I'm doing a PROCESS Model 4 mediation analysis, where:

  • X = Persuasion Knowledge (continuous)

  • M1 = Brand Attitude (continuous)

  • M2 = Brand Awareness, which consists of aided brand recall (dichotomous) and unaided brand recall (dichotomous)

I tried putting it into PROCESS with three mediating variables, but I get the error that the mediators are dichotomous.

What can I do? I cannot find any literature that merges aided recall and unaided recall in one variable... should I just do that? Or is there a way to use 3 mediators in PROCESS from which two are dichotomous variables?

  • 1
    $\begingroup$ You mention you have three mediating variables... Where is the third? It seems like brand awareness is one variable with two levels (aided or unaided). Also, are you running these mediators in parallel or in serial? $\endgroup$
    – Mark White
    Jun 10, 2017 at 5:12
  • 1
    $\begingroup$ The mediation framework underlying PROCESS does not support dichotomous mediators. You need to use a more general framework to mediation to study this. Techniques in causal mediation have been developed to solve this problem. $\endgroup$
    – Noah
    Aug 8, 2018 at 15:00

1 Answer 1


I'm not sure whether there is a way to do that in process, but what you could do is manually go over the steps that process uses, i.e. to conduct a series of multiple regressions,

  1. X predicting Y (path c)
  2. X predicting M1 (path a)
  3. X predicting M2 (the second path a), and
  4. X, M1, and M2 all predicting Y (paths c', and b).

If you have paths a and b significant for a given mediator, I suppose you could say that there was an indirect effect of X on Y through that mediator.

I am not sure how to get confidence intervals from this procedure though. Also, I am not sure whether this procedure is something a hardcore statistician would object.

However it is a way for you to see whether it makes sense to talk about mediation or not. Hope this helps


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