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Conducting multiple linear regression via sequential analysis

edited July 2016 in JASP & BayesFactor

Hi all,

Before I begin, I would like to state that my statistical knowledge is pretty limited, so I apologize in advance if my queries sound elementary.

The goals of my current research project require me to 'statistically control' for covariates (e.g., fluid intelligence) so that I can evaluate the unique contributions of the variables of interest to the model. Traditionally, in SPSS, I would do this by first adding the covariates to Step 1 and subsequently including the variables of interest to Step 2 in a multiple linear regression. However, I am trying to see if I can do this using a Bayesian approach in JASP.

In JASP, I noticed that when I perform a Bayesian linear regression, there is a drop-down tab entitled 'Model' where I can select certain variables as 'nuisance' - does this mean I'm designating them as covariates? If true, does this achieve the same goal as 'statistically controlling' for covariates akin to a sequential analysis in multiple linear regression?

Any assistance on this matter would be very much appreciated. Thank you!


  • Hi ESV,

    Yes, when you design them as nuisance they get added as covariates. This is indeed similar to a hierarchical analyses where you add batches of covariates in a particular order.


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