# Load the tidyverse package for data manipulation and visualization
library(tidyverse)
# Import the exercise dataset into an object named "df"
df <- read_csv("Exercise_dataset.csv")
# Preview the data
glimpse(df)## Rows: 415
## Columns: 26
## $ ResponseId <chr> "ID_1", "ID_2", "ID_3", "ID_4", "ID_5", "ID_6", "ID_7", "…
## $ condition <chr> "CA_mock", "pic_enhanced", "text_single_control", "CA_moc…
## $ fear_1 <dbl> 2.00, 3.25, 1.00, 3.00, 4.00, 3.25, 3.50, 2.75, 4.00, 1.0…
## $ fear_2 <dbl> 2.00, 4.00, 1.00, 3.00, 4.00, 3.00, 3.50, 3.25, 5.00, 1.0…
## $ fear_3 <dbl> 2.00, 3.50, 1.00, 3.00, 4.00, 3.00, 3.50, 2.50, 4.00, 1.0…
## $ int_1 <dbl> 2, 3, 4, 2, 2, 3, 3, 2, 4, 4, 2, 2, 4, 4, 1, 3, 4, 2, 2, …
## $ int_2 <dbl> 3, 1, 3, 2, 3, 3, 3, 2, 4, 4, 2, 2, 3, 4, 1, 3, 4, 2, 2, …
## $ int_3 <dbl> 3, 2, 4, 2, 4, 3, 4, 2, 4, 4, 3, 3, 4, 4, 1, 3, 4, 2, 3, …
## $ peer_inter_1 <dbl> 2, 3, 3, 2, 3, 3, 4, 3, 4, 1, 3, 3, 1, 1, 3, 3, 4, 2, 2, …
## $ peer_inter_2 <dbl> 2, 3, 3, 2, 3, 3, 4, 2, 4, 1, 3, 3, 3, 1, 1, 3, 1, 2, 3, …
## $ peer_inter_3 <dbl> 3, 3, 3, 2, 4, 3, 4, 3, 4, 1, 4, 3, 1, 1, 1, 2, 1, 2, 2, …
## $ age <chr> "21-26", "21-26", "21-26", "21-26", "21-26", "18-20", "21…
## $ race <chr> "White", "White", "White", "Other", "White", "Other", "Wh…
## $ ethnic <chr> "Non-Hispanic", "Non-Hispanic", "Non-Hispanic", "Non-Hisp…
## $ support_1 <dbl> 1, 3, 5, 3, 5, 3, 5, 3, 5, 1, 4, 5, 4, 4, 4, 3, 1, 3, 4, …
## $ support_2 <dbl> 1, 4, 5, 3, 5, 3, 5, 2, 5, 1, 3, 4, 1, 4, 4, 5, 3, 3, 3, …
## $ support_3 <dbl> 4, 4, 5, 3, 4, 3, 5, 3, 5, 1, 5, 3, 1, 3, 4, 3, 5, 3, 4, …
## $ support_4 <dbl> 4, 2, 4, 3, 3, 3, 5, 3, 5, 1, 5, 3, 1, 4, 4, 4, 3, 3, 4, …
## $ support_5 <dbl> 1, 3, 4, 3, 4, 3, 5, 2, 5, 1, 5, 3, 4, 4, 4, 4, 2, 3, 3, …
## $ support_6 <dbl> 5, 3, 4, 3, 3, 3, 5, 2, 5, 4, 4, 3, 2, 4, 4, 4, 5, 3, 4, …
## $ support_7 <dbl> 3, 4, 5, 3, 3, 3, 4, 2, 5, 2, 4, 4, 3, 4, 3, 4, 3, 3, 4, …
## $ support_8 <dbl> 3, 3, 4, 3, 4, 3, 4, 4, 5, 1, 4, 3, 1, 3, 4, 4, 4, 3, 3, …
## $ support_9 <dbl> 3, 4, 5, 3, 3, 3, 4, 2, 5, 1, 4, 4, 4, 4, 4, 3, 3, 3, 4, …
## $ support_10 <dbl> 3, 2, 4, 3, 4, 3, 5, 4, 5, 1, 3, 3, 4, 4, 4, 4, 3, 3, 2, …
## $ support_11 <dbl> 3, 3, 4, 3, 5, 3, 5, 3, 5, 1, 4, 2, 1, 3, 3, 4, 2, 3, 2, …
## $ support_12 <dbl> 3, 4, 4, 3, 5, 3, 5, 3, 5, 1, 3, 4, 2, 4, 5, 4, 2, 3, 4, …# Source the process.R script to make the PROCESS macro available in the environment
# Update the path below to wherever process.R is saved on your machine
source("process.R")##
## ********************* 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
##
## ***********************************************************************
##
## PROCESS is now ready for use.
## Copyright 2020-2023 by Andrew F. Hayes ALL RIGHTS RESERVED
## Workshop schedule at http://haskayne.ucalgary.ca/CCRAM
## # --- Compute scale variables ---
# Compute fear_mean as the row mean of fear1, fear2, and fear3
# rowMeans() calculates the average across the three fear items for each participant
df <- df %>%
mutate(
fear_mean = rowMeans(select(., fear_1, fear_2, fear_3), na.rm = TRUE),
# Compute intention_mean as the row mean of int1, int2, and int3
intention_mean = rowMeans(select(., int_1, int_2, int_3), na.rm = TRUE)
)
# --- Filter to relevant conditions ---
# Keep only participants in the "pic_enhanced" and "text_single_control" conditions
# These are the two groups we want to compare in the mediation analysis
df_subset <- df %>%
filter(condition %in% c("pic_enhanced", "text_single_control"))
# --- Create numeric condition variable ---
# PROCESS requires numeric predictors, so recode condition as a dummy variable:
# pic_enhanced (graphic warning) = 1
# text_single_control (text-only) = 0
df_subset <- df_subset %>%
mutate(
cond_num = if_else(condition == "pic_enhanced", 1, 0)
)
# --- Drop missing values ---
# PROCESS requires complete data (no NAs) across the key analysis variables
df_subset <- df_subset %>%
drop_na(cond_num, fear_mean, intention_mean)
# Check the resulting subset
cat("Rows after filtering and dropping NAs:", nrow(df_subset), "\n")## Rows after filtering and dropping NAs: 208table(df_subset$condition)##
## pic_enhanced text_single_control
## 102 106Research Question: Does fear
(fear_mean) mediate the relationship between the graphic
warning label condition (cond_num) and cannabis use
intention (intention_mean)?
# Run the simple mediation model using the PROCESS macro (Model 4)
# Arguments:
# data = df_subset (our filtered, complete dataset)
# y = "intention_mean" (outcome / dependent variable)
# x = "cond_num" (predictor / independent variable)
# m = "fear_mean" (mediator)
# model = 4 (simple mediation)
# boot = 5000 (number of bootstrap samples for indirect effect CI)
# seed = 12345 (set seed for reproducibility)
process(
data = df_subset,
y = "intention_mean",
x = "cond_num",
m = "fear_mean",
model = 4,
boot = 5000,
seed = 12345
)##
## ********************* 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 : intention_mean
## X : cond_num
## M : fear_mean
##
## Sample size: 208
##
## Custom seed: 12345
##
##
## ***********************************************************************
## Outcome Variable: fear_mean
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.0572 0.0033 0.8791 0.6773 1.0000 206.0000 0.4115
##
## Model:
## coeff se t p LLCI ULCI
## constant 2.4167 0.0911 26.5374 0.0000 2.2371 2.5962
## cond_num 0.1070 0.1300 0.8230 0.4115 -0.1494 0.3634
##
## ***********************************************************************
## Outcome Variable: intention_mean
##
## Model Summary:
## R R-sq MSE F df1 df2 p
## 0.3450 0.1190 0.6661 13.8472 2.0000 205.0000 0.0000
##
## Model:
## coeff se t p LLCI ULCI
## constant 3.5250 0.1666 21.1541 0.0000 3.1965 3.8536
## cond_num -0.0278 0.1134 -0.2452 0.8065 -0.2514 0.1958
## fear_mean -0.3175 0.0607 -5.2342 0.0000 -0.4370 -0.1979
##
## ***********************************************************************
## Bootstrapping progress:
## | | | 0% | | | 1% | |> | 1% | |> | 2% | |>> | 2% | |>> | 3% | |>> | 4% | |>>> | 4% | |>>> | 5% | |>>> | 6% | |>>>> | 6% | |>>>> | 7% | |>>>>> | 7% | |>>>>> | 8% | |>>>>> | 9% | |>>>>>> | 9% | |>>>>>> | 10% | |>>>>>>> | 10% | |>>>>>>> | 11% | |>>>>>>> | 12% | |>>>>>>>> | 12% | |>>>>>>>> | 13% | |>>>>>>>> | 14% | |>>>>>>>>> | 14% | |>>>>>>>>> | 15% | |>>>>>>>>>> | 15% | |>>>>>>>>>> | 16% | |>>>>>>>>>> | 17% | |>>>>>>>>>>> | 17% | |>>>>>>>>>>> | 18% | |>>>>>>>>>>> | 19% | |>>>>>>>>>>>> | 19% | |>>>>>>>>>>>> | 20% | |>>>>>>>>>>>>> | 20% | |>>>>>>>>>>>>> | 21% | |>>>>>>>>>>>>> | 22% | |>>>>>>>>>>>>>> | 22% | |>>>>>>>>>>>>>> | 23% | |>>>>>>>>>>>>>>> | 23% | |>>>>>>>>>>>>>>> | 24% | |>>>>>>>>>>>>>>> | 25% | |>>>>>>>>>>>>>>>> | 25% | |>>>>>>>>>>>>>>>> | 26% | |>>>>>>>>>>>>>>>> | 27% | |>>>>>>>>>>>>>>>>> | 27% | |>>>>>>>>>>>>>>>>> | 28% | |>>>>>>>>>>>>>>>>>> | 28% | |>>>>>>>>>>>>>>>>>> | 29% | |>>>>>>>>>>>>>>>>>> | 30% | |>>>>>>>>>>>>>>>>>>> | 30% | |>>>>>>>>>>>>>>>>>>> | 31% | |>>>>>>>>>>>>>>>>>>>> | 31% | |>>>>>>>>>>>>>>>>>>>> | 32% | |>>>>>>>>>>>>>>>>>>>> | 33% | |>>>>>>>>>>>>>>>>>>>>> | 33% | |>>>>>>>>>>>>>>>>>>>>> | 34% | |>>>>>>>>>>>>>>>>>>>>> | 35% | |>>>>>>>>>>>>>>>>>>>>>> | 35% | |>>>>>>>>>>>>>>>>>>>>>> | 36% | |>>>>>>>>>>>>>>>>>>>>>>> | 36% | |>>>>>>>>>>>>>>>>>>>>>>> | 37% | |>>>>>>>>>>>>>>>>>>>>>>> | 38% | |>>>>>>>>>>>>>>>>>>>>>>>> | 38% | |>>>>>>>>>>>>>>>>>>>>>>>> | 39% | |>>>>>>>>>>>>>>>>>>>>>>>> | 40% | |>>>>>>>>>>>>>>>>>>>>>>>>> | 40% | |>>>>>>>>>>>>>>>>>>>>>>>>> | 41% | |>>>>>>>>>>>>>>>>>>>>>>>>>> | 41% | |>>>>>>>>>>>>>>>>>>>>>>>>>> | 42% | |>>>>>>>>>>>>>>>>>>>>>>>>>> | 43% | |>>>>>>>>>>>>>>>>>>>>>>>>>>> | 43% | |>>>>>>>>>>>>>>>>>>>>>>>>>>> | 44% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 44% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 45% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 46% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 46% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 47% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 48% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 48% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 49% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 49% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 50% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 51% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 51% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 52% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 52% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 53% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 54% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 54% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 55% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 56% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 56% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 57% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 57% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 58% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 59% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 59% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 60% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 60% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 61% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 62% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 62% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 63% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 64% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 64% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 65% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 65% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 66% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 67% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 67% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 68% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 69% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 69% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 70% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 70% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 71% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 72% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 72% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 73% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 73% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 74% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 75% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 75% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 76% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 77% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 77% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 78% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 78% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 79% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 80% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 80% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 81% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 81% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 82% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 83% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 83% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 84% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 85% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 85% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 86% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 86% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 87% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 88% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 88% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 89% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 90% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 90% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 91% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 91% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 92% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 93% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 93% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 94% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 94% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 95% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 96% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 96% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 97% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 98% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 98% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> | 99% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>| 99% | |>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>| 100%
##
## **************** DIRECT AND INDIRECT EFFECTS OF X ON Y ****************
##
## Direct effect of X on Y:
## effect se t p LLCI ULCI
## -0.0278 0.1134 -0.2452 0.8065 -0.2514 0.1958
##
## Indirect effect(s) of X on Y:
## Effect BootSE BootLLCI BootULCI
## fear_mean -0.0340 0.0425 -0.1201 0.0473
##
## ******************** ANALYSIS NOTES AND ERRORS ************************
##
## Level of confidence for all confidence intervals in output: 95
##
## Number of bootstraps for percentile bootstrap confidence intervals: 5000# The 'a' path tests whether the graphic warning label (vs. text control) increases fear.
# Look at the coefficient for cond_num in the model where fear_mean is the outcome.
#
# Interpretation example (update with your actual output values):
# - If the coefficient is positive and significant (p < .05), the graphic warning label
# produced significantly higher fear compared to the text-only control.
# - If p > .05, there is no significant difference in fear between conditions.
cat("**Path a:** The 'a' path coefficient represents the effect of the graphic warning
label condition (1 = pic_enhanced, 0 = text_single_control) on fear.
A significant positive coefficient would indicate that participants exposed to
the graphic warning reported higher fear than those in the text-only control.")Path a: The ‘a’ path coefficient represents the effect of the graphic warning label condition (1 = pic_enhanced, 0 = text_single_control) on fear. A significant positive coefficient would indicate that participants exposed to the graphic warning reported higher fear than those in the text-only control.
# The 'b' path tests whether fear predicts intention to use cannabis,
# after accounting for the effect of condition.
#
# Interpretation:
# - A significant negative coefficient would indicate that greater fear is associated
# with lower intention to use cannabis (i.e., fear deters use).
# - If not significant, fear does not independently predict intention once
# condition is controlled.
cat("**Path b:** The 'b' path coefficient represents the effect of fear on cannabis
use intention, controlling for condition. A significant negative coefficient would
indicate that higher fear is associated with lower intention to use cannabis.")Path b: The ‘b’ path coefficient represents the effect of fear on cannabis use intention, controlling for condition. A significant negative coefficient would indicate that higher fear is associated with lower intention to use cannabis.
# The direct effect (c') tests whether condition still predicts intention
# *after* accounting for the mediating role of fear.
#
# Interpretation:
# - If c' is not significant, it suggests that fear fully mediates the
# condition-intention relationship (full mediation).
# - If c' remains significant, it suggests partial mediation.
cat("**Direct Effect (c'):** The direct effect tests whether the graphic warning condition
predicts cannabis use intention after controlling for fear. If this effect is
non-significant, it suggests full mediation through fear.")Direct Effect (c’): The direct effect tests whether the graphic warning condition predicts cannabis use intention after controlling for fear. If this effect is non-significant, it suggests full mediation through fear.
# The indirect effect (ab) = a * b, tested via bootstrapped confidence intervals (CI).
# PROCESS uses bootstrapping because indirect effects are not normally distributed.
#
# Decision rule:
# - If the 95% bootstrap CI does NOT include zero, the indirect effect is significant,
# providing evidence of mediation.
# - If the CI includes zero, there is no significant indirect effect.
cat("**Indirect Effect (ab):** The indirect effect represents the portion of the
condition-intention relationship that operates through fear (a × b).
If the 95% bootstrapped confidence interval excludes zero, this provides
evidence that fear significantly mediates the relationship.")Indirect Effect (ab): The indirect effect represents the portion of the condition-intention relationship that operates through fear (a × b). If the 95% bootstrapped confidence interval excludes zero, this provides evidence that fear significantly mediates the relationship.
# Update the bracketed values below with your actual output numbers before submitting.
cat("
A simple mediation analysis (Model 4; Hayes, 2022) was conducted to test whether
fear mediated the effect of warning label condition on cannabis use intention.
Condition was dummy coded such that the graphic warning label (pic_enhanced) = 1
and the text-only control (text_single_control) = 0.
The results indicated that the graphic warning label condition significantly
[or did not significantly] predict fear (Path a: b = [value], SE = [value],
p = [value]), and fear significantly [or did not significantly] predict cannabis
use intention after controlling for condition (Path b: b = [value], SE = [value],
p = [value]). The direct effect of condition on intention was [significant /
non-significant] (c' = [value], SE = [value], p = [value]).
Bootstrapping with 5,000 resamples indicated that the indirect effect of condition
on cannabis use intention through fear was [significant / non-significant]
(ab = [value], 95% CI [lower, upper]). Because the confidence interval
[did not include / included] zero, these results [support / do not support]
fear as a mediator of the relationship between warning label condition and
cannabis use intention.
Reference: Hayes, A. F. (2022). *Introduction to mediation, moderation, and
conditional process analysis* (3rd ed.). Guilford Press.
")A simple mediation analysis (Model 4; Hayes, 2022) was conducted to test whether fear mediated the effect of warning label condition on cannabis use intention. Condition was dummy coded such that the graphic warning label (pic_enhanced) = 1 and the text-only control (text_single_control) = 0.
The results indicated that the graphic warning label condition significantly [or did not significantly] predict fear (Path a: b = [value], SE = [value], p = [value]), and fear significantly [or did not significantly] predict cannabis use intention after controlling for condition (Path b: b = [value], SE = [value], p = [value]). The direct effect of condition on intention was [significant / non-significant] (c’ = [value], SE = [value], p = [value]).
Bootstrapping with 5,000 resamples indicated that the indirect effect of condition on cannabis use intention through fear was [significant / non-significant] (ab = [value], 95% CI [lower, upper]). Because the confidence interval [did not include / included] zero, these results [support / do not support] fear as a mediator of the relationship between warning label condition and cannabis use intention.
Reference: Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis (3rd ed.). Guilford Press.