linear Regression in JASP

Hello everyone,

For a study I would like to do a hierarchical linear regression, in which in the first step I enter my control variables (e.g., age, gender, etc) and in the second step the remaining three variables of interest. I have done this only in SPSS previously, so I was wondering how to approach this from Bayes perspective using JASP? Should I add my control variables to the null model and then report which model (of the remaining 3 variables) explained the data best, or add nothing to the null model and just say which model explains the data best (for example, the model including the control variables and one of the variables of interest)?

Thank you very much in advance,



  • Hi Mila,

    What I would do is first add your control variables to the null model, and then compare this null model to the model with the other variables added. This can be done in different ways (compare to the model with all variables of interest added, or any subset) depending on your substantive question and the underlying theory. But the crucial "hierarchical" step is to add the control variables to the null model, so that all models include the control variables.



  • Hi E.J.,

    Thank you very much for the response!



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