この節の作者: 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)