The following shows steps of setup Hayes Mediation PROCESS in R.

- Click here to visit the offcial Hayes PROCESS R package webpage. Then, click “Download PROCESS v 4.3.”
- Open the downloaded zip file.

You should be able to find the folder of “PROCESS v4.3 for R.” Within the folder, you should be able to find the “process.R ” file. - Open “process.R”

Assuming that you can R Studio in your computer, you can double-click the process.R . R Studio will open the process.R as an independent R file tap. - Run “process.R”

The process.R code is very long. In order to select all its R code,**right click the computer mouse**and then click “**Select all**.” Then, hit**Run**in RStudio.

Then, the process() function is in the R environment (You will see the following output). That means that we can use the function.

After setting up the PROCESS in R, we can use Model 4 as a simple example. In the following R code, we first download the data from GitHub. Then, run it using the process().

```
# Read data from GitHub
data_mediation <- read.csv("https://raw.githubusercontent.com/tidydatayt/mediation_analysis/main/mediation_hypothetical_data.csv")
# run model 4 using PROCESS in R as an example
process(data = data_mediation, y = "Y", x = "X", m ="M", model = 4)
```

After running the R code above, you should see the following output, which means that you setup PROCESS in R correctly. You can change variable names and the model number to do your own mediation analysis.

********************* PROCESS for R Version 4.3.1 ********************* Written by Andrew F. Hayes, Ph.D. www.afhayes.com Documentation available in Hayes (2022). www.guilford.com/p/hayes3 *********************************************************************** Model : 4 Y : Y X : X M : M Sample size: 100 Random seed: 611311 *********************************************************************** Outcome Variable: M Model Summary: R R-sq MSE F df1 0.2891 0.0836 1.2885 8.9359 1.0000 df2 p 98.0000 0.0035 Model: coeff se t p constant 0.6468 0.9247 0.6996 0.4859 X 0.0333 0.0111 2.9893 0.0035 LLCI ULCI constant -1.1881 2.4818 X 0.0112 0.0553 *********************************************************************** Outcome Variable: Y Model Summary: R R-sq MSE F df1 0.9566 0.9150 1.1382 522.3480 2.0000 df2 p 97.0000 0.0000 Model: coeff se t p constant 0.0327 0.8712 0.0376 0.9701 X 0.0023 0.0109 0.2093 0.8346 M 2.9318 0.0949 30.8807 0.0000 LLCI ULCI constant -1.6964 1.7618 X -0.0194 0.0240 M 2.7433 3.1202 *********************************************************************** Bootstrapping progress: |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>| 100% **************** DIRECT AND INDIRECT EFFECTS OF X ON Y **************** Direct effect of X on Y: effect se t p LLCI ULCI 0.0023 0.0109 0.2093 0.8346 -0.0194 0.0240 Indirect effect(s) of X on Y: Effect BootSE BootLLCI BootULCI M 0.0975 0.0304 0.0368 0.1562 ******************** ANALYSIS NOTES AND ERRORS ************************ Level of confidence for all confidence intervals in output: 95 Number of bootstraps for percentile bootstrap confidence intervals: 5000