Section author: Sebastian Jentschke

Use the R-version of the PROCESS-macro from within jamovi

In preparation: You need to install Rj and to download the most recent version of the PROCESS-macro. Open the ZIP-file that you downloaded, go into the folder PROCESS v... for R and extract the process.R-file into your Documents-directory.

Open a data file that you want to use for your analyses. Afterwards, open Rj using the R-symbol in the Analyses-icon-bar (Rj is a jamovi module; if you have not installed it yet, you may check Install modules in jamovi).

Now you are ready to write R-code inside jamovi. Run the following code in the Rj text box for commands. You may just copy-and-paste the following code.

• on Windows

  source(file.path(Sys.getenv('HOMEDRIVE'), Sys.getenv('HOMEPATH'), 'Documents', 'process.R'))
  

• on Mac and Linux

  source(file.path(Sys.getenv('HOME'), 'Documents', 'process.R'))
  

Run this code (source…) by pressing the green triangle. Please be patient, it might take a moment (up to a minute, depending on how fast your computer is). You should see an output like this

******************** PROCESS for R Version 4.2 beta ****************

           Written by Andrew F. Hayes, Ph.D.  www.afhayes.com
   Documentation available in Hayes (2022). www.guilford.com/p/hayes3

***********************************************************************

PROCESS is now ready for use.
Copyright 2022 by Andrew F. Hayes ALL RIGHTS RESERVED
Workshop schedule at http://haskayne.ucalgary.ca/CCRAM

Afterwards, the PROCESS-macro is initialized and you can comment the line out (by putting a # at the start of the line) → # source(…

Now you are set to run analyses. Please note that the PROCESS-macro for R uses a different random number generator than SPSS and SAS[1] and that therefore the bootstrapping confidence intervals for the Indirect effect(s) of X on Y are different from what the output shown in the book. Furthermore, does the current version of the PROCESS-macro for R accept data only in numeric format.[2] Thus, factors must be converted to numeric form (e.g., 0 and 1) prior to their use in a PROCESS command. This can be done using the following command in Rj (just copy-and-paste it).

for (C in names(data)[sapply(data, is.factor)]) {
    data[[C]] = as.numeric(data[[C]]) - min(as.numeric(data[[C]]))
}

Once this is done, you may just write (or copy-and-paste if you own the e-book) the commands that are shown in the book. Please note that you have to change the name of the data set: in this example, taken from p. 188 of Hayes (2022), the dataset pmi is required (to download the data sets). Thecommand in the book has to be adjusted by changing data = pmi into data = data (data refers to the currently opened dataset in jamovi).

process(data = data, y = "reaction", x = "cond", m = c("import", "pmi"), total = 1, contrast = 1, model = 6,seed = 31216)

Please remember that you have to run the source… command again whenever you open a new dataset / a new jamovi session. If you want to run several analyses with the same dataset / within the same jamovi session, this is not required.

[1]

“The default random number generator in R is different than the default random number generator in SPSS and SAS. Thus, bootstrap confidence intervals generated by R will be different than those produced by SPSS and SAS even when the same seed is used when estimating the same model using the same data.” (Hayes, 2022, p. 613)

[2]

“PROCESS for R accepts data only in numeric format. Thus, for example, if a variable named sex were coded M and F in the data, these alphabetic codes must be converted to numeric form (e.g., 0 and 1) prior to their use in a PROCESS command.” (Hayes, 2022, p. 612)