MindFort Analysis Notebook
Load and preprocess certainty data
Preprocessing: For all the analysis below, we omitted data of 3 participants who have failed at least one attention check during the experiments, omitted all individual lesson data where the participants’ initial certainty score was below 10 points on the 15-point scale and removed any outliers (1.5 IQR).
library(readr)
library(dplyr)
library(stringr)
data <- read_csv("certainty.csv", show_col_types = FALSE) %>%
filter(exclude == 0) %>%
filter(participant_id > -1)
# Get all unique suffixes for lessons and conditions
likert_pre_cols <- grep("^likert_pre_", names(data), value = TRUE)
suffixes <- str_replace(likert_pre_cols, "^likert_pre_", "")
for (suf in suffixes) {
pre_col <- paste0("likert_pre_", suf)
mid_col <- paste0("likert_mid_", suf)
post_col <- paste0("likert_post_", suf)
# Check all columns exist
if (all(c(pre_col, mid_col, post_col) %in% names(data))) {
data <- data %>%
mutate(
across(
all_of(c(pre_col, mid_col, post_col)),
~ if_else(.data[[pre_col]] < 10, NA_real_, .)
)
)
}
}
print(data)NA
NADemographics
# Load necessary library
library(dplyr)
# Load the data from the CSV file
demographics_data <- read.csv("demographics.csv", stringsAsFactors = FALSE)
# Specify the IDs to remove
ids_to_remove <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
# Filter the data to remove participants with the specified IDs
filtered_data <- demographics_data %>%
filter(!Participant.id %in% ids_to_remove)
# Filter out rows where Status is not 'APPROVED'
accepted_data <- filtered_data %>%
filter(Status == "APPROVED")
# Assuming 'accepted_data' is the filtered dataset for participants with 'Status' == 'ACCEPTED'
# Ensure Age column is numeric
accepted_data$Age <- as.numeric(accepted_data$Age)
# Check for conversion issues and warn if any entries became NA
conversion_issues <- sum(is.na(accepted_data$Age))
if (conversion_issues > 0) {
warning(paste(conversion_issues, "non-numeric entries were converted to NA in the Age column."))
}
# Calculate mean and standard deviation, ignoring NAs
age_stats <- accepted_data %>%
summarise(
Mean_Age = mean(Age, na.rm = TRUE),
SD_Age = sd(Age, na.rm = TRUE)
)
age_summary <- paste0("Mean: ", round(age_stats$Mean_Age, 2), ", SD: ", round(age_stats$SD_Age, 2))
# Calculate gender distribution
gender_distribution <- paste(names(table(accepted_data$Sex)),
table(accepted_data$Sex),
sep = ": ",
collapse = ", ")
# Calculate ethnicity distribution
ethnicity_distribution <- paste(names(table(accepted_data$Ethnicity.simplified)),
table(accepted_data$Ethnicity.simplified),
sep = ": ",
collapse = ", ")
# List all nationalities with counts
nationality_distribution <- paste(names(table(accepted_data$Nationality)),
table(accepted_data$Nationality),
sep = ": ",
collapse = ", ")
# Calculate student status distribution
student_status_distribution <- paste(names(table(accepted_data$Student.status)),
table(accepted_data$Student.status),
sep = ": ",
collapse = ", ")
# Calculate employment status distribution
employment_status_distribution <- paste(names(table(accepted_data$Employment.status)),
table(accepted_data$Employment.status),
sep = ": ",
collapse = ", ")
# Compile results into a data frame
demographics_summary <- data.frame(
Statistic = c(
"Age (Mean, SD)",
"Gender Distribution",
"Ethnicity Distribution",
"Nationalities",
"Student Status Distribution",
"Employment Status Distribution"
),
Value = c(
age_summary,
gender_distribution,
ethnicity_distribution,
nationality_distribution,
student_status_distribution,
employment_status_distribution
),
stringsAsFactors = FALSE
)
# Print the summary table
print(demographics_summary)Timeline data
library(DBI)
library(RSQLite)
library(dplyr)
library(ggplot2)
library(lubridate)
# Connect to DB and load filtered data
con <- dbConnect(RSQLite::SQLite(), "database.db")
load_phase <- function(table, phase) {
dbReadTable(con, table) %>%
filter(user_id >= 0, user_id <= 2000) %>%
arrange(user_id, created_at) %>%
group_by(user_id) %>%
mutate(phase = phase, index = row_number()) %>%
ungroup()
}
# Load each phase
pre <- load_phase("likert_pre", "pre")
mid <- load_phase("likert_mid", "mid")
post <- load_phase("likert_post", "post")
# Combine all
all_data <- bind_rows(pre, mid, post) %>%
mutate(created_at = ymd_hms(created_at))
# Create interleaved step label: pre_1, mid_1, post_1, ..., post_4
all_data <- all_data %>%
mutate(step = paste0(phase, "_", index))
# Normalize time per user relative to their first submission
all_data <- all_data %>%
group_by(user_id) %>%
arrange(created_at) %>%
mutate(rel_time = as.numeric(difftime(created_at, min(created_at), units = "mins"))) %>%
ungroup()
# Set ordered factor for correct x-axis sequencing
# Interleaved step order: pre_1, mid_1, post_1, ..., post_4
step_levels <- as.vector(t(outer(1:4, c("pre", "mid", "post"), function(i, p) paste0(p, "_", i))))
all_data$step <- factor(all_data$step, levels = step_levels)
# Plot progression
ggplot(all_data, aes(x = step, y = rel_time, group = user_id, color = as.factor(user_id))) +
geom_line(linewidth = 1) +
geom_point(size = 2.5) +
labs(
title = "User Progression Across All Steps (Relative Time)",
x = "Step",
y = "Time Since First Submission (minutes)",
color = "User ID"
) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Disconnect from database
dbDisconnect(con)
Data Integrity Assertions
Assertion 1: The pre-scores are not significantly
different across conditions.
Assertion 2: The pre-scores are significantly different
across conditions. Assertion 3: Participant has exactly
4 imi scores and are in the right order (lesson 0, 1, 2, 3) based on
submission time
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
library(purrr)
# STEP 1: Extract and reshape pre scores for conditions
condition_pre <- data %>%
select(matches("likert_pre_condition\\d+")) %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = "condition",
names_pattern = "likert_pre_condition(\\d+)",
values_to = "score"
) %>%
mutate(condition = paste0("Condition ", condition))
# STEP 2: Extract and reshape pre scores for lessons
lesson_pre <- data %>%
select(matches("likert_pre_lesson\\d+")) %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = "lesson",
names_pattern = "likert_pre_lesson(\\d+)",
values_to = "score"
) %>%
mutate(lesson = paste0("Lesson ", lesson))
# STEP 3: Plot pre scores per condition
plot_conditions <- ggplot(condition_pre, aes(x = condition, y = score, fill = condition)) +
geom_boxplot(alpha = 0.6, outlier.shape = NA) +
geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = condition)) +
labs(title = "Pre Scores by Condition", x = "Condition", y = "Pre Score") +
theme_minimal() +
theme(legend.position = "none")
# STEP 4: Plot pre scores per LESSON
plot_lessons <- ggplot(lesson_pre, aes(x = lesson, y = score, fill = lesson)) +
geom_boxplot(alpha = 0.6, outlier.shape = NA) +
geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = lesson)) +
labs(title = "Pre Scores by Lesson", x = "Lesson", y = "Pre Score") +
theme_minimal() +
theme(legend.position = "none")
# Show plots
print(plot_conditions)
print(plot_lessons)
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Reshape pre condition scores
condition_pre <- data %>%
select(matches("likert_pre_condition\\d+")) %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = "condition",
names_pattern = "likert_pre_condition(\\d+)",
values_to = "score"
) %>%
mutate(condition = factor(paste0("Condition ", condition)))
# Kruskal-Wallis test
kw <- kruskal.test(score ~ condition, data = condition_pre)
cat("Kruskal-Wallis test p-value:", kw$p.value, "\n")
# If significant, run post-hoc pairwise Wilcoxon tests
if (kw$p.value < 0.05) {
cat("Significant differences found — pairwise Wilcoxon tests:\n")
pw <- pairwise.wilcox.test(condition_pre$score, condition_pre$condition, p.adjust.method = "BH")
print(pw$p.value)
} else {
cat("No significant differences between conditions for pre scores.\n")
}
library(readr)
library(dplyr)
library(tidyr)
# Reshape pre lesson scores
lesson_pre <- data %>%
select(matches("likert_pre_lesson\\d+")) %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = "lesson",
names_pattern = "likert_pre_lesson(\\d+)",
values_to = "score"
) %>%
mutate(lesson = factor(paste0("Lesson ", lesson)))
# Kruskal-Wallis test
kw <- kruskal.test(score ~ lesson, data = lesson_pre)
cat("Kruskal-Wallis test p-value (by lesson):", kw$p.value, "\n")
# Post-hoc pairwise Wilcoxon if significant
if (kw$p.value < 0.05) {
cat("Significant differences found — pairwise Wilcoxon tests:\n")
pw <- pairwise.wilcox.test(lesson_pre$score, lesson_pre$lesson, p.adjust.method = "BH")
print(pw$p.value)
} else {
cat("No significant differences between lessons for pre scores.\n")
}
Certainty
! Certainty per Condition
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
# Extract condition-related likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))
# Reshape to long format
long_condition_data <- condition_data %>%
pivot_longer(cols = everything(),
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score") %>%
mutate(phase = factor(phase, levels = c("pre", "mid", "post")),
condition = paste0("condition ", condition))
# Summarize by phase and condition
summary_data <- long_condition_data %>%
group_by(phase, condition) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
# Line plot: 4 lines (conditions), x-axis is phase
ggplot(summary_data, aes(x = phase, y = mean_score, group = condition, color = condition)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
labs(title = "Mean Likert Scores per condition Over Phases",
x = "Phase",
y = "Mean Likert Score",
color = "condition") +
theme_minimal()
condition_data <- data %>% select(matches("likert_.*_condition"))
long_condition_data <- condition_data %>%
pivot_longer(
cols = everything(),
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = factor(paste0("condition ", condition), levels = paste0("condition ", 1:4))
)
# Calculate means for labels
means <- long_condition_data %>%
group_by(condition, phase) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
ggplot(long_condition_data, aes(x = phase, y = score, fill = phase)) +
geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.8),
size = 2, alpha = 0.7) +
geom_text(data = means, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
position = position_dodge(width = 0.8), inherit.aes = FALSE,
size = 3, fontface = "bold", color = "black") +
facet_wrap(~ condition, nrow = 1) + # All conditions in one row, visually grouped by facets
scale_fill_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
scale_color_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
labs(
title = "Likert Scores by Phase and condition (overall)",
x = "Phase",
y = "Likert Score",
fill = "Phase",
color = "Phase"
) +
theme_minimal() +
theme(
strip.text = element_text(size = 12, face = "bold"), # facet label style
legend.position = "top"
)
# Prepare condition_order in long format
condition_order_long <- data %>%
mutate(id = row_number()) %>%
separate(condition_order, into = paste0("lesson_", 1:4), sep = ",", convert = TRUE) %>%
pivot_longer(
cols = starts_with("lesson_"),
names_to = "lesson",
names_pattern = "lesson_(\\d+)",
values_to = "condition_index"
) %>%
mutate(
lesson = as.integer(lesson),
condition = factor(paste0("condition ", condition_index + 1), levels = paste0("condition ", 1:4))
) %>%
select(id, lesson, condition)
# Prepare long_condition_data with participant id
long_condition_data <- data %>%
mutate(id = row_number()) %>%
select(id, matches("likert_.*_condition")) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = factor(paste0("condition ", condition), levels = paste0("condition ", 1:4))
)
# Join lesson info to condition data
long_condition_with_lesson <- long_condition_data %>%
left_join(condition_order_long, by = c("id", "condition"))
# Titles for each lesson
comparison_titles <- paste("Lesson", 1:4)
# Generate plots for each lesson and print
for (lesson_num in 1:4) {
plot_data <- long_condition_with_lesson %>% filter(lesson == lesson_num)
means_lesson <- plot_data %>%
group_by(condition, phase) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
p <- ggplot(plot_data, aes(x = phase, y = score, fill = phase)) +
geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.8),
size = 2, alpha = 0.7) +
geom_text(data = means_lesson, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
position = position_dodge(width = 0.8), inherit.aes = FALSE,
size = 3, fontface = "bold", color = "black") +
facet_wrap(~ condition, nrow = 1) +
scale_fill_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
scale_color_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
labs(
title = paste("Likert Scores by Phase and condition -", comparison_titles[lesson_num]),
x = "Phase",
y = "Likert Score",
fill = "Phase",
color = "Phase"
) +
theme_minimal() +
theme(
strip.text = element_text(size = 12, face = "bold"),
legend.position = "top"
)
print(p)
}
! Certainty per Condition - ANOVA
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Reshape condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))
long_condition_data <- condition_data %>%
pivot_longer(
cols = everything(),
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = factor(paste0("condition ", condition), levels = paste0("condition ", 1:4))
)
print("\n\nIs there a difference between conditions for pre, mid or post values?")
# Function to run Kruskal-Wallis + pairwise Wilcoxon for one phase
test_phase <- function(phase_name) {
df <- long_condition_data %>% filter(phase == phase_name)
kw <- kruskal.test(score ~ condition, data = df)
cat("\nPhase:", phase_name, "\n")
cat("Kruskal-Wallis p-value:", kw$p.value, "\n")
if (kw$p.value < 0.05) {
cat("Significant difference found, running pairwise Wilcoxon tests:\n")
pw <- pairwise.wilcox.test(df$score, df$condition, p.adjust.method = "BH")
print(pw$p.value)
}
}
# Run tests for each phase
for (ph in levels(long_condition_data$phase)) {
test_phase(ph)
}
library(readr)
library(dplyr)
library(tidyr)
library(stringr)
library(purrr)
# Step 1: Reshape likert columns to long format
condition_data <- data %>%
select(condition_order, matches("likert_.*_condition\\d+"))
long_condition_data <- condition_data %>%
pivot_longer(
cols = -condition_order,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
condition = as.integer(condition),
phase = factor(phase, levels = c("pre", "mid", "post"))
)
# Step 2: Map each condition to its lesson using condition_order
long_condition_data <- long_condition_data %>%
mutate(
lesson = map2_int(condition_order, condition, ~ {
# Split the condition_order string into numeric vector
lesson_vector <- as.integer(str_split(.x, ",", simplify = TRUE))
# Return the lesson index for that condition
lesson_vector[.y + 1] # condition is 0-based index in condition_order
})
)
# Step 3: Function to test within one lesson
test_lesson_phases <- function(lesson_id) {
cat("\n\nLesson:", lesson_id, "")
lesson_data <- long_condition_data %>% filter(lesson == lesson_id)
for (ph in levels(lesson_data$phase)) {
df <- lesson_data %>% filter(phase == ph)
kw <- kruskal.test(score ~ factor(condition), data = df)
cat("\nPhase:", ph, "\n")
cat("Kruskal-Wallis p-value:", kw$p.value, "")
if (kw$p.value < 0.05) {
cat("Significant difference found, running pairwise Wilcoxon tests:\n")
pw <- pairwise.wilcox.test(df$score, factor(df$condition), p.adjust.method = "BH")
print(pw$p.value)
}
}
}
print("\n\nIs there a difference between conditions for pre, mid or post values in certain lessons?")
# Step 4: Run the test for each lesson (0 to 3)
for (les in 0:3) {
test_lesson_phases(les)
}Certainty per Lesson
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Extract lesson-related likert columns
lesson_data <- data %>% select(matches("likert_.*_lesson"))
# Reshape to long format
long_lesson_data <- lesson_data %>%
pivot_longer(cols = everything(),
names_to = c("phase", "lesson"),
names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
values_to = "score") %>%
mutate(phase = factor(phase, levels = c("pre", "mid", "post")),
lesson = paste0("lesson ", lesson))
# Summarize by phase and lesson
summary_data <- long_lesson_data %>%
group_by(phase, lesson) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
# Line plot: 4 lines (lessons), x-axis is phase
ggplot(summary_data, aes(x = phase, y = mean_score, group = lesson, color = lesson)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
labs(title = "Mean Likert Scores per lesson Over Phases",
x = "Phase",
y = "Mean Likert Score",
color = "lesson") +
theme_minimal()
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggplot2)
library(ggsignif)
lesson_data <- data %>% select(matches("likert_.*_lesson"))
long_lesson_data <- lesson_data %>%
pivot_longer(
cols = everything(),
names_to = c("phase", "lesson"),
names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
lesson = factor(paste0("Lesson ", lesson), levels = paste0("Lesson ", 1:4))
)
# Calculate means for labels
means <- long_lesson_data %>%
group_by(lesson, phase) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
ggplot(long_lesson_data, aes(x = phase, y = score, fill = phase)) +
geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.8),
size = 2, alpha = 0.7) +
geom_text(data = means, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
position = position_dodge(width = 0.8), inherit.aes = FALSE,
size = 3, fontface = "bold", color = "black") +
geom_signif(
comparisons = list(c("post")),
map_signif_level = TRUE
) +
facet_wrap(~ lesson, nrow = 1) + # All lessons in one row, visually grouped by facets
scale_fill_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
scale_color_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
labs(
title = "Likert Scores by Phase and Lesson",
x = "Phase",
y = "Likert Score",
fill = "Phase",
color = "Phase"
) +
theme_minimal() +
theme(
strip.text = element_text(size = 12, face = "bold"), # facet label style
legend.position = "top"
)
library(ggplot2)
library(ggsignif)
# Your long_lesson_data is already fine
# Define lesson comparisons (pairwise between all 4 lessons)
lesson_comparisons <- combn(levels(long_lesson_data$lesson), 2, simplify = FALSE)
# Plot: X = lesson, facet by phase
ggplot(long_lesson_data, aes(x = lesson, y = score, fill = lesson)) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) +
geom_jitter(aes(color = lesson), position = position_jitter(width = 0.15), size = 2, alpha = 0.7) +
geom_signif(
comparisons = lesson_comparisons,
step_increase = 0.2,
map_signif_level=c("***"=0.001,"**"=0.01, "*"=0.05, " "=2),
test = "wilcox.test"
) +
facet_wrap(~ phase, nrow = 1) +
scale_fill_brewer(palette = "Set2") +
scale_color_brewer(palette = "Set2") +
labs(
title = "Likert Scores Across Lessons (by Phase)",
x = "Lesson",
y = "Likert Score",
fill = "Lesson",
color = "Lesson"
) +
theme_minimal() +
theme(
strip.text = element_text(size = 12, face = "bold"),
legend.position = "top"
)
Certainty per Lesson - ANOVA
Finding Kruskal-Wallis shows there is significant difference between lessons in their pre scores (p < 0.05) and post scores (p < 0.05). Pairwise Wilcoxon test shows Lesson 4 is significantly different from Lessons 1 and 2 in pre scores (p < 0.05) and Lesson 1 in post scores (p < 0.05).
library(readr)
library(dplyr)
library(tidyr)
# Select lesson-related Likert columns
lesson_data <- data %>% select(matches("likert_.*_lesson"))
# Reshape to long format: id, phase, lesson, score
long_lesson_data <- lesson_data %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "lesson"),
names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
lesson = paste0("Lesson ", lesson)
)
# Run Kruskal-Wallis test for each phase comparing lessons
kruskal_phase <- long_lesson_data %>%
group_by(phase) %>%
summarise(
kruskal_p = kruskal.test(score ~ lesson)$p.value
)
print(kruskal_phase)
# Only for phases with significant Kruskal-Wallis results
significant_phases <- kruskal_phase %>% filter(kruskal_p < 0.05) %>% pull(phase)
for (ph in significant_phases) {
cat("\n=== Phase:", ph, "===\n")
df <- long_lesson_data %>% filter(phase == ph)
# Pairwise Wilcoxon
pairwise_result <- pairwise.wilcox.test(df$score, df$lesson, p.adjust.method = "BH")
print(pairwise_result$p.value)
}
Certainty Deltas per Condition
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Extract and reshape condition-related likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))
long_condition_data <- condition_data %>%
pivot_longer(
cols = everything(),
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = paste0("condition ", condition)
)
# Compute mean scores per phase and condition
mean_scores <- long_condition_data %>%
group_by(condition, phase) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
# Join with pre scores to compute difference
pre_scores <- mean_scores %>% filter(phase == "pre") %>%
rename(pre_score = mean_score) %>%
select(condition, pre_score)
diff_data <- mean_scores %>%
left_join(pre_scores, by = "condition") %>%
mutate(diff_from_pre = mean_score - pre_score)
# Plot differences, with pre as baseline (0), mid/post as deltas
ggplot(diff_data, aes(x = phase, y = diff_from_pre, group = condition, color = condition)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
labs(
title = "Mean Likert Score Changes Relative to Pre",
x = "Phase",
y = "Score Difference from Pre",
color = "condition"
) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
theme_minimal()
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Extract and reshape condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))
# Long format with phase and condition
long_condition_data <- condition_data %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = paste0("condition ", condition)
)
# Calculate differences: mid-pre, post-pre, and post-mid
pivoted <- long_condition_data %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff_mid = mid - pre,
diff_post = post - pre,
diff_mid_post = post - mid
) %>%
pivot_longer(
cols = c(diff_mid, diff_post, diff_mid_post),
names_to = "phase",
values_to = "diff_from_pre"
) %>%
mutate(
phase = recode(phase,
diff_mid = "mid-pre",
diff_post = "post-pre",
diff_mid_post = "post-mid"),
phase = factor(phase, levels = c("mid-pre", "post-pre", "post-mid"))
)
# Calculate means for labels/points
means <- pivoted %>%
group_by(condition, phase) %>%
summarise(mean_val = mean(diff_from_pre, na.rm = TRUE), .groups = "drop")
# Plot all conditions side by side, phase as fill dodge, with mean points
ggplot(pivoted, aes(x = condition, y = diff_from_pre, fill = phase)) +
geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8), alpha = 0.7, size = 2) +
geom_point(data = means, aes(x = condition, y = mean_val, group = phase),
position = position_dodge(width = 0.8), color = "black", size = 3, shape = 18) +
scale_y_continuous(limits = c(-15, 15)) +
labs(
title = "Differences between Phases per condition",
x = "condition",
y = "Score Difference",
fill = "Phase Comparison",
color = "Phase Comparison"
) +
theme_minimal() +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray")
Certainty Deltas per Condition - ANOVA
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Extract and reshape only condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))
# Long format with phase and condition
long_condition_data <- condition_data %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = paste0("condition ", condition)
)
# Calculate differences from pre per individual and condition
delta_data <- long_condition_data %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff_mid = mid - pre,
diff_post = post - pre
) %>%
pivot_longer(
cols = c(diff_mid, diff_post),
names_to = "phase",
values_to = "diff_from_pre"
) %>%
mutate(
phase = recode(phase, diff_mid = "mid", diff_post = "post"),
phase = factor(phase, levels = c("mid", "post"))
)
# Perform Shapiro-Wilk test by condition and phase
normality_tests <- delta_data %>%
group_by(condition, phase) %>%
summarise(
p_value = shapiro.test(diff_from_pre)$p.value,
.groups = "drop"
)
print("Shapiro-Wilk Normality Test (p < 0.05 = not normal):")
print(normality_tests)
library(readr)
library(dplyr)
library(tidyr)
# Select condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))
# Reshape to long format: id, phase, condition, score
long_condition_data <- condition_data %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = paste0("condition ", condition)
)
# Run Kruskal-Wallis test for each phase comparing conditions
kruskal_phase <- long_condition_data %>%
group_by(phase) %>%
summarise(
kruskal_p = kruskal.test(score ~ condition)$p.value
)
print(kruskal_phase)
Certainty Deltas per Lesson
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Extract and reshape lesson-related likert columns
condition_data <- data %>% select(matches("likert_.*_lesson"))
long_lesson_data <- condition_data %>%
pivot_longer(
cols = everything(),
names_to = c("phase", "lesson"),
names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
lesson = paste0("condition ", lesson)
)
# Compute mean scores per phase and lesson
mean_scores <- long_lesson_data %>%
group_by(lesson, phase) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
# Join with pre scores to compute difference
pre_scores <- mean_scores %>% filter(phase == "pre") %>%
rename(pre_score = mean_score) %>%
select(lesson, pre_score)
diff_data <- mean_scores %>%
left_join(pre_scores, by = "lesson") %>%
mutate(diff_from_pre = mean_score - pre_score)
# Plot differences, with pre as baseline (0), mid/post as deltas
ggplot(diff_data, aes(x = phase, y = diff_from_pre, group = lesson, color = lesson)) +
geom_line(size = 1.2) +
geom_point(size = 3) +
labs(
title = "Mean Likert Score Changes Relative to Pre",
x = "Phase",
y = "Score Difference from Pre",
color = "Lesson"
) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
theme_minimal()
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Extract and reshape condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_lesson"))
# Long format with phase and condition
long_lesson_data <- condition_data %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "lesson"),
names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
condition = paste0("Lesson ", lesson)
)
# Calculate differences: mid-pre, post-pre, and post-mid
pivoted <- long_lesson_data %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff_mid = mid - pre,
diff_post = post - pre,
diff_mid_post = post - mid
) %>%
pivot_longer(
cols = c(diff_mid, diff_post, diff_mid_post),
names_to = "phase",
values_to = "diff_from_pre"
) %>%
mutate(
phase = recode(phase,
diff_mid = "mid-pre",
diff_post = "post-pre",
diff_mid_post = "post-mid"),
phase = factor(phase, levels = c("mid-pre", "post-pre", "post-mid"))
)
# Calculate means for labels/points
means <- pivoted %>%
group_by(condition, phase) %>%
summarise(mean_val = mean(diff_from_pre, na.rm = TRUE), .groups = "drop")
# Plot all conditions side by side, phase as fill dodge, with mean points
ggplot(pivoted, aes(x = condition, y = diff_from_pre, fill = phase)) +
geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8), alpha = 0.7, size = 2) +
geom_point(data = means, aes(x = condition, y = mean_val, group = phase),
position = position_dodge(width = 0.8), color = "black", size = 3, shape = 18) +
scale_y_continuous(limits = c(-15, 15)) +
labs(
title = "Differences between Phases per Lesson",
x = "Lesson",
y = "Score Difference",
fill = "Phase Comparison",
color = "Phase Comparison"
) +
theme_minimal() +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray")
Certainty Deltas per Lesson - ANOVA
Certainty for all Lessons and conditions
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
lesson_data <- data %>%
select(matches("likert_.*_lesson")) %>%
mutate(id = row_number()) # Add this line
long_lesson_data <- lesson_data %>%
pivot_longer(
cols = -id, # Exclude 'id' from pivoting
names_to = c("phase", "lesson"),
names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
values_to = "score"
) %>%
mutate(
phase = factor(phase, levels = c("pre", "mid", "post")),
lesson = paste0("Lesson ", lesson)
)
# Split 'condition_order' into separate rows
condition_order_long <- data %>%
mutate(id = row_number()) %>%
separate(condition_order, into = paste0("lesson_", 1:4), sep = ",", convert = TRUE) %>%
pivot_longer(
cols = starts_with("lesson_"),
names_to = "lesson",
names_pattern = "lesson_(\\d+)",
values_to = "condition_index"
) %>%
mutate(
lesson = paste0("Lesson ", as.integer(lesson)),
condition = paste0("condition ", condition_index + 1)
) %>%
select(id, lesson, condition)
# ✅ JOIN to get full combined data
combined <- long_lesson_data %>%
left_join(condition_order_long, by = c("id", "lesson")) %>%
mutate(condition_lesson = paste(condition, lesson, sep = " - "))
# Join lesson scores with their corresponding condition (via condition_order)
combined <- combined %>%
mutate(condition_lesson = paste(condition, lesson, sep = " - "))
# Step 4: Get means for label
means <- combined %>%
group_by(condition_lesson, phase) %>%
summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
# Step 5: Plot
# Define color scales
fill_colors <- c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")
text_color <- "black"
# Function to generate a plot for a specific phase
plot_by_phase <- function(phase_input) {
# Filter combined data and means for the given phase
plot_data <- combined %>% filter(phase == phase_input)
phase_means <- means %>% filter(phase == phase_input)
ggplot(plot_data, aes(x = phase, y = score, fill = phase)) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) +
geom_jitter(aes(color = phase), position = position_jitter(width = 0.15),
size = 1.5, alpha = 0.7) +
geom_text(data = phase_means, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
inherit.aes = FALSE,
size = 2.5, fontface = "bold", color = text_color) +
facet_wrap(~ condition_lesson, ncol = 4) +
scale_fill_manual(values = fill_colors) +
scale_color_manual(values = fill_colors) +
labs(
title = paste("Likert Scores for", tools::toTitleCase(phase_input), "Phase"),
x = NULL,
y = "Likert Score",
fill = "Phase",
color = "Phase"
) +
theme_minimal() +
theme(
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
strip.text = element_text(size = 10, face = "bold"),
legend.position = "none"
)
}
# Now generate plots
plot_pre <- plot_by_phase("pre")
plot_mid <- plot_by_phase("mid")
plot_post <- plot_by_phase("post")
# Print them one after another (in an R script or notebook)
print(plot_pre)
print(plot_mid)
print(plot_post)
library(ggplot2)
library(tidyr)
library(dplyr)
library(patchwork)
means_heatmap <- means %>%
separate(condition_lesson, into = c("condition", "lesson"), sep = " - ") %>%
mutate(
condition = factor(str_remove(condition, "condition "), levels = c("1", "2", "3", "4")),
lesson = factor(str_remove(lesson, "Lesson "), levels = c("1", "2", "3", "4")),
phase = factor(phase, levels = c("pre", "mid", "post"))
) %>%
complete(phase, condition, lesson)
min_score <- floor(min(means_heatmap$mean_score, na.rm = TRUE))
max_score <- ceiling(max(means_heatmap$mean_score, na.rm = TRUE))
plot_heatmap <- function(phase_name, show_x = TRUE, show_y = TRUE) {
ggplot(means_heatmap %>% filter(phase == phase_name), aes(x = lesson, y = condition, fill = mean_score)) +
geom_tile(color = "white") +
geom_label(aes(label = round(mean_score, 2)),
fill = alpha("white", 0.5), # faint white background
color = "black",
size = 2, # smaller text size
label.size = 0) + # remove label border
scale_fill_viridis_c(option = "C", direction = -1, limits = c(min_score, max_score), na.value = "grey90") +
labs(title = paste(toupper(phase_name), "Phase"),
x = ifelse(show_x, "Lesson", ""),
y = ifelse(show_y, "condition", ""),
fill = "Mean Score") +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 13, face = "bold"),
axis.title.y = element_text(size = 13, face = "bold")
)
}
# Show y-axis labels only on leftmost plot, x-axis only on bottom plots
heatmap_pre <- plot_heatmap("pre", show_x = FALSE, show_y = TRUE)
heatmap_mid <- plot_heatmap("mid", show_x = FALSE, show_y = FALSE)
heatmap_post <- plot_heatmap("post", show_x = TRUE, show_y = FALSE)
combined_heatmaps <- heatmap_pre + heatmap_mid + heatmap_post +
plot_layout(ncol = 3, guides = "collect") &
theme(legend.position = "right")
print(combined_heatmaps)
Certainty deltas for all Lessons and conditions
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
# Spread phases wide
means_wide <- means_heatmap %>%
select(condition, lesson, phase, mean_score) %>%
pivot_wider(names_from = phase, values_from = mean_score)
# Calculate deltas as later phase minus earlier phase
deltas <- means_wide %>%
mutate(
mid_pre = mid - pre, # Change after Inoculation
post_pre = post - pre, # Change after Inoculation and Strong attack
post_mid = post - mid # Change after Strong attack
) %>%
select(condition, lesson, mid_pre, post_pre, post_mid) %>%
pivot_longer(cols = c(mid_pre, post_pre, post_mid),
names_to = "comparison",
values_to = "delta")
# Named titles for the plots
comparison_titles <- c(
mid_pre = "Change after\nInoculation",
post_pre = "Change after\nInoculation and\nStrong attack",
post_mid = "Change after\nStrong attack"
)
plot_delta_heatmap <- function(comp_name) {
ggplot(deltas %>% filter(comparison == comp_name), aes(x = lesson, y = condition, fill = delta)) +
geom_tile(color = "white") +
geom_label(aes(label = round(delta, 2)),
fill = alpha("white", 0.5),
color = "black",
size = 2,
label.size = 0) +
scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
limits = c(min(deltas$delta, na.rm = TRUE), max(deltas$delta, na.rm = TRUE)),
na.value = "grey90") +
labs(
title = comparison_titles[comp_name],
x = ifelse(comp_name == "mid_pre", "Lesson", ""),
y = ifelse(comp_name == "mid_pre", "condition", ""),
fill = "Delta"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
axis.text.y = element_text(size = 12),
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.title.x = element_text(size = 13, face = "bold"),
axis.title.y = element_text(size = 13, face = "bold")
)
}
heatmap_mid_pre <- plot_delta_heatmap("mid_pre")
heatmap_post_pre <- plot_delta_heatmap("post_pre")
heatmap_post_mid <- plot_delta_heatmap("post_mid")
combined_deltas <- heatmap_mid_pre + heatmap_post_mid + heatmap_post_pre +
plot_layout(ncol = 3, guides = "collect") &
theme(legend.position = "right")
print(combined_deltas)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
library(stringr)
# Assuming you have 'combined' dataframe with participant_id, condition, lesson, phase, score
# Pivot wider to get pre, mid, post scores per participant-condition-lesson
participant_wide <- combined %>%
select(id, condition, lesson, phase, score) %>%
pivot_wider(names_from = phase, values_from = score)
# Calculate participant-level deltas (later - earlier)
participant_deltas <- participant_wide %>%
mutate(
mid_pre = mid - pre,
post_pre = post - pre,
post_mid = post - mid
) %>%
select(id, condition, lesson, mid_pre, post_pre, post_mid) %>%
pivot_longer(cols = c(mid_pre, post_pre, post_mid),
names_to = "comparison",
values_to = "delta")
# Fix factors for plotting
participant_deltas <- participant_deltas %>%
mutate(
condition = factor(str_remove(condition, "condition "), levels = c("1", "2", "3", "4")),
lesson = factor(str_remove(lesson, "Lesson "), levels = c("1", "2", "3", "4")),
comparison = factor(comparison, levels = c("mid_pre", "post_pre", "post_mid"))
)
# Named titles for the plots
comparison_titles <- c(
mid_pre = "Change after\nInoculation",
post_pre = "Change after\nInoculation and\nStrong attack",
post_mid = "Change after\nStrong attack"
)
# Plotting function for boxplots + scatter
plot_delta_boxplots <- function(comp_name) {
df <- participant_deltas %>% filter(comparison == comp_name)
ggplot(df, aes(x = lesson, y = delta)) +
geom_boxplot(outlier.shape = NA, fill = "#a6cee3", alpha = 0.5) +
geom_jitter(width = 0.2, alpha = 0.7, size = 1.5, color = "#1f78b4") +
facet_wrap(~condition, nrow = 4, ncol = 1, strip.position = "right") +
ylim(-15, 15) + # Fixed y-axis scale here
labs(
title = comparison_titles[comp_name],
x = "Lesson",
y = "Delta Score"
) +
theme_minimal() +
theme(
strip.text.y = element_text(angle = 0, size = 12, face = "bold"),
plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1)
)
}
# Generate plots
plot_mid_pre <- plot_delta_boxplots("mid_pre")
plot_post_pre <- plot_delta_boxplots("post_pre")
plot_post_mid <- plot_delta_boxplots("post_mid")
# Combine plots side by side with shared legend area (no legend here actually)
combined_boxplots <- plot_mid_pre + plot_post_pre + plot_post_mid +
plot_layout(ncol = 3, guides = "collect") &
theme(legend.position = "bottom")
# Show combined plot
print(combined_boxplots)
Resistance to Persuasion (Certainty mid-post)
! All certainty deltas
# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
select(matches("likert_(mid|post)_condition\\d+")) %>%
mutate(participant_id = row_number()) %>%
pivot_longer(
cols = -participant_id,
names_to = c("phase", "condition"),
names_pattern = "likert_(mid|post)_condition(\\d+)",
values_to = "score"
) %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff = post - mid,
condition = case_when(
condition == "1" ~ "Control",
condition == "2" ~ "Reading",
condition == "3" ~ "Writing",
condition == "4" ~ "Chatbot",
TRUE ~ condition
)
) %>%
select(participant_id, condition, diff) %>%
pivot_wider(names_from = condition, values_from = diff)
# Step 2: View the result
print(all_certainty_deltas)
# Optional: Save to CSV
write.csv(all_certainty_deltas, "participant_differences_by_condition.csv", row.names = FALSE)
!! Resistance to Persuasion per Condition
!! Resistance to Persuasion per Condition - ANOVA
Finding: We performed a Shapiro-Wilk normality test, which determined that the certainty scores are not normally distributed in either of the conditions (p < 0.01). Then, we performed Wilcoxon pairwise tests to compare the Chatbot condition to each of the three other condition. Talking to Forty the chatbot made participants significantly more resistant to a strong counter-attitudinal message than the writing task (p = .02, r =.38), the reading task (p = .02, r = .34) and control condition (p = .02, r = .33).
Resistance to Persuasion per Lesson (all conditions)
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(ggpubr)
plot_data <- data %>%
select(matches("likert_(mid|post)_lesson\\d+")) %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "lesson"),
names_pattern = "likert_(mid|post)_lesson(\\d+)",
values_to = "score"
) %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff_value = post - mid,
condition = case_when(
condition == "1" ~ "Control",
condition == "2" ~ "Reading",
condition == "3" ~ "Writing",
condition == "4" ~ "Chatbot",
TRUE ~ condition # serves as a failsafe
),
# Convert condition to a factor with specified levels
condition = factor(condition, levels = c("Control", "Reading", "Writing", "Chatbot"))
)Error in `mutate()`:
ℹ In argument: `condition = case_when(...)`.
Caused by error in `case_when()`:
! Failed to evaluate the left-hand side of formula 1.
Caused by error:
! object 'condition' not found
Run `]8;;x-r-run:rlang::last_trace()rlang::last_trace()]8;;` to see where the error occurred.Resistance to Persuasion per Lesson (all conditions) - ANOVA
library(dplyr)
library(rcompanion)
# ---- Normality check per lesson ----
normality_results <- plot_data %>%
group_by(lesson) %>%
summarise(
shapiro_p = ifelse(length(diff_value) >= 3, # Shapiro test requires at least 3 obs
shapiro.test(diff_value)$p.value,
NA_real_)
)
print(normality_results)
# ---- Outlier detection and removal ----
plot_data_with_flags <- plot_data %>%
group_by(lesson) %>%
mutate(
Q1 = quantile(diff_value, 0.25, na.rm = TRUE),
Q3 = quantile(diff_value, 0.75, na.rm = TRUE),
IQR = Q3 - Q1,
lower_bound = Q1 - 1.5 * IQR,
upper_bound = Q3 + 1.5 * IQR,
is_outlier = diff_value < lower_bound | diff_value > upper_bound
) %>%
ungroup()
print(table(plot_data_with_flags$is_outlier, plot_data_with_flags$lesson))
Exercise and\nMental Health Binge Drinking Protecting Nature Dental Hygiene
FALSE 58 51 53 49
TRUE 0 0 5 9plot_data_clean <- plot_data_with_flags %>%
filter(!is_outlier) %>%
select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound, -is_outlier)
# ---- Compute all pairwise comparisons with effect sizes ----
pairwise_comparisons <- combn(unique(plot_data_clean$lesson), 2, simplify = FALSE)
compute_pairwise_wilcox <- function(group1, group2, data) {
subset <- data %>% filter(lesson %in% c(group1, group2))
subset <- subset %>%
mutate(group_numeric = ifelse(lesson == group1, 1, 2))
test <- wilcox.test(diff_value ~ group_numeric,
data = subset,
exact = FALSE,
p.adjust.method = 'none',
alternative = "less")
print(test)
effect_size_r <- wilcoxonR(x = subset$diff_value, g = subset$group_numeric)
tibble(
group1 = group1,
group2 = group2,
p_value_uncorrected = test$p.value,
effect_size_r = effect_size_r
)
}
effect_size_results <- bind_rows(
lapply(pairwise_comparisons, function(g) {
compute_pairwise_wilcox(g[1], g[2], plot_data_clean)
})
)
Wilcoxon rank sum test with continuity correction
data: diff_value by group_numeric
W = 1444, p-value = 0.4164
alternative hypothesis: true location shift is less than 0
Wilcoxon rank sum test with continuity correction
data: diff_value by group_numeric
W = 955, p-value = 0.0002137
alternative hypothesis: true location shift is less than 0
Wilcoxon rank sum test with continuity correction
data: diff_value by group_numeric
W = 635, p-value = 1.613e-07
alternative hypothesis: true location shift is less than 0
Wilcoxon rank sum test with continuity correction
data: diff_value by group_numeric
W = 912.5, p-value = 0.001617
alternative hypothesis: true location shift is less than 0
Wilcoxon rank sum test with continuity correction
data: diff_value by group_numeric
W = 627, p-value = 3.236e-06
alternative hypothesis: true location shift is less than 0
Wilcoxon rank sum test with continuity correction
data: diff_value by group_numeric
W = 970.5, p-value = 0.006495
alternative hypothesis: true location shift is less than 0# ---- Now, adjust p-values only for pairs involving the NEW lesson (e.g., "Chatbot") ----
new_lesson <- "Chatbot"
# Filter rows with Chatbot involved
effect_size_new <- effect_size_results %>%
filter(group1 == new_lesson | group2 == new_lesson) %>%
mutate(
p_adj_bonf_subset = p.adjust(p_value_uncorrected, method = "bonferroni"),
p_adj_bh_subset = p.adjust(p_value_uncorrected, method = "BH")
)
# Print full effect size results for reference
print(effect_size_results)
cat("\nAdjusted p-values ONLY for comparisons involving", new_lesson, ":\n")
Adjusted p-values ONLY for comparisons involving Chatbot :print(effect_size_new)NAOverall Change in Certainty
Per Condition
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(ggpubr)
# Data wrangling
plot_data <- data %>%
select(matches("likert_(pre|post)_condition\\d+")) %>%
mutate(id = row_number()) %>%
pivot_longer(
cols = -id,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|post)_condition(\\d+)",
values_to = "score"
) %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff_value = post - pre,
condition = recode(condition,
"1" = "Control",
"2" = "Reading",
"3" = "Writing",
"4" = "Chatbot"
),
condition = factor(condition, levels = c("Control", "Reading", "Writing", "Chatbot"))
)
# Remove outliers (per condition, 1.5 × IQR rule)
plot_data_clean <- plot_data %>%
group_by(condition) %>%
mutate(
Q1 = quantile(diff_value, 0.25, na.rm = TRUE),
Q3 = quantile(diff_value, 0.75, na.rm = TRUE),
IQR = Q3 - Q1,
lower_bound = Q1 - 1.5 * IQR,
upper_bound = Q3 + 1.5 * IQR,
is_outlier = diff_value < lower_bound | diff_value > upper_bound
) %>%
filter(!is_outlier) %>%
ungroup() %>%
select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound, -is_outlier)
# Summary statistics (use cleaned data)
summary_stats_clean <- plot_data_clean %>%
group_by(condition) %>%
summarise(mean_val = mean(diff_value, na.rm = TRUE), .groups = "drop")
pvals <- data.frame(
group1 = c("Control", "Reading", "Writing"),
group2 = c("Chatbot", "Chatbot", "Chatbot"),
p_value = c(0.01, 0.01, 0.01), # replace with your actual p-values
xstart = c(1, 2, 3), # numeric x for groups; adjust if your factor levels differ
xend = c(4, 4, 4), # "Chatbot" assumed at position 4
y = c(5, 6.8, 8.6) # y positions for brackets, adjust based on your data range
)
# Plot using cleaned data
ggplot(plot_data_clean, aes(x = condition, y = diff_value, fill = condition)) +
geom_boxplot(alpha = 0.5, outlier.shape = NA) +
geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = condition)) +
geom_point(data = summary_stats_clean, aes(x = condition, y = mean_val),
color = "black", size = 3, shape = 18) +
geom_text(data = summary_stats_clean, aes(x = condition, y = mean_val, label = round(mean_val, 2)),
vjust = -1, color = "black", size = 4) +
geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
scale_y_continuous(breaks = pretty_breaks(n = 10)) +
labs(
title = "Change in certainty after strong attack",
x = "Condition",
y = "Certainty score change",
fill = "Condition",
color = "Condition"
) +
theme_minimal() +
# Add brackets as lines
geom_segment(data = pvals, aes(x = xstart, xend = xend, y = y, yend = y),
inherit.aes = FALSE) +
geom_segment(data = pvals, aes(x = xstart, xend = xstart, y = y, yend = y - 0.3),
inherit.aes = FALSE) +
geom_segment(data = pvals, aes(x = xend, xend = xend, y = y, yend = y - 0.3),
inherit.aes = FALSE) +
# Add p-value labels, show "p < 0.05" if below threshold, else blank
geom_text(
data = pvals,
aes(x = (xstart + xend) / 2, y = y + 0.5,
label = ifelse(p_value < 0.05, "*", "")),
inherit.aes = FALSE,
size = 3
)
IMI Data
Overall Mindfort IMI
library(readr)
library(dplyr)
library(tidyr)
library(likert)
# Load and clean
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Select all lesson columns (lesson1 to lesson4)
lesson_cols <- imi_clean %>%
select(participant_id, matches("^imi\\d+_lesson[1-4]$"))
# Convert from wide to long format
long_data <- lesson_cols %>%
pivot_longer(
cols = -participant_id,
names_to = c("question", "lesson"),
names_pattern = "imi(\\d+)_lesson(\\d)",
values_to = "response"
) %>%
mutate(
question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
response = factor(as.numeric(response),
levels = 1:7,
labels = c("Extremely disagree", "Disagree", "Slightly disagree",
"Neutral", "Slightly agree", "Agree", "Extremely agree"),
ordered = TRUE)
)
# Convert to wide format for likert: rows = responses, columns = Q1–Q25
likert_ready <- long_data %>%
arrange(participant_id, lesson) %>%
group_by(participant_id, lesson) %>%
mutate(response_id = cur_group_id()) %>%
ungroup() %>%
select(response_id, question, response) %>%
pivot_wider(names_from = question, values_from = response) %>%
select(paste0("Q", 1:25)) # Ensure correct order
# Convert to data.frame for likert package
likert_ready <- as.data.frame(likert_ready)
# Create and plot
likert_obj <- likert(likert_ready)
plot(likert_obj, group.order = paste0("Q", 1:25))
library(readr)
library(dplyr)
library(tidyr)
# Load and clean
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Select all lesson columns (lesson1 to lesson4)
lesson_cols <- imi_clean %>%
select(participant_id, matches("^imi\\d+_lesson[1-4]$"))
# Convert from wide to long format
long_data <- lesson_cols %>%
pivot_longer(
cols = -participant_id,
names_to = c("question", "lesson"),
names_pattern = "imi(\\d+)_lesson(\\d)",
values_to = "response"
) %>%
mutate(
# Q1 is imi0, so Qn = imin-1, now label nicely again for reporting
question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE)
)
# Reverse-score the right questions
reverse_questions <- c("Q8", "Q12", "Q14", "Q18", "Q20", "Q24")
long_data <- long_data %>%
mutate(
response_num = as.numeric(response),
response_num = ifelse(question %in% reverse_questions, 8 - response_num, response_num)
)
# Calculate and print means for each question
question_means <- long_data %>%
group_by(question) %>%
summarise(mean = mean(response_num, na.rm = TRUE)) %>%
arrange(question)
print(question_means)
IMI All Subscales per Condition
# Load libraries
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
# Define subscale map
subscale_map <- tribble(
~qn, ~subscale, ~reverse,
1, 1, FALSE,
2, 1, FALSE,
3, 1, TRUE,
4, 1, TRUE,
5, 1, FALSE,
6, 1, FALSE,
7, 1, FALSE,
8, 2, FALSE,
9, 2, FALSE,
10, 2, FALSE,
11, 2, FALSE,
12, 2, FALSE,
13, 2, TRUE,
14, 3, FALSE,
15, 3, TRUE,
16, 3, FALSE,
17, 3, FALSE,
18, 3, TRUE,
19, 4, FALSE,
20, 4, FALSE,
21, 4, FALSE,
22, 4, FALSE,
23, 4, FALSE,
24, 4, FALSE,
25, 4, FALSE
)
# Load and clean
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Prepare question columns
qnums_all <- 1:25
condition_cols <- imi_clean %>%
select(
participant_id,
# Conditions 1–4
all_of(unlist(lapply(1:4, function(c) paste0("imi", qnums_all, "_condition", c)))),
# Condition 5 (_final)
all_of(paste0("imi", qnums_all, "_final"))
)
# Pivot to long format (handle both patterns)
long_data <- condition_cols %>%
pivot_longer(
cols = -participant_id,
names_to = "item",
values_to = "response"
) %>%
filter(!is.na(response)) %>%
mutate(
qn = as.integer(str_extract(item, "\\d+")),
condition = case_when(
grepl("_condition(\\d)", item) ~ str_extract(item, "(?<=_condition)\\d"),
grepl("_final$", item) ~ "5"
),
response_num = as.numeric(response)
) %>%
left_join(subscale_map, by = "qn") %>%
mutate(
response_num = ifelse(reverse, 8 - response_num, response_num)
)
# Average per subscale
subscale_scores <- long_data %>%
group_by(participant_id, condition, subscale) %>%
summarise(avg_score = mean(response_num, na.rm = TRUE), .groups = "drop")
# 1.5*IQR outlier removal
filtered_scores <- subscale_scores %>%
group_by(condition, subscale) %>%
mutate(
Q1 = quantile(avg_score, 0.25),
Q3 = quantile(avg_score, 0.75),
IQR = Q3 - Q1,
lower = Q1 - 1.5 * IQR,
upper = Q3 + 1.5 * IQR
) %>%
filter(avg_score >= lower & avg_score <= upper) %>%
ungroup()
# Mean labels
mean_labels <- filtered_scores %>%
group_by(condition, subscale) %>%
summarise(mean_val = mean(avg_score), .groups = "drop")
# Define subscale names
subscale_names <- c(
"1" = "Interest/Enjoyment",
"2" = "Perceived Competence",
"3" = "Effort/Importance",
"4" = "Value/Usefulness"
)
# Apply factor labels
filtered_scores <- filtered_scores %>%
mutate(subscale = factor(subscale, levels = names(subscale_names), labels = subscale_names))
mean_labels <- mean_labels %>%
mutate(subscale = factor(subscale, levels = names(subscale_names), labels = subscale_names))
# Define all condition labels including "Final"
condition_labels <- c(
"1" = "Control",
"2" = "Reading",
"3" = "Writing",
"4" = "Chatbot",
"5" = "Final"
)
# Apply descriptive labels
filtered_scores <- filtered_scores %>%
mutate(condition_label = factor(condition, levels = names(condition_labels), labels = condition_labels))
mean_labels <- mean_labels %>%
mutate(condition_label = factor(condition, levels = names(condition_labels), labels = condition_labels))
# Plot with all 5 conditions
ggplot(filtered_scores, aes(x = subscale, y = avg_score, fill = condition_label)) +
geom_boxplot(outlier.shape = NA, position = position_dodge(width = 0.75)) +
geom_jitter(aes(color = condition_label), size = 1.5, alpha = 0.5,
position = position_jitterdodge(jitter.width = 0.1, dodge.width = 0.75)) +
geom_text(
data = mean_labels,
aes(label = round(mean_val, 2), y = mean_val),
position = position_dodge(width = 0.75),
vjust = -0.5,
size = 3,
color = "black"
) +
labs(
title = "Average Subscale Scores by Condition (Outliers Removed)",
x = "Subscale",
y = "Average Score (Reversed where needed)",
fill = "Condition",
color = "Condition"
) +
theme_minimal()
IMI All Subscales per Condition - ANOVA
# Load libraries
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stats)
# Define subscale map
subscale_map <- tribble(
~qn, ~subscale, ~reverse,
1, 1, FALSE,
2, 1, FALSE,
3, 1, TRUE,
4, 1, TRUE,
5, 1, FALSE,
6, 1, FALSE,
7, 1, FALSE,
8, 2, FALSE,
9, 2, FALSE,
10, 2, FALSE,
11, 2, FALSE,
12, 2, FALSE,
13, 2, TRUE,
14, 3, FALSE,
15, 3, TRUE,
16, 3, FALSE,
17, 3, FALSE,
18, 3, TRUE,
19, 4, FALSE,
20, 4, FALSE,
21, 4, FALSE,
22, 4, FALSE,
23, 4, FALSE,
24, 4, FALSE,
25, 4, FALSE
)
# Load data
imi <- read_csv("imi.csv", show_col_types = FALSE)
# Exclude problematic participant IDs
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Prepare column names
qnums_all <- 1:25
condition_1_4_cols <- paste0("imi", qnums_all, "_condition", rep(1:4, each = 25))
condition_5_cols <- paste0("imi", qnums_all, "_final")
all_imi_cols <- unique(c("participant_id", condition_1_4_cols, condition_5_cols))
# Select relevant columns
condition_cols <- imi_clean %>% select(any_of(all_imi_cols))
# Pivot longer to include both condition types
long_data <- condition_cols %>%
pivot_longer(
cols = -participant_id,
names_to = "item",
values_to = "response"
) %>%
filter(!is.na(response)) %>%
mutate(
qn = as.integer(str_extract(item, "\\d+")),
condition = case_when(
grepl("_condition(\\d)", item) ~ str_extract(item, "(?<=_condition)\\d"),
grepl("_final$", item) ~ "5"
),
response_num = as.numeric(response)
) %>%
left_join(subscale_map, by = "qn") %>%
mutate(
response_num = ifelse(reverse, 8 - response_num, response_num)
)
# Average score per participant, condition, subscale
subscale_scores <- long_data %>%
group_by(participant_id, condition, subscale) %>%
summarise(avg_score = mean(response_num, na.rm = TRUE), .groups = "drop")
# Remove 1.5*IQR outliers within each condition and subscale
subscale_scores_filtered <- subscale_scores %>%
group_by(condition, subscale) %>%
mutate(
Q1 = quantile(avg_score, 0.25, na.rm = TRUE),
Q3 = quantile(avg_score, 0.75, na.rm = TRUE),
IQR = Q3 - Q1,
lower = Q1 - 1.5 * IQR,
upper = Q3 + 1.5 * IQR
) %>%
ungroup() %>%
filter(avg_score >= lower, avg_score <= upper) %>%
select(participant_id, condition, subscale, avg_score)
# Reshape for tests: one row per participant per subscale
test_data <- subscale_scores_filtered %>%
pivot_wider(
names_from = condition,
values_from = avg_score,
names_prefix = "condition"
)
# STEP 1: Overall Friedman tests per subscale
overall_results <- list()
significant_subscales <- c()
for (s in unique(subscale_scores$subscale)) {
sub_data <- test_data %>% filter(subscale == s)
scores_mat <- sub_data %>%
select(starts_with("condition")) %>%
na.omit() %>%
as.matrix()
if (nrow(scores_mat) >= 3) {
friedman_res <- friedman.test(scores_mat)
overall_results[[length(overall_results) + 1]] <- data.frame(
subscale = s,
p_value = friedman_res$p.value
)
if (friedman_res$p.value < 0.05) {
significant_subscales <- c(significant_subscales, s)
}
}
}
overall_df <- bind_rows(overall_results)
overall_df$p_adj_bh <- p.adjust(overall_df$p_value, method = "BH")
# STEP 2: Post-hoc pairwise Wilcoxon tests
pairwise_results <- list()
conditions <- c("1", "2", "3", "4", "5")
pair_combinations <- combn(conditions, 2, simplify = FALSE)
for (s in significant_subscales) {
sub_data <- test_data %>% filter(subscale == s)
for (pair in pair_combinations) {
c1 <- paste0("condition", pair[1])
c2 <- paste0("condition", pair[2])
paired_data <- sub_data %>%
select(participant_id, all_of(c(c1, c2))) %>%
na.omit()
if (nrow(paired_data) >= 3) {
test <- wilcox.test(paired_data[[c1]], paired_data[[c2]], paired = TRUE)
pairwise_results[[length(pairwise_results) + 1]] <- data.frame(
subscale = s,
comparison = paste("Condition", pair[1], "vs", pair[2]),
p_value = test$p.value
)
}
}
}
pairwise_df <- bind_rows(pairwise_results)
pairwise_df$p_adj_bh <- p.adjust(pairwise_df$p_value, method = "BH")
# Label subscales
subscale_names <- c(
"1" = "Interest/Enjoyment",
"2" = "Perceived Competence",
"3" = "Effort/Importance",
"4" = "Value/Usefulness"
)
# Final formatting
overall_df$subscale <- factor(overall_df$subscale, levels = names(subscale_names), labels = subscale_names)
pairwise_df$subscale <- factor(pairwise_df$subscale, levels = names(subscale_names), labels = subscale_names)
# View results
cat("=== Overall Friedman Test Results ===\n")
print(overall_df)
cat("\n=== Pairwise Wilcoxon Comparisons (Only for Significant Subscales) ===\n")
pairwise_df = pairwise_df %>% filter(p_value < 0.05)
print(pairwise_df )
Per-lesson IMI
library(readr)
library(dplyr)
library(tidyr)
library(likert)
library(ggplot2) # for ggtitle()
# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Select all lesson columns (lesson1 to lesson4)
lesson_cols <- imi_clean %>%
select(participant_id, matches("^imi\\d+_lesson[1-4]$"))
# Convert from wide to long format (all lessons)
long_data <- lesson_cols %>%
pivot_longer(
cols = -participant_id,
names_to = c("question", "lesson"),
names_pattern = "imi(\\d+)_lesson(\\d)",
values_to = "response"
) %>%
mutate(
question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
response = factor(as.numeric(response),
levels = 1:7,
labels = c("Extremely disagree", "Disagree", "Slightly disagree",
"Neutral", "Slightly agree", "Agree", "Extremely agree"),
ordered = TRUE)
)
full_levels <- c("Extremely disagree", "Disagree", "Slightly disagree",
"Neutral", "Slightly agree", "Agree", "Extremely agree")
# Loop over each lesson and create separate likert plots
for (lesson_num in unique(long_data$lesson)) {
lesson_data <- long_data %>% filter(lesson == lesson_num)
likert_ready <- lesson_data %>%
group_by(participant_id) %>%
select(participant_id, question, response) %>%
pivot_wider(names_from = question, values_from = response) %>%
select(paste0("Q", 1:25))
likert_ready <- as.data.frame(likert_ready)
# Ensure all columns have the same ordered factor levels:
likert_ready[] <- lapply(likert_ready, function(x) {
factor(x, levels = full_levels, ordered = TRUE)
})
likert_obj <- likert(likert_ready)
print(plot(likert_obj, group.order = paste0("Q", 1:25)) +
ggtitle(paste("Likert Plot for Lesson", lesson_num)))
}
Per-lesson IMI - ANOVA
library(readr)
library(dplyr)
library(tidyr)
library(purrr)
# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Select relevant lesson columns
lesson_cols <- imi_clean %>%
select(participant_id, matches("^imi\\d+_lesson[1-4]$"))
# Convert from wide to long format
long_data <- lesson_cols %>%
pivot_longer(
cols = -participant_id,
names_to = c("question", "lesson"),
names_pattern = "imi(\\d+)_lesson(\\d)",
values_to = "response"
) %>%
mutate(
question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
lesson = factor(lesson, levels = 1:4),
response_num = as.numeric(response)
)
# Shapiro-Wilk test for normality per question (combining lessons)
normality_results <- long_data %>%
group_by(question) %>%
summarise(
shapiro_p = if(n() >= 3) shapiro.test(response_num)$p.value else NA_real_
)
print(normality_results)
# Prepare data wide: each row = participant & question, columns = lessons
friedman_data <- long_data %>%
select(participant_id, question, lesson, response_num) %>%
pivot_wider(names_from = lesson, values_from = response_num) %>%
filter(complete.cases(.)) # only keep participants with all lessons
# Run Friedman test per question
friedman_results <- friedman_data %>%
group_by(question) %>%
summarise(
p_value = {
mat <- as.matrix(select(cur_data(), `1`, `2`, `3`, `4`))
friedman.test(mat)$p.value
}
)
print(friedman_results)
# Step 2: Post-hoc pairwise Wilcoxon signed-rank tests for those questions
# Function to do pairwise Wilcoxon for a question
posthoc_wilcox <- function(df) {
# df: filtered for one question, wide format with lessons columns
lessons <- c("1","2","3","4")
pairs <- combn(lessons, 2, simplify = FALSE)
results <- map_dfr(pairs, function(pair) {
res <- wilcox.test(df[[pair[1]]], df[[pair[2]]], paired = TRUE, exact = FALSE)
mean_diff <- mean(df[[pair[1]]] - df[[pair[2]]])
tibble(
lesson_1 = pair[1],
lesson_2 = pair[2],
p_value = res$p.value,
mean_difference = mean_diff
)
})
# Adjust p-values for multiple comparisons (Bonferroni)
results <- results %>%
mutate(p_adj = p.adjust(p_value, method = "bonferroni"))
return(results)
}
# Run posthoc tests for each significant question
posthoc_results <- friedman_results %>%
pull(question) %>%
set_names() %>%
map_df(function(q) {
df_q <- friedman_data %>% filter(question == q)
res <- posthoc_wilcox(df_q)
res$question <- q
res
}, .id = "question")
# Filter only pairs with significant adjusted p-values < 0.05
significant_pairs <- posthoc_results %>%
filter(p_adj < 0.05) %>%
select(question, lesson_1, lesson_2, mean_difference, p_value, p_adj)
print(significant_pairs)
Per-condition IMI
library(readr)
library(dplyr)
library(tidyr)
library(likert)
library(ggplot2) # for ggtitle()
# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Select all condition columns (condition1 to condition4)
condition_cols <- imi_clean %>%
select(participant_id, matches("^imi\\d+_condition[1-4]$"))
# Convert from wide to long format (all conditions)
long_data <- condition_cols %>%
pivot_longer(
cols = -participant_id,
names_to = c("question", "condition"),
names_pattern = "imi(\\d+)_condition(\\d)",
values_to = "response"
) %>%
mutate(
question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
response = factor(as.numeric(response),
levels = 1:7,
labels = c("Extremely disagree", "Disagree", "Slightly disagree",
"Neutral", "Slightly agree", "Agree", "Extremely agree"),
ordered = TRUE)
)
full_levels <- c("Extremely disagree", "Disagree", "Slightly disagree",
"Neutral", "Slightly agree", "Agree", "Extremely agree")
# Loop over each condition and create separate likert plots
for (condition_num in unique(long_data$condition)) {
condition_data <- long_data %>% filter(condition == condition_num)
likert_ready <- condition_data %>%
group_by(participant_id) %>%
select(participant_id, question, response) %>%
pivot_wider(names_from = question, values_from = response) %>%
select(paste0("Q", 1:25))
likert_ready <- as.data.frame(likert_ready)
# Ensure all columns have the same ordered factor levels:
likert_ready[] <- lapply(likert_ready, function(x) {
factor(x, levels = full_levels, ordered = TRUE)
})
likert_obj <- likert(likert_ready)
print(plot(likert_obj, group.order = paste0("Q", 1:25)) +
ggtitle(paste("Likert Plot for Condition", condition_num)))
}
Per-condition IMI - ANOVA
library(readr)
library(dplyr)
library(tidyr)
# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)
# Select relevant condition columns
lesson_cols <- imi_clean %>%
select(participant_id, matches("^imi\\d+_condition[1-4]$"))
# Convert from wide to long format
long_data <- lesson_cols %>%
pivot_longer(
cols = -participant_id,
names_to = c("question", "condition"),
names_pattern = "imi(\\d+)_condition(\\d)",
values_to = "response"
) %>%
mutate(
question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
lesson = factor(condition, levels = 1:4),
response_num = as.numeric(response)
)
# Shapiro-Wilk test for normality per question (combining lessons)
normality_results <- long_data %>%
group_by(question) %>%
summarise(
shapiro_p = if(n() >= 3) shapiro.test(response_num)$p.value else NA_real_
)
print(normality_results)
# Prepare data wide: each row = participant & question, columns = lessons
friedman_data <- long_data %>%
select(participant_id, question, condition, response_num) %>%
pivot_wider(names_from = condition, values_from = response_num) %>%
filter(complete.cases(.)) # only keep participants with all lessons
# Run Friedman test per question
friedman_results <- friedman_data %>%
group_by(question) %>%
summarise(
p_value = {
mat <- as.matrix(select(cur_data(), `1`, `2`, `3`, `4`))
friedman.test(mat)$p.value
}
)
print(friedman_results)
# Step 2: Post-hoc pairwise Wilcoxon signed-rank tests for those questions
# Function to do pairwise Wilcoxon for a question
posthoc_wilcox <- function(df) {
# df: filtered for one question, wide format with lessons columns
conditions <- c("1","2","3","4")
pairs <- combn(conditions, 2, simplify = FALSE)
results <- map_dfr(pairs, function(pair) {
res <- wilcox.test(df[[pair[1]]], df[[pair[2]]], paired = TRUE, exact = FALSE)
mean_diff <- mean(df[[pair[1]]] - df[[pair[2]]])
tibble(
condition_1 = pair[1],
condition_2 = pair[2],
p_value = res$p.value,
mean_difference = mean_diff
)
})
# Adjust p-values for multiple comparisons (bh)
results <- results %>%
mutate(p_adj_bh = p.adjust(p_value, method = "BH"))
results <- results %>%
mutate(p_adj_bonferroni = p.adjust(p_value, method = "bonferroni"))
return(results)
}
# Run posthoc tests for each significant question
posthoc_results <- friedman_results %>%
pull(question) %>%
set_names() %>%
map_df(function(q) {
df_q <- friedman_data %>% filter(question == q)
res <- posthoc_wilcox(df_q)
res$question <- q
res
}, .id = "question")
# Filter only pairs with significant adjusted p-values < 0.05
significant_pairs <- posthoc_results %>%
filter(p_value < 0.1) %>%
filter(condition_2 == 4) %>%
select(question, condition_1, condition_2, mean_difference, p_value, p_adj_bh, p_adj_bonferroni)
print(significant_pairs)
LIWC
Correlations on Users (mid to post)
library(readr)
library(dplyr)
library(corrr)
print(data)
# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
select(user_id, matches("likert_(mid|post)_condition\\d+")) %>% # keep user_id
pivot_longer(
cols = -user_id,
names_to = c("phase", "condition"),
names_pattern = "likert_(mid|post)_condition(\\d+)",
values_to = "score"
) %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff = post - mid,
condition = case_when(
condition == "1" ~ "Control",
condition == "2" ~ "Reading",
condition == "3" ~ "Writing",
condition == "4" ~ "Chatbot",
TRUE ~ condition
)
) %>%
select(user_id, condition, diff) %>%
pivot_wider(names_from = condition, values_from = diff)
# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - participant_texts - LIWC Analysis.csv", delim = ";")
# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>% # Extract numeric ID from "user_XX"
filter(!user_id %in% excluded_ids)
# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
mutate(user_id = as.numeric(user_id)) %>%
filter(!user_id %in% excluded_ids)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
filter(!user_id %in% excluded_ids)
# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")
print(merged_data)
# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
select(-Segment, -user_id) %>%
select(where(is.numeric)) %>% names()
cor_input <- merged_data %>%
select(all_of(liwc_columns), Chatbot)
cor_results <- correlate(cor_input) %>%
focus(Chatbot) %>%
arrange(desc(abs(Chatbot))) # Sort by strength of correlation
# 6. View results
print(cor_results)
# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
filter(abs(Chatbot) > 0.3)
print(strong_corrs)
# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
select(all_of(liwc_columns))
chatbot_scores <- merged_data$Chatbot
# Initialize a results data frame
results <- data.frame(term = character(),
cor = numeric(),
p_value = numeric(),
stringsAsFactors = FALSE)
# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
results <- rbind(results, data.frame(
term = var,
cor = test$estimate,
p_value = test$p.value
))
}
# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))
results <- results %>%
mutate(p_adj = p.adjust(p_value, method = "BH")) # FDR correction
# View significant after correction
sig_results <- results %>%
filter(p_value < 0.05)
print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)
# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
select(user_id, Chatbot, all_of(liwc_columns))
# Convert LIWC terms to long format
long_data <- plot_data %>%
pivot_longer(
cols = -c(user_id, Chatbot),
names_to = "LIWC_term",
values_to = "LIWC_value"
)
# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)
# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
group_by(LIWC_term) %>%
summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')
# Merge with the correlation results
results_with_proportion <- results %>%
inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))
# Set thresholds
cor_threshold <- 0.3
nz_threshold <- 0.3
# Filter for strong correlations and non-zero inflated terms
# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
filter(term == "health") %>%
pull(term)
# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
filter(LIWC_term %in% filtered_terms)
library(dplyr)
library(tidyr)
library(ggplot2)
# Add custom names mapping
custom_names <- c(
"health" = "Health"
)
# Filter and prepare annotations
annotations <- results_with_proportion %>%
filter(term %in% filtered_terms) %>%
mutate(
label = paste0("r = ", round(cor, 2), ", p = ", format(p_adj, digits = 2)),
LIWC_term = term
) %>%
select(LIWC_term, label)
# Add annotations to the long data for plotting
top_non_zero_long_data <- top_non_zero_long_data %>%
left_join(annotations, by = "LIWC_term")
# Plot with custom facet labels
ggplot(top_non_zero_long_data, aes(x = LIWC_value, y = Chatbot)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE, color = "steelblue") +
facet_wrap(~ LIWC_term, scales = "free_x", labeller = labeller(LIWC_term = custom_names)) +
labs(
title = "Correlation of LIWC 'Health' Term Value\nand Post-Attack Certainty Change",
x = "LIWC 'Health' Term Value",
y = "Post-Attack Certainty Change"
) +
theme_minimal(base_size = 14) +
theme(strip.text = element_text(size = 12, face = "bold")) +
geom_label(
data = annotations,
mapping = aes(x = Inf, y = Inf, label = label),
hjust = 1.1, vjust = 1.5,
inherit.aes = FALSE,
fill = alpha("white", 0.5)
)
Correlations on Users (pre to mid)
library(readr)
library(dplyr)
library(corrr)
print(data)
# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
select(user_id, matches("likert_(pre|mid)_condition\\d+")) %>% # keep user_id
pivot_longer(
cols = -user_id,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid)_condition(\\d+)",
values_to = "score"
) %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff = mid - pre,
condition = case_when(
condition == "1" ~ "Control",
condition == "2" ~ "Reading",
condition == "3" ~ "Writing",
condition == "4" ~ "Chatbot",
TRUE ~ condition
)
) %>%
select(user_id, condition, diff) %>%
pivot_wider(names_from = condition, values_from = diff)
# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - participant_texts - LIWC Analysis.csv", delim = ";")
# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>% # Extract numeric ID from "user_XX"
filter(!user_id %in% excluded_ids)
# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
mutate(user_id = as.numeric(user_id)) %>%
filter(!user_id %in% excluded_ids)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
filter(!user_id %in% excluded_ids)
# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")
print(merged_data)
# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
select(-Segment, -user_id) %>%
select(where(is.numeric)) %>% names()
cor_input <- merged_data %>%
select(all_of(liwc_columns), Chatbot)
cor_results <- correlate(cor_input) %>%
focus(Chatbot) %>%
arrange(desc(abs(Chatbot))) # Sort by strength of correlation
# 6. View results
print(cor_results)
# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
filter(abs(Chatbot) > 0.3)
print(strong_corrs)
# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
select(all_of(liwc_columns))
chatbot_scores <- merged_data$Chatbot
# Initialize a results data frame
results <- data.frame(term = character(),
cor = numeric(),
p_value = numeric(),
stringsAsFactors = FALSE)
# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
results <- rbind(results, data.frame(
term = var,
cor = test$estimate,
p_value = test$p.value
))
}
# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))
results <- results %>%
mutate(p_adj = p.adjust(p_value, method = "BH")) # FDR correction
# View significant after correction
sig_results <- results %>%
filter(p_value < 0.05)
print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)
# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
select(user_id, Chatbot, all_of(liwc_columns))
# Convert LIWC terms to long format
long_data <- plot_data %>%
pivot_longer(
cols = -c(user_id, Chatbot),
names_to = "LIWC_term",
values_to = "LIWC_value"
)
# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)
# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
group_by(LIWC_term) %>%
summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')
# Merge with the correlation results
results_with_proportion <- results %>%
inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))
# Set thresholds
cor_threshold <- 0.3
nz_threshold <- 0.3
# Filter for strong correlations and non-zero inflated terms
# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
filter(term == "health") %>%
pull(term)
# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
filter(LIWC_term %in% filtered_terms)
Correlations on Chatbot (mid to post)
library(readr)
library(dplyr)
library(corrr)
print(data)
# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
select(user_id, matches("likert_(mid|post)_condition\\d+")) %>% # keep user_id
pivot_longer(
cols = -user_id,
names_to = c("phase", "condition"),
names_pattern = "likert_(mid|post)_condition(\\d+)",
values_to = "score"
) %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff = post - mid,
condition = case_when(
condition == "1" ~ "Control",
condition == "2" ~ "Reading",
condition == "3" ~ "Writing",
condition == "4" ~ "Chatbot",
TRUE ~ condition
)
) %>%
select(user_id, condition, diff) %>%
pivot_wider(names_from = condition, values_from = diff)
# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - bot_texts - LIWC Analysis.csv", delim = ";")
# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>% # Extract numeric ID from "user_XX"
filter(!user_id %in% excluded_ids)
# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
mutate(user_id = as.numeric(user_id)) %>%
filter(!user_id %in% excluded_ids)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
filter(!user_id %in% excluded_ids)
# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")
print(merged_data)
# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
select(-Segment, -user_id) %>%
select(where(is.numeric)) %>% names()
cor_input <- merged_data %>%
select(all_of(liwc_columns), Chatbot)
cor_results <- correlate(cor_input) %>%
focus(Chatbot) %>%
arrange(desc(abs(Chatbot))) # Sort by strength of correlation
# 6. View results
print(cor_results)
# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
filter(abs(Chatbot) > 0.3)
print(strong_corrs)
# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
select(all_of(liwc_columns))
chatbot_scores <- merged_data$Chatbot
# Initialize a results data frame
results <- data.frame(term = character(),
cor = numeric(),
p_value = numeric(),
stringsAsFactors = FALSE)
# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
results <- rbind(results, data.frame(
term = var,
cor = test$estimate,
p_value = test$p.value
))
}
# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))
results <- results %>%
mutate(p_adj = p.adjust(p_value, method = "BH")) # FDR correction
# View significant after correction
sig_results <- results %>%
filter(p_value < 0.05)
print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)
# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
select(user_id, Chatbot, all_of(liwc_columns))
# Convert LIWC terms to long format
long_data <- plot_data %>%
pivot_longer(
cols = -c(user_id, Chatbot),
names_to = "LIWC_term",
values_to = "LIWC_value"
)
# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)
# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
group_by(LIWC_term) %>%
summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')
# Merge with the correlation results
results_with_proportion <- results %>%
inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))
# Set thresholds
cor_threshold <- 0.3
nz_threshold <- 0.3
# Filter for strong correlations and non-zero inflated terms
# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
filter(term == "health") %>%
pull(term)
# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
filter(LIWC_term %in% filtered_terms)
library(dplyr)
library(tidyr)
library(ggplot2)
# Add custom names mapping
custom_names <- c(
"health" = "Health"
)
# Filter and prepare annotations
annotations <- results_with_proportion %>%
filter(term %in% filtered_terms) %>%
mutate(
label = paste0("r = ", round(cor, 2), ", p = ", format(p_adj, digits = 2)),
LIWC_term = term
) %>%
select(LIWC_term, label)
# Add annotations to the long data for plotting
top_non_zero_long_data <- top_non_zero_long_data %>%
left_join(annotations, by = "LIWC_term")
# Plot with custom facet labels
ggplot(top_non_zero_long_data, aes(x = LIWC_value, y = Chatbot)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE, color = "steelblue") +
facet_wrap(~ LIWC_term, scales = "free_x", labeller = labeller(LIWC_term = custom_names)) +
labs(
title = "Correlation of LIWC 'Health' Term Value\nand Post-Inoculation Certainty Change",
x = "LIWC 'Health' Term Value",
y = "Post-Attack Certainty Change"
) +
theme_minimal(base_size = 14) +
theme(strip.text = element_text(size = 12, face = "bold")) +
geom_label(
data = annotations,
mapping = aes(x = Inf, y = Inf, label = label),
hjust = 1.1, vjust = 1.5,
inherit.aes = FALSE,
fill = alpha("white", 0.5)
)
Correlations on Chatbot (pre to mid)
library(readr)
library(dplyr)
library(corrr)
print(data)
# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
select(user_id, matches("likert_(pre|mid)_condition\\d+")) %>% # keep user_id
pivot_longer(
cols = -user_id,
names_to = c("phase", "condition"),
names_pattern = "likert_(pre|mid)_condition(\\d+)",
values_to = "score"
) %>%
pivot_wider(names_from = phase, values_from = score) %>%
mutate(
diff = mid - pre,
condition = case_when(
condition == "1" ~ "Control",
condition == "2" ~ "Reading",
condition == "3" ~ "Writing",
condition == "4" ~ "Chatbot",
TRUE ~ condition
)
) %>%
select(user_id, condition, diff) %>%
pivot_wider(names_from = condition, values_from = diff)
print(all_certainty_deltas)
# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - bot_texts - LIWC Analysis.csv", delim = ";")
# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>% # Extract numeric ID from "user_XX"
filter(!user_id %in% excluded_ids)
# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
mutate(user_id = as.numeric(user_id)) %>%
filter(!user_id %in% excluded_ids)
liwc_filtered <- liwc_data %>%
mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
filter(!user_id %in% excluded_ids)
# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")
print(merged_data)
# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
select(-Segment, -user_id) %>%
select(where(is.numeric)) %>% names()
cor_input <- merged_data %>%
select(all_of(liwc_columns), Chatbot)
cor_results <- correlate(cor_input) %>%
focus(Chatbot) %>%
arrange(desc(abs(Chatbot))) # Sort by strength of correlation
# 6. View results
print(cor_results)
# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
filter(abs(Chatbot) > 0.3)
print(strong_corrs)
# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
select(all_of(liwc_columns))
chatbot_scores <- merged_data$Chatbot
# Initialize a results data frame
results <- data.frame(term = character(),
cor = numeric(),
p_value = numeric(),
stringsAsFactors = FALSE)
# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
results <- rbind(results, data.frame(
term = var,
cor = test$estimate,
p_value = test$p.value
))
}
# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))
results <- results %>%
mutate(p_adj = p.adjust(p_value, method = "BH")) # FDR correction
# View significant after correction
sig_results <- results %>%
filter(p_value < 0.05)
print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)
# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
select(user_id, Chatbot, all_of(liwc_columns))
# Convert LIWC terms to long format
long_data <- plot_data %>%
pivot_longer(
cols = -c(user_id, Chatbot),
names_to = "LIWC_term",
values_to = "LIWC_value"
)
# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)
# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
group_by(LIWC_term) %>%
summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')
# Merge with the correlation results
results_with_proportion <- results %>%
inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))
# Set thresholds
cor_threshold <- 0.3
nz_threshold <- 0.3
# Filter for strong correlations and non-zero inflated terms
# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
filter(term == "health") %>%
pull(term)
# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
filter(LIWC_term %in% filtered_terms)
library(dplyr)
library(tidyr)
library(ggplot2)
# Add custom names mapping
custom_names <- c(
"health" = "Health"
)
# Filter and prepare annotations
annotations <- results_with_proportion %>%
filter(term %in% filtered_terms) %>%
mutate(
label = paste0("r = ", round(cor, 2), ", p = ", format(p_adj, digits = 2)),
LIWC_term = term
) %>%
select(LIWC_term, label)
# Add annotations to the long data for plotting
top_non_zero_long_data <- top_non_zero_long_data %>%
left_join(annotations, by = "LIWC_term")
# Plot with custom facet labels
ggplot(top_non_zero_long_data, aes(x = LIWC_value, y = Chatbot)) +
geom_point(alpha = 0.6) +
geom_smooth(method = "lm", se = FALSE, color = "steelblue") +
facet_wrap(~ LIWC_term, scales = "free_x", labeller = labeller(LIWC_term = custom_names)) +
labs(
title = "Correlation of LIWC 'Health' Term Value\nand Post-Inoculation Certainty Change",
x = "LIWC 'Health' Term Value",
y = "Post-Inoculation Certainty Change"
) +
theme_minimal(base_size = 14) +
theme(strip.text = element_text(size = 12, face = "bold")) +
geom_label(
data = annotations,
mapping = aes(x = Inf, y = Inf, label = label),
hjust = 1.1, vjust = 1.5,
inherit.aes = FALSE,
fill = alpha("white", 0.5)
)
---
title: "MindFort Analysis Notebook"
output: html_notebook
---

# Load and preprocess certainty data

**Preprocessing**: For all the analysis below, we omitted data of 3 participants who have failed at least one attention check during the experiments, omitted all individual lesson data where the participants' initial certainty score was below 10 points on the 15-point scale and removed any outliers (1.5 IQR).

```{r}
library(readr)
library(dplyr)
library(stringr)

data <- read_csv("certainty.csv", show_col_types = FALSE) %>%
  filter(exclude == 0) %>%
  filter(participant_id > -1)
  

# Get all unique suffixes for lessons and conditions
likert_pre_cols <- grep("^likert_pre_", names(data), value = TRUE)
suffixes <- str_replace(likert_pre_cols, "^likert_pre_", "")

for (suf in suffixes) {
  pre_col <- paste0("likert_pre_", suf)
  mid_col <- paste0("likert_mid_", suf)
  post_col <- paste0("likert_post_", suf)
  
  # Check all columns exist
  if (all(c(pre_col, mid_col, post_col) %in% names(data))) {
    data <- data %>%
      mutate(
        across(
          all_of(c(pre_col, mid_col, post_col)),
          ~ if_else(.data[[pre_col]] < 10, NA_real_, .)
        )
      )
  }
}

print(data)


```

# Demographics

```{r}
# Load necessary library
library(dplyr)

# Load the data from the CSV file
demographics_data <- read.csv("demographics.csv", stringsAsFactors = FALSE)

# Specify the IDs to remove
ids_to_remove <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")

# Filter the data to remove participants with the specified IDs
filtered_data <- demographics_data %>%
  filter(!Participant.id %in% ids_to_remove)

# Filter out rows where Status is not 'APPROVED'
accepted_data <- filtered_data %>%
  filter(Status == "APPROVED")
# Assuming 'accepted_data' is the filtered dataset for participants with 'Status' == 'ACCEPTED'
# Ensure Age column is numeric
accepted_data$Age <- as.numeric(accepted_data$Age)

# Check for conversion issues and warn if any entries became NA
conversion_issues <- sum(is.na(accepted_data$Age))
if (conversion_issues > 0) {
  warning(paste(conversion_issues, "non-numeric entries were converted to NA in the Age column."))
}

# Calculate mean and standard deviation, ignoring NAs
age_stats <- accepted_data %>%
  summarise(
    Mean_Age = mean(Age, na.rm = TRUE),
    SD_Age = sd(Age, na.rm = TRUE)
  )

age_summary <- paste0("Mean: ", round(age_stats$Mean_Age, 2), ", SD: ", round(age_stats$SD_Age, 2))


# Calculate gender distribution
gender_distribution <- paste(names(table(accepted_data$Sex)), 
                             table(accepted_data$Sex), 
                             sep = ": ", 
                             collapse = ", ")

# Calculate ethnicity distribution
ethnicity_distribution <- paste(names(table(accepted_data$Ethnicity.simplified)), 
                                table(accepted_data$Ethnicity.simplified), 
                                sep = ": ", 
                                collapse = ", ")

# List all nationalities with counts
nationality_distribution <- paste(names(table(accepted_data$Nationality)), 
                                  table(accepted_data$Nationality), 
                                  sep = ": ", 
                                  collapse = ", ")

# Calculate student status distribution
student_status_distribution <- paste(names(table(accepted_data$Student.status)), 
                                     table(accepted_data$Student.status), 
                                     sep = ": ", 
                                     collapse = ", ")

# Calculate employment status distribution
employment_status_distribution <- paste(names(table(accepted_data$Employment.status)), 
                                        table(accepted_data$Employment.status), 
                                        sep = ": ", 
                                        collapse = ", ")

# Compile results into a data frame
demographics_summary <- data.frame(
  Statistic = c(
    "Age (Mean, SD)",
    "Gender Distribution",
    "Ethnicity Distribution",
    "Nationalities",
    "Student Status Distribution",
    "Employment Status Distribution"
  ),
  Value = c(
    age_summary,
    gender_distribution,
    ethnicity_distribution,
    nationality_distribution,
    student_status_distribution,
    employment_status_distribution
  ),
  stringsAsFactors = FALSE
)

# Print the summary table
print(demographics_summary)
```

# Timeline data

```{r}
library(DBI)
library(RSQLite)
library(dplyr)
library(ggplot2)
library(lubridate)

# Connect to DB and load filtered data
con <- dbConnect(RSQLite::SQLite(), "database.db")

load_phase <- function(table, phase) {
  dbReadTable(con, table) %>%
    filter(user_id >= 0, user_id <= 2000) %>%
    arrange(user_id, created_at) %>%
    group_by(user_id) %>%
    mutate(phase = phase, index = row_number()) %>%
    ungroup()
}

# Load each phase
pre <- load_phase("likert_pre", "pre")
mid <- load_phase("likert_mid", "mid")
post <- load_phase("likert_post", "post")

# Combine all
all_data <- bind_rows(pre, mid, post) %>%
  mutate(created_at = ymd_hms(created_at))

# Create interleaved step label: pre_1, mid_1, post_1, ..., post_4
all_data <- all_data %>%
  mutate(step = paste0(phase, "_", index))

# Normalize time per user relative to their first submission
all_data <- all_data %>%
  group_by(user_id) %>%
  arrange(created_at) %>%
  mutate(rel_time = as.numeric(difftime(created_at, min(created_at), units = "mins"))) %>%
  ungroup()

# Set ordered factor for correct x-axis sequencing
# Interleaved step order: pre_1, mid_1, post_1, ..., post_4
step_levels <- as.vector(t(outer(1:4, c("pre", "mid", "post"), function(i, p) paste0(p, "_", i))))
all_data$step <- factor(all_data$step, levels = step_levels)

# Plot progression
ggplot(all_data, aes(x = step, y = rel_time, group = user_id, color = as.factor(user_id))) +
  geom_line(linewidth = 1) +
  geom_point(size = 2.5) +
  labs(
    title = "User Progression Across All Steps (Relative Time)",
    x = "Step",
    y = "Time Since First Submission (minutes)",
    color = "User ID"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))


# Disconnect from database
dbDisconnect(con)

```

# Data Integrity Assertions

**Assertion 1:** The pre-scores are not significantly different across conditions.\
**Assertion 2:** The pre-scores are significantly different across conditions. **Assertion 3:** Participant has exactly 4 imi scores and are in the right order (lesson 0, 1, 2, 3) based on submission time

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)
library(purrr)

# STEP 1: Extract and reshape pre scores for conditions
condition_pre <- data %>%
  select(matches("likert_pre_condition\\d+")) %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = "condition",
    names_pattern = "likert_pre_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(condition = paste0("Condition ", condition))

# STEP 2: Extract and reshape pre scores for lessons
lesson_pre <- data %>%
  select(matches("likert_pre_lesson\\d+")) %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = "lesson",
    names_pattern = "likert_pre_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(lesson = paste0("Lesson ", lesson))

# STEP 3: Plot pre scores per condition
plot_conditions <- ggplot(condition_pre, aes(x = condition, y = score, fill = condition)) +
  geom_boxplot(alpha = 0.6, outlier.shape = NA) +
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = condition)) +
  labs(title = "Pre Scores by Condition", x = "Condition", y = "Pre Score") +
  theme_minimal() +
  theme(legend.position = "none")

# STEP 4: Plot pre scores per LESSON
plot_lessons <- ggplot(lesson_pre, aes(x = lesson, y = score, fill = lesson)) +
  geom_boxplot(alpha = 0.6, outlier.shape = NA) +
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = lesson)) +
  labs(title = "Pre Scores by Lesson", x = "Lesson", y = "Pre Score") +
  theme_minimal() +
  theme(legend.position = "none")

# Show plots
print(plot_conditions)
print(plot_lessons)

library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Reshape pre condition scores
condition_pre <- data %>%
  select(matches("likert_pre_condition\\d+")) %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = "condition",
    names_pattern = "likert_pre_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(condition = factor(paste0("Condition ", condition)))

# Kruskal-Wallis test
kw <- kruskal.test(score ~ condition, data = condition_pre)

cat("Kruskal-Wallis test p-value:", kw$p.value, "\n")

# If significant, run post-hoc pairwise Wilcoxon tests
if (kw$p.value < 0.05) {
  cat("Significant differences found — pairwise Wilcoxon tests:\n")
  pw <- pairwise.wilcox.test(condition_pre$score, condition_pre$condition, p.adjust.method = "BH")
  print(pw$p.value)
} else {
  cat("No significant differences between conditions for pre scores.\n")
}

library(readr)
library(dplyr)
library(tidyr)

# Reshape pre lesson scores
lesson_pre <- data %>%
  select(matches("likert_pre_lesson\\d+")) %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = "lesson",
    names_pattern = "likert_pre_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(lesson = factor(paste0("Lesson ", lesson)))

# Kruskal-Wallis test
kw <- kruskal.test(score ~ lesson, data = lesson_pre)

cat("Kruskal-Wallis test p-value (by lesson):", kw$p.value, "\n")

# Post-hoc pairwise Wilcoxon if significant
if (kw$p.value < 0.05) {
  cat("Significant differences found — pairwise Wilcoxon tests:\n")
  pw <- pairwise.wilcox.test(lesson_pre$score, lesson_pre$lesson, p.adjust.method = "BH")
  print(pw$p.value)
} else {
  cat("No significant differences between lessons for pre scores.\n")
}

```

# Certainty

## ! Certainty per Condition

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)

# Extract condition-related likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))

# Reshape to long format
long_condition_data <- condition_data %>%
  pivot_longer(cols = everything(),
               names_to = c("phase", "condition"),
               names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
               values_to = "score") %>%
  mutate(phase = factor(phase, levels = c("pre", "mid", "post")),
         condition = paste0("condition ", condition))

# Summarize by phase and condition
summary_data <- long_condition_data %>%
  group_by(phase, condition) %>%
  summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")

# Line plot: 4 lines (conditions), x-axis is phase
ggplot(summary_data, aes(x = phase, y = mean_score, group = condition, color = condition)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  labs(title = "Mean Likert Scores per condition Over Phases",
       x = "Phase",
       y = "Mean Likert Score",
       color = "condition") +
  theme_minimal()

condition_data <- data %>% select(matches("likert_.*_condition"))

long_condition_data <- condition_data %>%
  pivot_longer(
    cols = everything(),
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = factor(paste0("condition ", condition), levels = paste0("condition ", 1:4))
  )

# Calculate means for labels
means <- long_condition_data %>%
  group_by(condition, phase) %>%
  summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")

ggplot(long_condition_data, aes(x = phase, y = score, fill = phase)) +
  geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
  geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.8),
              size = 2, alpha = 0.7) +
  geom_text(data = means, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
            position = position_dodge(width = 0.8), inherit.aes = FALSE,
            size = 3, fontface = "bold", color = "black") +
  facet_wrap(~ condition, nrow = 1) +  # All conditions in one row, visually grouped by facets
  scale_fill_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
  scale_color_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
  labs(
    title = "Likert Scores by Phase and condition (overall)",
    x = "Phase",
    y = "Likert Score",
    fill = "Phase",
    color = "Phase"
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(size = 12, face = "bold"), # facet label style
    legend.position = "top"
  )


# Prepare condition_order in long format
condition_order_long <- data %>%
  mutate(id = row_number()) %>%
  separate(condition_order, into = paste0("lesson_", 1:4), sep = ",", convert = TRUE) %>%
  pivot_longer(
    cols = starts_with("lesson_"),
    names_to = "lesson",
    names_pattern = "lesson_(\\d+)",
    values_to = "condition_index"
  ) %>%
  mutate(
    lesson = as.integer(lesson),
    condition = factor(paste0("condition ", condition_index + 1), levels = paste0("condition ", 1:4))
  ) %>%
  select(id, lesson, condition)

# Prepare long_condition_data with participant id
long_condition_data <- data %>%
  mutate(id = row_number()) %>%
  select(id, matches("likert_.*_condition")) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = factor(paste0("condition ", condition), levels = paste0("condition ", 1:4))
  )

# Join lesson info to condition data
long_condition_with_lesson <- long_condition_data %>%
  left_join(condition_order_long, by = c("id", "condition"))

# Titles for each lesson
comparison_titles <- paste("Lesson", 1:4)

# Generate plots for each lesson and print
for (lesson_num in 1:4) {
  plot_data <- long_condition_with_lesson %>% filter(lesson == lesson_num)
  
  means_lesson <- plot_data %>%
    group_by(condition, phase) %>%
    summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")
  
  p <- ggplot(plot_data, aes(x = phase, y = score, fill = phase)) +
    geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
    geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.8),
                size = 2, alpha = 0.7) +
    geom_text(data = means_lesson, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
              position = position_dodge(width = 0.8), inherit.aes = FALSE,
              size = 3, fontface = "bold", color = "black") +
    facet_wrap(~ condition, nrow = 1) +
    scale_fill_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
    scale_color_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
    labs(
      title = paste("Likert Scores by Phase and condition -", comparison_titles[lesson_num]),
      x = "Phase",
      y = "Likert Score",
      fill = "Phase",
      color = "Phase"
    ) +
    theme_minimal() +
    theme(
      strip.text = element_text(size = 12, face = "bold"),
      legend.position = "top"
    )
  
  print(p)
}


```

## ! Certainty per Condition - ANOVA

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Reshape condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))

long_condition_data <- condition_data %>%
  pivot_longer(
    cols = everything(),
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = factor(paste0("condition ", condition), levels = paste0("condition ", 1:4))
  )

print("\n\nIs there a difference between conditions for pre, mid or post values?")
# Function to run Kruskal-Wallis + pairwise Wilcoxon for one phase
test_phase <- function(phase_name) {
  df <- long_condition_data %>% filter(phase == phase_name)
  
  kw <- kruskal.test(score ~ condition, data = df)
  
  cat("\nPhase:", phase_name, "\n")
  cat("Kruskal-Wallis p-value:", kw$p.value, "\n")
  
  if (kw$p.value < 0.05) {
    cat("Significant difference found, running pairwise Wilcoxon tests:\n")
    pw <- pairwise.wilcox.test(df$score, df$condition, p.adjust.method = "BH")
    print(pw$p.value)
  } 
}

# Run tests for each phase
for (ph in levels(long_condition_data$phase)) {
  test_phase(ph)
}

library(readr)
library(dplyr)
library(tidyr)
library(stringr)
library(purrr)

# Step 1: Reshape likert columns to long format
condition_data <- data %>%
  select(condition_order, matches("likert_.*_condition\\d+"))

long_condition_data <- condition_data %>%
  pivot_longer(
    cols = -condition_order,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    condition = as.integer(condition),
    phase = factor(phase, levels = c("pre", "mid", "post"))
  )

# Step 2: Map each condition to its lesson using condition_order
long_condition_data <- long_condition_data %>%
  mutate(
    lesson = map2_int(condition_order, condition, ~ {
      # Split the condition_order string into numeric vector
      lesson_vector <- as.integer(str_split(.x, ",", simplify = TRUE))
      # Return the lesson index for that condition
      lesson_vector[.y + 1]  # condition is 0-based index in condition_order
    })
  )


# Step 3: Function to test within one lesson
test_lesson_phases <- function(lesson_id) {
  cat("\n\nLesson:", lesson_id, "")
  
  lesson_data <- long_condition_data %>% filter(lesson == lesson_id)
  
  for (ph in levels(lesson_data$phase)) {
    df <- lesson_data %>% filter(phase == ph)
    
    kw <- kruskal.test(score ~ factor(condition), data = df)
    
    cat("\nPhase:", ph, "\n")
    cat("Kruskal-Wallis p-value:", kw$p.value, "")
    
    if (kw$p.value < 0.05) {
      cat("Significant difference found, running pairwise Wilcoxon tests:\n")
      pw <- pairwise.wilcox.test(df$score, factor(df$condition), p.adjust.method = "BH")
      print(pw$p.value)
    } 
  }
}

print("\n\nIs there a difference between conditions for pre, mid or post values in certain lessons?")
# Step 4: Run the test for each lesson (0 to 3)
for (les in 0:3) {
  test_lesson_phases(les)
}
```

## Certainty per Lesson

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Extract lesson-related likert columns
lesson_data <- data %>% select(matches("likert_.*_lesson"))

# Reshape to long format
long_lesson_data <- lesson_data %>%
  pivot_longer(cols = everything(),
               names_to = c("phase", "lesson"),
               names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
               values_to = "score") %>%
  mutate(phase = factor(phase, levels = c("pre", "mid", "post")),
         lesson = paste0("lesson ", lesson))

# Summarize by phase and lesson
summary_data <- long_lesson_data %>%
  group_by(phase, lesson) %>%
  summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")

# Line plot: 4 lines (lessons), x-axis is phase
ggplot(summary_data, aes(x = phase, y = mean_score, group = lesson, color = lesson)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  labs(title = "Mean Likert Scores per lesson Over Phases",
       x = "Phase",
       y = "Mean Likert Score",
       color = "lesson") +
  theme_minimal()

library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggplot2)
library(ggsignif)

lesson_data <- data %>% select(matches("likert_.*_lesson"))

long_lesson_data <- lesson_data %>%
  pivot_longer(
    cols = everything(),
    names_to = c("phase", "lesson"),
    names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    lesson = factor(paste0("Lesson ", lesson), levels = paste0("Lesson ", 1:4))
  )

# Calculate means for labels
means <- long_lesson_data %>%
  group_by(lesson, phase) %>%
  summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")

ggplot(long_lesson_data, aes(x = phase, y = score, fill = phase)) +
  geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
  geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.8),
              size = 2, alpha = 0.7) +
  geom_text(data = means, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
            position = position_dodge(width = 0.8), inherit.aes = FALSE,
            size = 3, fontface = "bold", color = "black") +
  geom_signif(
    comparisons = list(c("post")),
    map_signif_level = TRUE
  ) +
  facet_wrap(~ lesson, nrow = 1) +  # All lessons in one row, visually grouped by facets
  scale_fill_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
  scale_color_manual(values = c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")) +
  labs(
    title = "Likert Scores by Phase and Lesson",
    x = "Phase",
    y = "Likert Score",
    fill = "Phase",
    color = "Phase"
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(size = 12, face = "bold"), # facet label style
    legend.position = "top"
  )

library(ggplot2)
library(ggsignif)

# Your long_lesson_data is already fine

# Define lesson comparisons (pairwise between all 4 lessons)
lesson_comparisons <- combn(levels(long_lesson_data$lesson), 2, simplify = FALSE)

# Plot: X = lesson, facet by phase
ggplot(long_lesson_data, aes(x = lesson, y = score, fill = lesson)) +
  geom_boxplot(alpha = 0.5, outlier.shape = NA) +
  geom_jitter(aes(color = lesson), position = position_jitter(width = 0.15), size = 2, alpha = 0.7) +
  geom_signif(
    comparisons = lesson_comparisons,
    step_increase = 0.2,
    map_signif_level=c("***"=0.001,"**"=0.01, "*"=0.05, " "=2),
    test = "wilcox.test"
  ) +
  facet_wrap(~ phase, nrow = 1) +
  scale_fill_brewer(palette = "Set2") +
  scale_color_brewer(palette = "Set2") +
  labs(
    title = "Likert Scores Across Lessons (by Phase)",
    x = "Lesson",
    y = "Likert Score",
    fill = "Lesson",
    color = "Lesson"
  ) +
  theme_minimal() +
  theme(
    strip.text = element_text(size = 12, face = "bold"),
    legend.position = "top"
  )


```

## Certainty per Lesson - ANOVA

**Finding** Kruskal-Wallis shows there is significant difference between lessons in their pre scores (p \< 0.05) and post scores (p \< 0.05). Pairwise Wilcoxon test shows Lesson 4 is significantly different from Lessons 1 and 2 in pre scores (p \< 0.05) and Lesson 1 in post scores (p \< 0.05).

```{r}

library(readr)
library(dplyr)
library(tidyr)

# Select lesson-related Likert columns
lesson_data <- data %>% select(matches("likert_.*_lesson"))

# Reshape to long format: id, phase, lesson, score
long_lesson_data <- lesson_data %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "lesson"),
    names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    lesson = paste0("Lesson ", lesson)
  )

# Run Kruskal-Wallis test for each phase comparing lessons
kruskal_phase <- long_lesson_data %>%
  group_by(phase) %>%
  summarise(
    kruskal_p = kruskal.test(score ~ lesson)$p.value
  )

print(kruskal_phase)

# Only for phases with significant Kruskal-Wallis results
significant_phases <- kruskal_phase %>% filter(kruskal_p < 0.05) %>% pull(phase)

for (ph in significant_phases) {
  cat("\n=== Phase:", ph, "===\n")
  df <- long_lesson_data %>% filter(phase == ph)
  
  # Pairwise Wilcoxon
  pairwise_result <- pairwise.wilcox.test(df$score, df$lesson, p.adjust.method = "BH")
  
  print(pairwise_result$p.value)
}

```

## Certainty Deltas per Condition

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Extract and reshape condition-related likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))

long_condition_data <- condition_data %>%
  pivot_longer(
    cols = everything(),
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = paste0("condition ", condition)
  )

# Compute mean scores per phase and condition
mean_scores <- long_condition_data %>%
  group_by(condition, phase) %>%
  summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")

# Join with pre scores to compute difference
pre_scores <- mean_scores %>% filter(phase == "pre") %>%
  rename(pre_score = mean_score) %>%
  select(condition, pre_score)

diff_data <- mean_scores %>%
  left_join(pre_scores, by = "condition") %>%
  mutate(diff_from_pre = mean_score - pre_score)

# Plot differences, with pre as baseline (0), mid/post as deltas
ggplot(diff_data, aes(x = phase, y = diff_from_pre, group = condition, color = condition)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  labs(
    title = "Mean Likert Score Changes Relative to Pre",
    x = "Phase",
    y = "Score Difference from Pre",
    color = "condition"
  ) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  theme_minimal()
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Extract and reshape condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))

# Long format with phase and condition
long_condition_data <- condition_data %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = paste0("condition ", condition)
  )

# Calculate differences: mid-pre, post-pre, and post-mid
pivoted <- long_condition_data %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff_mid = mid - pre,
    diff_post = post - pre,
    diff_mid_post = post - mid
  ) %>%
  pivot_longer(
    cols = c(diff_mid, diff_post, diff_mid_post),
    names_to = "phase",
    values_to = "diff_from_pre"
  ) %>%
  mutate(
    phase = recode(phase,
                   diff_mid = "mid-pre",
                   diff_post = "post-pre",
                   diff_mid_post = "post-mid"),
    phase = factor(phase, levels = c("mid-pre", "post-pre", "post-mid"))
  )

# Calculate means for labels/points
means <- pivoted %>%
  group_by(condition, phase) %>%
  summarise(mean_val = mean(diff_from_pre, na.rm = TRUE), .groups = "drop")

# Plot all conditions side by side, phase as fill dodge, with mean points
ggplot(pivoted, aes(x = condition, y = diff_from_pre, fill = phase)) +
  geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
  geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8), alpha = 0.7, size = 2) +
  geom_point(data = means, aes(x = condition, y = mean_val, group = phase),
             position = position_dodge(width = 0.8), color = "black", size = 3, shape = 18) +
  scale_y_continuous(limits = c(-15, 15)) +
  labs(
    title = "Differences between Phases per condition",
    x = "condition",
    y = "Score Difference",
    fill = "Phase Comparison",
    color = "Phase Comparison"
  ) +
  theme_minimal() +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray")

```

## Certainty Deltas per Condition - ANOVA

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Extract and reshape only condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))

# Long format with phase and condition
long_condition_data <- condition_data %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = paste0("condition ", condition)
  )

# Calculate differences from pre per individual and condition
delta_data <- long_condition_data %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff_mid = mid - pre,
    diff_post = post - pre
  ) %>%
  pivot_longer(
    cols = c(diff_mid, diff_post),
    names_to = "phase",
    values_to = "diff_from_pre"
  ) %>%
  mutate(
    phase = recode(phase, diff_mid = "mid", diff_post = "post"),
    phase = factor(phase, levels = c("mid", "post"))
  )

# Perform Shapiro-Wilk test by condition and phase
normality_tests <- delta_data %>%
  group_by(condition, phase) %>%
  summarise(
    p_value = shapiro.test(diff_from_pre)$p.value,
    .groups = "drop"
  )


print("Shapiro-Wilk Normality Test (p < 0.05 = not normal):")
print(normality_tests)
library(readr)
library(dplyr)
library(tidyr)
# Select condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_condition"))

# Reshape to long format: id, phase, condition, score
long_condition_data <- condition_data %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = paste0("condition ", condition)
  )

# Run Kruskal-Wallis test for each phase comparing conditions
kruskal_phase <- long_condition_data %>%
  group_by(phase) %>%
  summarise(
    kruskal_p = kruskal.test(score ~ condition)$p.value
  )

print(kruskal_phase)

```

## Certainty Deltas per Lesson

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Extract and reshape lesson-related likert columns
condition_data <- data %>% select(matches("likert_.*_lesson"))

long_lesson_data <- condition_data %>%
  pivot_longer(
    cols = everything(),
    names_to = c("phase", "lesson"),
    names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    lesson = paste0("condition ", lesson)
  )

# Compute mean scores per phase and lesson
mean_scores <- long_lesson_data %>%
  group_by(lesson, phase) %>%
  summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")

# Join with pre scores to compute difference
pre_scores <- mean_scores %>% filter(phase == "pre") %>%
  rename(pre_score = mean_score) %>%
  select(lesson, pre_score)

diff_data <- mean_scores %>%
  left_join(pre_scores, by = "lesson") %>%
  mutate(diff_from_pre = mean_score - pre_score)

# Plot differences, with pre as baseline (0), mid/post as deltas
ggplot(diff_data, aes(x = phase, y = diff_from_pre, group = lesson, color = lesson)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  labs(
    title = "Mean Likert Score Changes Relative to Pre",
    x = "Phase",
    y = "Score Difference from Pre",
    color = "Lesson"
  ) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  theme_minimal()
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)


# Extract and reshape condition-related Likert columns
condition_data <- data %>% select(matches("likert_.*_lesson"))

# Long format with phase and condition
long_lesson_data <- condition_data %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "lesson"),
    names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    condition = paste0("Lesson ", lesson)
  )

# Calculate differences: mid-pre, post-pre, and post-mid
pivoted <- long_lesson_data %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff_mid = mid - pre,
    diff_post = post - pre,
    diff_mid_post = post - mid
  ) %>%
  pivot_longer(
    cols = c(diff_mid, diff_post, diff_mid_post),
    names_to = "phase",
    values_to = "diff_from_pre"
  ) %>%
  mutate(
    phase = recode(phase,
                   diff_mid = "mid-pre",
                   diff_post = "post-pre",
                   diff_mid_post = "post-mid"),
    phase = factor(phase, levels = c("mid-pre", "post-pre", "post-mid"))
  )

# Calculate means for labels/points
means <- pivoted %>%
  group_by(condition, phase) %>%
  summarise(mean_val = mean(diff_from_pre, na.rm = TRUE), .groups = "drop")

# Plot all conditions side by side, phase as fill dodge, with mean points
ggplot(pivoted, aes(x = condition, y = diff_from_pre, fill = phase)) +
  geom_boxplot(position = position_dodge(width = 0.8), alpha = 0.5, outlier.shape = NA) +
  geom_jitter(aes(color = phase), position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8), alpha = 0.7, size = 2) +
  geom_point(data = means, aes(x = condition, y = mean_val, group = phase),
             position = position_dodge(width = 0.8), color = "black", size = 3, shape = 18) +
  scale_y_continuous(limits = c(-15, 15)) +
  labs(
    title = "Differences between Phases per Lesson",
    x = "Lesson",
    y = "Score Difference",
    fill = "Phase Comparison",
    color = "Phase Comparison"
  ) +
  theme_minimal() +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray")

```

## Certainty Deltas per Lesson - ANOVA

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Extract and reshape only lesson-related Likert columns
lesson_data <- data %>% select(matches("likert_.*_lesson"))

# Long format with phase and lesson
long_lesson_data <- lesson_data %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "lesson"),
    names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    lesson = paste0("Lesson ", lesson)
  )

# Calculate differences from pre per individual and lesson
delta_data <- long_lesson_data %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff_mid = mid - pre,
    diff_post = post - pre
  ) %>%
  pivot_longer(
    cols = c(diff_mid, diff_post),
    names_to = "phase",
    values_to = "diff_from_pre"
  ) %>%
  mutate(
    phase = recode(phase, diff_mid = "mid", diff_post = "post"),
    phase = factor(phase, levels = c("mid", "post"))
  )

# Perform Shapiro-Wilk test by lesson and phase
normality_tests <- delta_data %>%
  group_by(lesson, phase) %>%
  summarise(
    p_value = shapiro.test(diff_from_pre)$p.value,
    .groups = "drop"
  )


print("Shapiro-Wilk Normality Test (p < 0.05 = not normal):")
print(normality_tests)

```

## Certainty for all Lessons and conditions

```{r}

library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stringr)

lesson_data <- data %>% 
  select(matches("likert_.*_lesson")) %>%
  mutate(id = row_number())  # Add this line

long_lesson_data <- lesson_data %>%
  pivot_longer(
    cols = -id,  # Exclude 'id' from pivoting
    names_to = c("phase", "lesson"),
    names_pattern = "likert_(pre|mid|post)_lesson(\\d+)",
    values_to = "score"
  ) %>%
  mutate(
    phase = factor(phase, levels = c("pre", "mid", "post")),
    lesson = paste0("Lesson ", lesson)
  )

# Split 'condition_order' into separate rows
condition_order_long <- data %>%
  mutate(id = row_number()) %>%
  separate(condition_order, into = paste0("lesson_", 1:4), sep = ",", convert = TRUE) %>%
  pivot_longer(
    cols = starts_with("lesson_"),
    names_to = "lesson",
    names_pattern = "lesson_(\\d+)",
    values_to = "condition_index"
  ) %>%
  mutate(
    lesson = paste0("Lesson ", as.integer(lesson)),
    condition = paste0("condition ", condition_index + 1)
  ) %>%
  select(id, lesson, condition)

# ✅ JOIN to get full combined data
combined <- long_lesson_data %>%
  left_join(condition_order_long, by = c("id", "lesson")) %>%
  mutate(condition_lesson = paste(condition, lesson, sep = " - "))

# Join lesson scores with their corresponding condition (via condition_order)
combined <- combined %>%
  mutate(condition_lesson = paste(condition, lesson, sep = " - "))


# Step 4: Get means for label
means <- combined %>%
  group_by(condition_lesson, phase) %>%
  summarise(mean_score = mean(score, na.rm = TRUE), .groups = "drop")

# Step 5: Plot
# Define color scales
fill_colors <- c("pre" = "#a6cee3", "mid" = "#1f78b4", "post" = "#b2df8a")
text_color <- "black"

# Function to generate a plot for a specific phase
plot_by_phase <- function(phase_input) {
  # Filter combined data and means for the given phase
  plot_data <- combined %>% filter(phase == phase_input)
  phase_means <- means %>% filter(phase == phase_input)
  
  ggplot(plot_data, aes(x = phase, y = score, fill = phase)) +
    geom_boxplot(alpha = 0.5, outlier.shape = NA) +
    geom_jitter(aes(color = phase), position = position_jitter(width = 0.15),
                size = 1.5, alpha = 0.7) +
    geom_text(data = phase_means, aes(x = phase, y = mean_score + 0.15, label = round(mean_score, 2)),
              inherit.aes = FALSE,
              size = 2.5, fontface = "bold", color = text_color) +
    facet_wrap(~ condition_lesson, ncol = 4) +
    scale_fill_manual(values = fill_colors) +
    scale_color_manual(values = fill_colors) +
    labs(
      title = paste("Likert Scores for", tools::toTitleCase(phase_input), "Phase"),
      x = NULL,
      y = "Likert Score",
      fill = "Phase",
      color = "Phase"
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_blank(),
      axis.ticks.x = element_blank(),
      strip.text = element_text(size = 10, face = "bold"),
      legend.position = "none"
    )
}

# Now generate plots
plot_pre <- plot_by_phase("pre")
plot_mid <- plot_by_phase("mid")
plot_post <- plot_by_phase("post")

# Print them one after another (in an R script or notebook)
print(plot_pre)
print(plot_mid)
print(plot_post)


library(ggplot2)
library(tidyr)
library(dplyr)
library(patchwork)

means_heatmap <- means %>%
  separate(condition_lesson, into = c("condition", "lesson"), sep = " - ") %>%
  mutate(
    condition = factor(str_remove(condition, "condition "), levels = c("1", "2", "3", "4")),
    lesson = factor(str_remove(lesson, "Lesson "), levels = c("1", "2", "3", "4")),
    phase = factor(phase, levels = c("pre", "mid", "post"))
  ) %>%
  complete(phase, condition, lesson)

min_score <- floor(min(means_heatmap$mean_score, na.rm = TRUE))
max_score <- ceiling(max(means_heatmap$mean_score, na.rm = TRUE))

plot_heatmap <- function(phase_name, show_x = TRUE, show_y = TRUE) {
  ggplot(means_heatmap %>% filter(phase == phase_name), aes(x = lesson, y = condition, fill = mean_score)) +
    geom_tile(color = "white") +
    geom_label(aes(label = round(mean_score, 2)), 
               fill = alpha("white", 0.5),   # faint white background
               color = "black", 
               size = 2,                    # smaller text size
               label.size = 0) +            # remove label border
    scale_fill_viridis_c(option = "C", direction = -1, limits = c(min_score, max_score), na.value = "grey90") +
    labs(title = paste(toupper(phase_name), "Phase"),
         x = ifelse(show_x, "Lesson", ""),
         y = ifelse(show_y, "condition", ""),
         fill = "Mean Score") +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
      axis.text.y = element_text(size = 12),
      plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
      axis.title.x = element_text(size = 13, face = "bold"),
      axis.title.y = element_text(size = 13, face = "bold")
    )
}

# Show y-axis labels only on leftmost plot, x-axis only on bottom plots
heatmap_pre <- plot_heatmap("pre", show_x = FALSE, show_y = TRUE)
heatmap_mid <- plot_heatmap("mid", show_x = FALSE, show_y = FALSE)
heatmap_post <- plot_heatmap("post", show_x = TRUE, show_y = FALSE)

combined_heatmaps <- heatmap_pre + heatmap_mid + heatmap_post + 
  plot_layout(ncol = 3, guides = "collect") & 
  theme(legend.position = "right")

print(combined_heatmaps)

```

## Certainty deltas for all Lessons and conditions

```{r}
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)

# Spread phases wide
means_wide <- means_heatmap %>%
  select(condition, lesson, phase, mean_score) %>%
  pivot_wider(names_from = phase, values_from = mean_score)

# Calculate deltas as later phase minus earlier phase
deltas <- means_wide %>%
  mutate(
    mid_pre = mid - pre,        # Change after Inoculation
    post_pre = post - pre,      # Change after Inoculation and Strong attack
    post_mid = post - mid       # Change after Strong attack
  ) %>%
  select(condition, lesson, mid_pre, post_pre, post_mid) %>%
  pivot_longer(cols = c(mid_pre, post_pre, post_mid),
               names_to = "comparison",
               values_to = "delta")

# Named titles for the plots
comparison_titles <- c(
  mid_pre = "Change after\nInoculation",
  post_pre = "Change after\nInoculation and\nStrong attack",
  post_mid = "Change after\nStrong attack"
)

plot_delta_heatmap <- function(comp_name) {
  ggplot(deltas %>% filter(comparison == comp_name), aes(x = lesson, y = condition, fill = delta)) +
    geom_tile(color = "white") +
    geom_label(aes(label = round(delta, 2)), 
               fill = alpha("white", 0.5), 
               color = "black", 
               size = 2, 
               label.size = 0) +
    scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0,
                         limits = c(min(deltas$delta, na.rm = TRUE), max(deltas$delta, na.rm = TRUE)),
                         na.value = "grey90") +
    labs(
      title = comparison_titles[comp_name],
      x = ifelse(comp_name == "mid_pre", "Lesson", ""),
      y = ifelse(comp_name == "mid_pre", "condition", ""),
      fill = "Delta"
    ) +
    theme_minimal() +
    theme(
      axis.text.x = element_text(angle = 45, hjust = 1, size = 12),
      axis.text.y = element_text(size = 12),
      plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
      axis.title.x = element_text(size = 13, face = "bold"),
      axis.title.y = element_text(size = 13, face = "bold")
    )
}

heatmap_mid_pre <- plot_delta_heatmap("mid_pre")
heatmap_post_pre <- plot_delta_heatmap("post_pre")
heatmap_post_mid <- plot_delta_heatmap("post_mid")

combined_deltas <- heatmap_mid_pre +  heatmap_post_mid + heatmap_post_pre +
  plot_layout(ncol = 3, guides = "collect") & 
  theme(legend.position = "right")

print(combined_deltas)

library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
library(stringr)

# Assuming you have 'combined' dataframe with participant_id, condition, lesson, phase, score

# Pivot wider to get pre, mid, post scores per participant-condition-lesson
participant_wide <- combined %>%
  select(id, condition, lesson, phase, score) %>%
  pivot_wider(names_from = phase, values_from = score)

# Calculate participant-level deltas (later - earlier)
participant_deltas <- participant_wide %>%
  mutate(
    mid_pre = mid - pre,
    post_pre = post - pre,
    post_mid = post - mid
  ) %>%
  select(id, condition, lesson, mid_pre, post_pre, post_mid) %>%
  pivot_longer(cols = c(mid_pre, post_pre, post_mid),
               names_to = "comparison",
               values_to = "delta")

# Fix factors for plotting
participant_deltas <- participant_deltas %>%
  mutate(
    condition = factor(str_remove(condition, "condition "), levels = c("1", "2", "3", "4")),
    lesson = factor(str_remove(lesson, "Lesson "), levels = c("1", "2", "3", "4")),
    comparison = factor(comparison, levels = c("mid_pre", "post_pre", "post_mid"))
  )

# Named titles for the plots
comparison_titles <- c(
  mid_pre = "Change after\nInoculation",
  post_pre = "Change after\nInoculation and\nStrong attack",
  post_mid = "Change after\nStrong attack"
)

# Plotting function for boxplots + scatter
plot_delta_boxplots <- function(comp_name) {
  df <- participant_deltas %>% filter(comparison == comp_name)
  
  ggplot(df, aes(x = lesson, y = delta)) +
    geom_boxplot(outlier.shape = NA, fill = "#a6cee3", alpha = 0.5) +
    geom_jitter(width = 0.2, alpha = 0.7, size = 1.5, color = "#1f78b4") +
    facet_wrap(~condition, nrow = 4, ncol = 1, strip.position = "right") +
    ylim(-15, 15) +    # Fixed y-axis scale here
    labs(
      title = comparison_titles[comp_name],
      x = "Lesson",
      y = "Delta Score"
    ) +
    theme_minimal() +
    theme(
      strip.text.y = element_text(angle = 0, size = 12, face = "bold"),
      plot.title = element_text(size = 14, face = "bold", hjust = 0.5),
      axis.text.x = element_text(angle = 45, hjust = 1)
    )
}


# Generate plots
plot_mid_pre <- plot_delta_boxplots("mid_pre")
plot_post_pre <- plot_delta_boxplots("post_pre")
plot_post_mid <- plot_delta_boxplots("post_mid")

# Combine plots side by side with shared legend area (no legend here actually)
combined_boxplots <- plot_mid_pre + plot_post_pre + plot_post_mid + 
  plot_layout(ncol = 3, guides = "collect") & 
  theme(legend.position = "bottom")

# Show combined plot
print(combined_boxplots)



```

# Resistance to Persuasion (Certainty mid-post)

## ! All certainty deltas

```{r}
# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
  select(matches("likert_(mid|post)_condition\\d+")) %>%
  mutate(participant_id = row_number()) %>%
  pivot_longer(
    cols = -participant_id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff = post - mid,
    condition = case_when(
      condition == "1" ~ "Control",
      condition == "2" ~ "Reading",
      condition == "3" ~ "Writing",
      condition == "4" ~ "Chatbot",
      TRUE ~ condition
    )
  ) %>%
  select(participant_id, condition, diff) %>%
  pivot_wider(names_from = condition, values_from = diff)

# Step 2: View the result
print(all_certainty_deltas)

# Optional: Save to CSV
write.csv(all_certainty_deltas, "participant_differences_by_condition.csv", row.names = FALSE)

```

## !! Resistance to Persuasion per Condition

```{r}
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(ggpubr)

plot_data <- data %>%
  select(matches("likert_(mid|post)_condition\\d+")) %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff_value = post - mid,
    condition = case_when(
      condition == "1" ~ "Control",
      condition == "2" ~ "Reading",
      condition == "3" ~ "Writing",
      condition == "4" ~ "Chatbot",
      TRUE ~ condition  # serves as a failsafe
    ),
    # Convert condition to a factor with specified levels
    condition = factor(condition, levels = c("Control", "Reading", "Writing", "Chatbot"))
  )

# Remove outliers (per condition, 1.5 × IQR rule)
plot_data_clean <- plot_data %>%
  group_by(condition) %>%
  mutate(
    Q1 = quantile(diff_value, 0.25, na.rm = TRUE),
    Q3 = quantile(diff_value, 0.75, na.rm = TRUE),
    IQR = Q3 - Q1,
    lower_bound = Q1 - 1.5 * IQR,
    upper_bound = Q3 + 1.5 * IQR,
    is_outlier = diff_value < lower_bound | diff_value > upper_bound
  ) %>%
  filter(!is_outlier) %>%
  ungroup() %>%
  select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound, -is_outlier)

# Summary statistics (use cleaned data)
summary_stats_clean <- plot_data_clean %>%
  group_by(condition) %>%
  summarise(mean_val = mean(diff_value, na.rm = TRUE), .groups = "drop")

pvals <- data.frame(
  group1 = c("Control", "Reading", "Writing"),
  group2 = c("Chatbot", "Chatbot", "Chatbot"),
  p_value = c(0.001, 0.02, 0.02),  # replace with your actual p-values
  xstart = c(1, 2, 3),            # numeric x for groups; adjust if your factor levels differ
  xend = c(4, 4, 4),              # "Chatbot" assumed at position 4
  y = c(5, 6.8, 8.6)               # y positions for brackets, adjust based on your data range
)

pvals <- data.frame(
  group1 = c("Control"),
  group2 = c("Chatbot"),
  p_value = c(0.001),  # replace with your actual p-values
  xstart = c(1),            # numeric x for groups; adjust if your factor levels differ
  xend = c(4),              # "Chatbot" assumed at position 4
  y = c(5)               # y positions for brackets, adjust based on your data range
)


# Plot using cleaned data
ggplot(plot_data_clean, aes(x = condition, y = diff_value, fill = condition)) +
  geom_boxplot(alpha = 0.5, outlier.shape = NA) +
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = condition)) +
  geom_point(data = summary_stats_clean, aes(x = condition, y = mean_val),
             color = "black", size = 3, shape = 18) +
  geom_text(data = summary_stats_clean, aes(x = condition, y = mean_val, label = round(mean_val, 2)),
            vjust = -1, color = "black", size = 4) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  scale_y_continuous(breaks = pretty_breaks(n = 10)) +
  labs(
    title = "Change in certainty after strong attack",
    x = "Condition",
    y = "Certainty score change",
    fill = "Condition",
    color = "Condition"
  ) +
  theme_minimal() +

  # Add brackets as lines
  geom_segment(data = pvals, aes(x = xstart, xend = xend, y = y, yend = y),
               inherit.aes = FALSE) +
  geom_segment(data = pvals, aes(x = xstart, xend = xstart, y = y, yend = y - 0.3),
               inherit.aes = FALSE) +
  geom_segment(data = pvals, aes(x = xend, xend = xend, y = y, yend = y - 0.3),
               inherit.aes = FALSE) +

  # Add p-value labels, show "p < 0.05" if below threshold, else blank
  geom_text(
  data = pvals,
  aes(x = (xstart + xend) / 2, y = y + 0.5,
      label = case_when(
        p_value < 0.001 ~ "****",
        p_value < 0.005 ~ "***",
        p_value < 0.01  ~ "**",
        p_value < 0.05  ~ "*",
        TRUE ~ ""
      )),
  inherit.aes = FALSE,
  size = 3
)

ggplot(plot_data_clean, aes(x = condition, y = diff_value, fill = condition)) +
  geom_violin(trim = FALSE, alpha = 0.6, color = NA, adjust = 0.6) +  # Violin only
  geom_point(
    data = summary_stats_clean,
    aes(x = condition, y = mean_val),
    color = "black", size = 3, shape = 18
  ) +
  geom_text(
    data = summary_stats_clean,
    aes(x = condition, y = mean_val, label = round(mean_val, 2)),
    vjust = -1, color = "black", size = 4
  ) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
  labs(
    title = "Change in certainty after strong attack",
    x = "Condition",
    y = "Certainty score change",
    fill = "Condition"
  ) +
  theme_minimal() +
  geom_segment(
    data = pvals,
    aes(x = xstart, xend = xend, y = y, yend = y),
    inherit.aes = FALSE
  ) +
  geom_segment(
    data = pvals,
    aes(x = xstart, xend = xstart, y = y, yend = y - 0.3),
    inherit.aes = FALSE
  ) +
  geom_segment(
    data = pvals,
    aes(x = xend, xend = xend, y = y, yend = y - 0.3),
    inherit.aes = FALSE
  ) +
  geom_text(
    data = pvals,
    aes(
      x = (xstart + xend) / 2,
      y = y + 0.5,
      label = case_when(
        p_value < 0.001 ~ "****",
        p_value < 0.005 ~ "***",
        p_value < 0.01  ~ "**",
        p_value < 0.05  ~ "*",
        TRUE ~ ""
      )
    ),
    inherit.aes = FALSE,
    size = 4
  )

```

## !! Resistance to Persuasion per Condition - ANOVA

**Finding**: We performed a Shapiro-Wilk normality test, which determined that the certainty scores are not normally distributed in either of the conditions (p \< 0.01). Then, we performed Wilcoxon pairwise tests to compare the Chatbot condition to each of the three other condition. Talking to Forty the chatbot made participants significantly more resistant to a strong counter-attitudinal message than the writing task (p = .02, r =.38), the reading task (p = .02, r = .34) and control condition (p = .02, r = .33).

```{r}
library(dplyr)
library(rcompanion)

# ---- Normality check per condition ----
normality_results <- plot_data %>%
  group_by(condition) %>%
  summarise(
    shapiro_p = ifelse(length(diff_value) >= 3,  # Shapiro test requires at least 3 obs
                       shapiro.test(diff_value)$p.value,
                       NA_real_)
  )

print(normality_results)


# ---- Outlier detection and removal ----
plot_data_with_flags <- plot_data %>%
  group_by(condition) %>%
  mutate(
    Q1 = quantile(diff_value, 0.25, na.rm = TRUE),
    Q3 = quantile(diff_value, 0.75, na.rm = TRUE),
    IQR = Q3 - Q1,
    lower_bound = Q1 - 1.5 * IQR,
    upper_bound = Q3 + 1.5 * IQR,
    is_outlier = diff_value < lower_bound | diff_value > upper_bound
  ) %>%
  ungroup()

print(table(plot_data_with_flags$is_outlier, plot_data_with_flags$condition))

plot_data_clean <- plot_data_with_flags %>%
  filter(!is_outlier) %>%
  select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound, -is_outlier)


# ---- Compute all pairwise comparisons with effect sizes ----
pairwise_comparisons <- combn(unique(plot_data_clean$condition), 2, simplify = FALSE)

compute_pairwise_wilcox <- function(group1, group2, data) {
  subset <- data %>% filter(condition %in% c(group1, group2))
  
  subset <- subset %>%
    mutate(group_numeric = ifelse(condition == group1, 1, 2))
  
   test <- wilcox.test(diff_value ~ group_numeric,
                       data = subset, 
                       exact = FALSE, 
                       p.adjust.method = 'none',
                       alternative = "less")

  print(test)
  effect_size_r <- wilcoxonR(x = subset$diff_value, g = subset$group_numeric)
  
  tibble(
    group1 = group1,
    group2 = group2,
    p_value_uncorrected = test$p.value,
    effect_size_r = effect_size_r
  )
}

effect_size_results <- bind_rows(
  lapply(pairwise_comparisons, function(g) {
    compute_pairwise_wilcox(g[1], g[2], plot_data_clean)
  })
)

# ---- Now, adjust p-values only for pairs involving the NEW condition (e.g., "Chatbot") ----
new_condition <- "Chatbot"

# Filter rows with Chatbot involved
effect_size_new <- effect_size_results %>%
  filter(group1 == new_condition | group2 == new_condition) %>%
  mutate(
    p_adj_bonf_subset = p.adjust(p_value_uncorrected, method = "bonferroni"),
    p_adj_bh_subset = p.adjust(p_value_uncorrected, method = "BH")
  )

# Print full effect size results for reference
print(effect_size_results)

cat("\nAdjusted p-values ONLY for comparisons involving", new_condition, ":\n")
print(effect_size_new)

```

## Resistance to Persuasion per Lesson (all conditions)

```{r}
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(ggpubr)

plot_data <- data %>%
  select(matches("likert_(mid|post)_lesson\\d+")) %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "lesson"),
    names_pattern = "likert_(mid|post)_lesson(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff_value = post - mid,
    lesson = case_when(
      lesson == "1" ~ "Exercise and\nMental Health",
      lesson == "2" ~ "Binge Drinking",
      lesson == "3" ~ "Protecting Nature",
      lesson == "4" ~ "Dental Hygiene",
      TRUE ~ lesson  # serves as a failsafe
    ),
    # Convert lesson to a factor with specified levels
    lesson = factor(lesson, levels = c("Exercise and\nMental Health", "Binge Drinking", "Protecting Nature", "Dental Hygiene"))
  )

# Remove outliers (per condition, 1.5 × IQR rule)
plot_data_clean <- plot_data %>%
  group_by(lesson) %>%
  mutate(
    Q1 = quantile(diff_value, 0.25, na.rm = TRUE),
    Q3 = quantile(diff_value, 0.75, na.rm = TRUE),
    IQR = Q3 - Q1,
    lower_bound = Q1 - 1.5 * IQR,
    upper_bound = Q3 + 1.5 * IQR,
    is_outlier = diff_value < lower_bound | diff_value > upper_bound
  ) %>%
  filter(!is_outlier) %>%
  ungroup() %>%
  select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound, -is_outlier)

# Summary statistics (use cleaned data)
summary_stats_clean <- plot_data_clean %>%
  group_by(lesson) %>%
  summarise(mean_val = mean(diff_value, na.rm = TRUE), .groups = "drop")

pvals <- data.frame(
  group1 = c("Control", "Reading", "Writing"),
  group2 = c("Chatbot", "Chatbot", "Chatbot"),
  p_value = c(0.001, 0.02, 0.02),  # replace with your actual p-values
  xstart = c(1, 2, 3),            # numeric x for groups; adjust if your factor levels differ
  xend = c(4, 4, 4),              # "Chatbot" assumed at position 4
  y = c(5, 6.8, 8.6)               # y positions for brackets, adjust based on your data range
)

pvals <- data.frame(
  group1 = c("Control"),
  group2 = c("Chatbot"),
  p_value = c(0.001),  # replace with your actual p-values
  xstart = c(1),            # numeric x for groups; adjust if your factor levels differ
  xend = c(4),              # "Chatbot" assumed at position 4
  y = c(5)               # y positions for brackets, adjust based on your data range
)


# Plot using cleaned data
ggplot(plot_data_clean, aes(x = lesson, y = diff_value, fill = lesson)) +
  geom_boxplot(alpha = 0.5, outlier.shape = NA) +
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = lesson)) +
  geom_point(data = summary_stats_clean, aes(x = lesson, y = mean_val),
             color = "black", size = 3, shape = 18) +
  geom_text(data = summary_stats_clean, aes(x = lesson, y = mean_val, label = round(mean_val, 2)),
            vjust = -1, color = "black", size = 4) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  scale_y_continuous(breaks = pretty_breaks(n = 10)) +
  labs(
    title = "Change in certainty after strong attack",
    x = "Lesson",
    y = "Certainty score change",
    fill = "Lesson",
    color = "Lesson"
  ) +
  theme_minimal() +

  # Add brackets as lines
  geom_segment(data = pvals, aes(x = xstart, xend = xend, y = y, yend = y),
               inherit.aes = FALSE) +
  geom_segment(data = pvals, aes(x = xstart, xend = xstart, y = y, yend = y - 0.3),
               inherit.aes = FALSE) +
  geom_segment(data = pvals, aes(x = xend, xend = xend, y = y, yend = y - 0.3),
               inherit.aes = FALSE) +

  # Add p-value labels, show "p < 0.05" if below threshold, else blank
  geom_text(
  data = pvals,
  aes(x = (xstart + xend) / 2, y = y + 0.5,
      label = case_when(
        p_value < 0.001 ~ "****",
        p_value < 0.005 ~ "***",
        p_value < 0.01  ~ "**",
        p_value < 0.05  ~ "*",
        TRUE ~ ""
      )),
  inherit.aes = FALSE,
  size = 3
)

ggplot(plot_data_clean, aes(x = lesson, y = diff_value, fill = lesson)) +
  geom_violin(trim = FALSE, alpha = 0.6, color = NA, adjust = 0.6) +  # Violin only
  geom_point(
    data = summary_stats_clean,
    aes(x = lesson, y = mean_val),
    color = "black", size = 3, shape = 18
  ) +
  geom_text(
    data = summary_stats_clean,
    aes(x = lesson, y = mean_val, label = round(mean_val, 2)),
    vjust = -1, color = "black", size = 4
  ) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
  labs(
    title = "Change in certainty after strong attack",
    x = "Lesson",
    y = "Certainty score change",
    fill = "Lesson"
  ) +
  theme_minimal() +
  geom_segment(
    data = pvals,
    aes(x = xstart, xend = xend, y = y, yend = y),
    inherit.aes = FALSE
  ) +
  geom_segment(
    data = pvals,
    aes(x = xstart, xend = xstart, y = y, yend = y - 0.3),
    inherit.aes = FALSE
  ) +
  geom_segment(
    data = pvals,
    aes(x = xend, xend = xend, y = y, yend = y - 0.3),
    inherit.aes = FALSE
  ) +
  geom_text(
    data = pvals,
    aes(
      x = (xstart + xend) / 2,
      y = y + 0.5,
      label = case_when(
        p_value < 0.001 ~ "****",
        p_value < 0.005 ~ "***",
        p_value < 0.01  ~ "**",
        p_value < 0.05  ~ "*",
        TRUE ~ ""
      )
    ),
    inherit.aes = FALSE,
    size = 4
  )

```

## Resistance to Persuasion per Lesson (all conditions) - ANOVA

```{r}
library(dplyr)
library(rcompanion)

# ---- Normality check per lesson ----
normality_results <- plot_data %>%
  group_by(lesson) %>%
  summarise(
    shapiro_p = ifelse(length(diff_value) >= 3,  # Shapiro test requires at least 3 obs
                       shapiro.test(diff_value)$p.value,
                       NA_real_)
  )

print(normality_results)


# ---- Outlier detection and removal ----
plot_data_with_flags <- plot_data %>%
  group_by(lesson) %>%
  mutate(
    Q1 = quantile(diff_value, 0.25, na.rm = TRUE),
    Q3 = quantile(diff_value, 0.75, na.rm = TRUE),
    IQR = Q3 - Q1,
    lower_bound = Q1 - 1.5 * IQR,
    upper_bound = Q3 + 1.5 * IQR,
    is_outlier = diff_value < lower_bound | diff_value > upper_bound
  ) %>%
  ungroup()

print(table(plot_data_with_flags$is_outlier, plot_data_with_flags$lesson))

plot_data_clean <- plot_data_with_flags %>%
  filter(!is_outlier) %>%
  select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound, -is_outlier)


# ---- Compute all pairwise comparisons with effect sizes ----
pairwise_comparisons <- combn(unique(plot_data_clean$lesson), 2, simplify = FALSE)

compute_pairwise_wilcox <- function(group1, group2, data) {
  subset <- data %>% filter(lesson %in% c(group1, group2))
  
  subset <- subset %>%
    mutate(group_numeric = ifelse(lesson == group1, 1, 2))
  
   test <- wilcox.test(diff_value ~ group_numeric,
                       data = subset, 
                       exact = FALSE, 
                       p.adjust.method = 'none',
                       alternative = "less")

  print(test)
  effect_size_r <- wilcoxonR(x = subset$diff_value, g = subset$group_numeric)
  
  tibble(
    group1 = group1,
    group2 = group2,
    p_value_uncorrected = test$p.value,
    effect_size_r = effect_size_r
  )
}

effect_size_results <- bind_rows(
  lapply(pairwise_comparisons, function(g) {
    compute_pairwise_wilcox(g[1], g[2], plot_data_clean)
  })
)

# ---- Now, adjust p-values only for pairs involving the NEW lesson (e.g., "Chatbot") ----
new_lesson <- "Chatbot"

# Filter rows with Chatbot involved
effect_size_new <- effect_size_results %>%
  filter(group1 == new_lesson | group2 == new_lesson) %>%
  mutate(
    p_adj_bonf_subset = p.adjust(p_value_uncorrected, method = "bonferroni"),
    p_adj_bh_subset = p.adjust(p_value_uncorrected, method = "BH")
  )

# Print full effect size results for reference
print(effect_size_results)

cat("\nAdjusted p-values ONLY for comparisons involving", new_lesson, ":\n")
print(effect_size_new)

```

# Overall Change in Certainty

## Per Condition

```{r}
library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(ggpubr)

# Data wrangling
plot_data <- data %>%
  select(matches("likert_(pre|post)_condition\\d+")) %>%
  mutate(id = row_number()) %>%
  pivot_longer(
    cols = -id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff_value = post - pre,
    condition = recode(condition,
      "1" = "Control",
      "2" = "Reading",
      "3" = "Writing",
      "4" = "Chatbot"
    ),
    condition = factor(condition, levels = c("Control", "Reading", "Writing", "Chatbot"))
  )

# Remove outliers (per condition, 1.5 × IQR rule)
plot_data_clean <- plot_data %>%
  group_by(condition) %>%
  mutate(
    Q1 = quantile(diff_value, 0.25, na.rm = TRUE),
    Q3 = quantile(diff_value, 0.75, na.rm = TRUE),
    IQR = Q3 - Q1,
    lower_bound = Q1 - 1.5 * IQR,
    upper_bound = Q3 + 1.5 * IQR,
    is_outlier = diff_value < lower_bound | diff_value > upper_bound
  ) %>%
  filter(!is_outlier) %>%
  ungroup() %>%
  select(-Q1, -Q3, -IQR, -lower_bound, -upper_bound, -is_outlier)

# Summary statistics (use cleaned data)
summary_stats_clean <- plot_data_clean %>%
  group_by(condition) %>%
  summarise(mean_val = mean(diff_value, na.rm = TRUE), .groups = "drop")

pvals <- data.frame(
  group1 = c("Control", "Reading", "Writing"),
  group2 = c("Chatbot", "Chatbot", "Chatbot"),
  p_value = c(0.01, 0.01, 0.01),  # replace with your actual p-values
  xstart = c(1, 2, 3),            # numeric x for groups; adjust if your factor levels differ
  xend = c(4, 4, 4),              # "Chatbot" assumed at position 4
  y = c(5, 6.8, 8.6)               # y positions for brackets, adjust based on your data range
)


# Plot using cleaned data
ggplot(plot_data_clean, aes(x = condition, y = diff_value, fill = condition)) +
  geom_boxplot(alpha = 0.5, outlier.shape = NA) +
  geom_dotplot(binaxis = "y", stackdir = "center", dotsize = 0.5, aes(color = condition)) +
  geom_point(data = summary_stats_clean, aes(x = condition, y = mean_val),
             color = "black", size = 3, shape = 18) +
  geom_text(data = summary_stats_clean, aes(x = condition, y = mean_val, label = round(mean_val, 2)),
            vjust = -1, color = "black", size = 4) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "gray") +
  scale_y_continuous(breaks = pretty_breaks(n = 10)) +
  labs(
    title = "Change in certainty after strong attack",
    x = "Condition",
    y = "Certainty score change",
    fill = "Condition",
    color = "Condition"
  ) +
  theme_minimal() +

  # Add brackets as lines
  geom_segment(data = pvals, aes(x = xstart, xend = xend, y = y, yend = y),
               inherit.aes = FALSE) +
  geom_segment(data = pvals, aes(x = xstart, xend = xstart, y = y, yend = y - 0.3),
               inherit.aes = FALSE) +
  geom_segment(data = pvals, aes(x = xend, xend = xend, y = y, yend = y - 0.3),
               inherit.aes = FALSE) +

  # Add p-value labels, show "p < 0.05" if below threshold, else blank
  geom_text(
    data = pvals,
    aes(x = (xstart + xend) / 2, y = y + 0.5,
        label = ifelse(p_value < 0.05, "*", "")),
    inherit.aes = FALSE,
    size = 3
  )


```

# IMI Data

## Overall Mindfort IMI

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(likert)

# Load and clean
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Select all lesson columns (lesson1 to lesson4)
lesson_cols <- imi_clean %>% 
  select(participant_id, matches("^imi\\d+_lesson[1-4]$"))

# Convert from wide to long format
long_data <- lesson_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = c("question", "lesson"),
    names_pattern = "imi(\\d+)_lesson(\\d)",
    values_to = "response"
  ) %>%
  mutate(
    question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
    response = factor(as.numeric(response),
                      levels = 1:7,
                      labels = c("Extremely disagree", "Disagree", "Slightly disagree",
                                 "Neutral", "Slightly agree", "Agree", "Extremely agree"),
                      ordered = TRUE)
  )

# Convert to wide format for likert: rows = responses, columns = Q1–Q25
likert_ready <- long_data %>%
  arrange(participant_id, lesson) %>%
  group_by(participant_id, lesson) %>%
  mutate(response_id = cur_group_id()) %>%
  ungroup() %>%
  select(response_id, question, response) %>%
  pivot_wider(names_from = question, values_from = response) %>%
  select(paste0("Q", 1:25))  # Ensure correct order

# Convert to data.frame for likert package
likert_ready <- as.data.frame(likert_ready)

# Create and plot
likert_obj <- likert(likert_ready)
plot(likert_obj, group.order = paste0("Q", 1:25))


library(readr)
library(dplyr)
library(tidyr)

# Load and clean
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Select all lesson columns (lesson1 to lesson4)
lesson_cols <- imi_clean %>% 
  select(participant_id, matches("^imi\\d+_lesson[1-4]$"))

# Convert from wide to long format
long_data <- lesson_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = c("question", "lesson"),
    names_pattern = "imi(\\d+)_lesson(\\d)",
    values_to = "response"
  ) %>%
  mutate(
    # Q1 is imi0, so Qn = imin-1, now label nicely again for reporting
    question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE)
  )

# Reverse-score the right questions
reverse_questions <- c("Q8", "Q12", "Q14", "Q18", "Q20", "Q24")
long_data <- long_data %>%
  mutate(
    response_num = as.numeric(response),
    response_num = ifelse(question %in% reverse_questions, 8 - response_num, response_num)
  )

# Calculate and print means for each question
question_means <- long_data %>%
  group_by(question) %>%
  summarise(mean = mean(response_num, na.rm = TRUE)) %>%
  arrange(question)

print(question_means)

```

## IMI All Subscales per Condition

```{r}
# Load libraries
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)

# Define subscale map
subscale_map <- tribble(
  ~qn, ~subscale, ~reverse,
   1,     1,      FALSE,
   2,     1,      FALSE,
   3,     1,      TRUE,
   4,     1,      TRUE,
   5,     1,      FALSE,
   6,     1,      FALSE,
   7,     1,      FALSE,
   8,     2,      FALSE,
   9,     2,      FALSE,
  10,     2,      FALSE,
  11,     2,      FALSE,
  12,     2,      FALSE,
  13,     2,      TRUE,
  14,     3,      FALSE,
  15,     3,      TRUE,
  16,     3,      FALSE,
  17,     3,      FALSE,
  18,     3,      TRUE,
  19,     4,      FALSE,
  20,     4,      FALSE,
  21,     4,      FALSE,
  22,     4,      FALSE,
  23,     4,      FALSE,
  24,     4,      FALSE,
  25,     4,      FALSE
)

# Load and clean
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Prepare question columns
qnums_all <- 1:25
condition_cols <- imi_clean %>%
  select(
    participant_id,
    # Conditions 1–4
    all_of(unlist(lapply(1:4, function(c) paste0("imi", qnums_all, "_condition", c)))),
    # Condition 5 (_final)
    all_of(paste0("imi", qnums_all, "_final"))
  )

# Pivot to long format (handle both patterns)
long_data <- condition_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = "item",
    values_to = "response"
  ) %>%
  filter(!is.na(response)) %>%
  mutate(
    qn = as.integer(str_extract(item, "\\d+")),
    condition = case_when(
      grepl("_condition(\\d)", item) ~ str_extract(item, "(?<=_condition)\\d"),
      grepl("_final$", item) ~ "5"
    ),
    response_num = as.numeric(response)
  ) %>%
  left_join(subscale_map, by = "qn") %>%
  mutate(
    response_num = ifelse(reverse, 8 - response_num, response_num)
  )

# Average per subscale
subscale_scores <- long_data %>%
  group_by(participant_id, condition, subscale) %>%
  summarise(avg_score = mean(response_num, na.rm = TRUE), .groups = "drop")

# 1.5*IQR outlier removal
filtered_scores <- subscale_scores %>%
  group_by(condition, subscale) %>%
  mutate(
    Q1 = quantile(avg_score, 0.25),
    Q3 = quantile(avg_score, 0.75),
    IQR = Q3 - Q1,
    lower = Q1 - 1.5 * IQR,
    upper = Q3 + 1.5 * IQR
  ) %>%
  filter(avg_score >= lower & avg_score <= upper) %>%
  ungroup()

# Mean labels
mean_labels <- filtered_scores %>%
  group_by(condition, subscale) %>%
  summarise(mean_val = mean(avg_score), .groups = "drop")

# Define subscale names
subscale_names <- c(
  "1" = "Interest/Enjoyment",
  "2" = "Perceived Competence",
  "3" = "Effort/Importance",
  "4" = "Value/Usefulness"
)

# Apply factor labels
filtered_scores <- filtered_scores %>%
  mutate(subscale = factor(subscale, levels = names(subscale_names), labels = subscale_names))

mean_labels <- mean_labels %>%
  mutate(subscale = factor(subscale, levels = names(subscale_names), labels = subscale_names))

# Define all condition labels including "Final"
condition_labels <- c(
  "1" = "Control",
  "2" = "Reading",
  "3" = "Writing",
  "4" = "Chatbot",
  "5" = "Final"
)

# Apply descriptive labels
filtered_scores <- filtered_scores %>%
  mutate(condition_label = factor(condition, levels = names(condition_labels), labels = condition_labels))

mean_labels <- mean_labels %>%
  mutate(condition_label = factor(condition, levels = names(condition_labels), labels = condition_labels))

# Plot with all 5 conditions
ggplot(filtered_scores, aes(x = subscale, y = avg_score, fill = condition_label)) +
  geom_boxplot(outlier.shape = NA, position = position_dodge(width = 0.75)) +
  geom_jitter(aes(color = condition_label), size = 1.5, alpha = 0.5,
              position = position_jitterdodge(jitter.width = 0.1, dodge.width = 0.75)) +
  geom_text(
    data = mean_labels,
    aes(label = round(mean_val, 2), y = mean_val),
    position = position_dodge(width = 0.75),
    vjust = -0.5,
    size = 3,
    color = "black"
  ) +
  labs(
    title = "Average Subscale Scores by Condition (Outliers Removed)",
    x = "Subscale",
    y = "Average Score (Reversed where needed)",
    fill = "Condition",
    color = "Condition"
  ) +
  theme_minimal()

```

## IMI All Subscales per Condition - ANOVA

```{r}
# Load libraries
library(readr)
library(dplyr)
library(tidyr)
library(ggplot2)
library(stats)

# Define subscale map
subscale_map <- tribble(
  ~qn, ~subscale, ~reverse,
   1,     1,      FALSE,
   2,     1,      FALSE,
   3,     1,      TRUE,
   4,     1,      TRUE,
   5,     1,      FALSE,
   6,     1,      FALSE,
   7,     1,      FALSE,
   8,     2,      FALSE,
   9,     2,      FALSE,
  10,     2,      FALSE,
  11,     2,      FALSE,
  12,     2,      FALSE,
  13,     2,      TRUE,
  14,     3,      FALSE,
  15,     3,      TRUE,
  16,     3,      FALSE,
  17,     3,      FALSE,
  18,     3,      TRUE,
  19,     4,      FALSE,
  20,     4,      FALSE,
  21,     4,      FALSE,
  22,     4,      FALSE,
  23,     4,      FALSE,
  24,     4,      FALSE,
  25,     4,      FALSE
)

# Load data
imi <- read_csv("imi.csv", show_col_types = FALSE)

# Exclude problematic participant IDs
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Prepare column names
qnums_all <- 1:25
condition_1_4_cols <- paste0("imi", qnums_all, "_condition", rep(1:4, each = 25))
condition_5_cols <- paste0("imi", qnums_all, "_final")
all_imi_cols <- unique(c("participant_id", condition_1_4_cols, condition_5_cols))

# Select relevant columns
condition_cols <- imi_clean %>% select(any_of(all_imi_cols))

# Pivot longer to include both condition types
long_data <- condition_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = "item",
    values_to = "response"
  ) %>%
  filter(!is.na(response)) %>%
  mutate(
    qn = as.integer(str_extract(item, "\\d+")),
    condition = case_when(
      grepl("_condition(\\d)", item) ~ str_extract(item, "(?<=_condition)\\d"),
      grepl("_final$", item) ~ "5"
    ),
    response_num = as.numeric(response)
  ) %>%
  left_join(subscale_map, by = "qn") %>%
  mutate(
    response_num = ifelse(reverse, 8 - response_num, response_num)
  )


# Average score per participant, condition, subscale
subscale_scores <- long_data %>%
  group_by(participant_id, condition, subscale) %>%
  summarise(avg_score = mean(response_num, na.rm = TRUE), .groups = "drop")


# Remove 1.5*IQR outliers within each condition and subscale
subscale_scores_filtered <- subscale_scores %>%
  group_by(condition, subscale) %>%
  mutate(
    Q1 = quantile(avg_score, 0.25, na.rm = TRUE),
    Q3 = quantile(avg_score, 0.75, na.rm = TRUE),
    IQR = Q3 - Q1,
    lower = Q1 - 1.5 * IQR,
    upper = Q3 + 1.5 * IQR
  ) %>%
  ungroup() %>%
  filter(avg_score >= lower, avg_score <= upper) %>%
  select(participant_id, condition, subscale, avg_score)

# Reshape for tests: one row per participant per subscale
test_data <- subscale_scores_filtered %>%
  pivot_wider(
    names_from = condition,
    values_from = avg_score,
    names_prefix = "condition"
  )

# STEP 1: Overall Friedman tests per subscale
overall_results <- list()
significant_subscales <- c()

for (s in unique(subscale_scores$subscale)) {
  sub_data <- test_data %>% filter(subscale == s)
  scores_mat <- sub_data %>%
    select(starts_with("condition")) %>%
    na.omit() %>%
    as.matrix()

  if (nrow(scores_mat) >= 3) {
    friedman_res <- friedman.test(scores_mat)
    overall_results[[length(overall_results) + 1]] <- data.frame(
      subscale = s,
      p_value = friedman_res$p.value
    )
    if (friedman_res$p.value < 0.05) {
      significant_subscales <- c(significant_subscales, s)
    }
  }
}

overall_df <- bind_rows(overall_results)
overall_df$p_adj_bh <- p.adjust(overall_df$p_value, method = "BH")

# STEP 2: Post-hoc pairwise Wilcoxon tests
pairwise_results <- list()
conditions <- c("1", "2", "3", "4", "5")
pair_combinations <- combn(conditions, 2, simplify = FALSE)

for (s in significant_subscales) {
  sub_data <- test_data %>% filter(subscale == s)
  for (pair in pair_combinations) {
    c1 <- paste0("condition", pair[1])
    c2 <- paste0("condition", pair[2])
    paired_data <- sub_data %>%
      select(participant_id, all_of(c(c1, c2))) %>%
      na.omit()

    if (nrow(paired_data) >= 3) {
      test <- wilcox.test(paired_data[[c1]], paired_data[[c2]], paired = TRUE)
      pairwise_results[[length(pairwise_results) + 1]] <- data.frame(
        subscale = s,
        comparison = paste("Condition", pair[1], "vs", pair[2]),
        p_value = test$p.value
      )
    }
  }
}

pairwise_df <- bind_rows(pairwise_results)
pairwise_df$p_adj_bh <- p.adjust(pairwise_df$p_value, method = "BH")

# Label subscales
subscale_names <- c(
  "1" = "Interest/Enjoyment",
  "2" = "Perceived Competence",
  "3" = "Effort/Importance",
  "4" = "Value/Usefulness"
)

# Final formatting
overall_df$subscale <- factor(overall_df$subscale, levels = names(subscale_names), labels = subscale_names)
pairwise_df$subscale <- factor(pairwise_df$subscale, levels = names(subscale_names), labels = subscale_names)

# View results
cat("=== Overall Friedman Test Results ===\n")
print(overall_df)

cat("\n=== Pairwise Wilcoxon Comparisons (Only for Significant Subscales) ===\n")
pairwise_df = pairwise_df  %>% filter(p_value < 0.05)
print(pairwise_df )

```

## Per-lesson IMI

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(likert)
library(ggplot2)  # for ggtitle()

# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Select all lesson columns (lesson1 to lesson4)
lesson_cols <- imi_clean %>% 
  select(participant_id, matches("^imi\\d+_lesson[1-4]$"))

# Convert from wide to long format (all lessons)
long_data <- lesson_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = c("question", "lesson"),
    names_pattern = "imi(\\d+)_lesson(\\d)",
    values_to = "response"
  ) %>%
  mutate(
    question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
    response = factor(as.numeric(response),
                      levels = 1:7,
                      labels = c("Extremely disagree", "Disagree", "Slightly disagree",
                                 "Neutral", "Slightly agree", "Agree", "Extremely agree"),
                      ordered = TRUE)
  )

full_levels <- c("Extremely disagree", "Disagree", "Slightly disagree",
                 "Neutral", "Slightly agree", "Agree", "Extremely agree")

# Loop over each lesson and create separate likert plots
for (lesson_num in unique(long_data$lesson)) {
  
  lesson_data <- long_data %>% filter(lesson == lesson_num)
  
  likert_ready <- lesson_data %>%
    group_by(participant_id) %>%
    select(participant_id, question, response) %>%
    pivot_wider(names_from = question, values_from = response) %>%
    select(paste0("Q", 1:25))
  
  likert_ready <- as.data.frame(likert_ready)
  
  # Ensure all columns have the same ordered factor levels:
  likert_ready[] <- lapply(likert_ready, function(x) {
    factor(x, levels = full_levels, ordered = TRUE)
  })
  
  likert_obj <- likert(likert_ready)
  
  print(plot(likert_obj, group.order = paste0("Q", 1:25)) + 
          ggtitle(paste("Likert Plot for Lesson", lesson_num)))
}

```

## Per-lesson IMI - ANOVA

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(purrr)

# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Select relevant lesson columns
lesson_cols <- imi_clean %>% 
  select(participant_id, matches("^imi\\d+_lesson[1-4]$"))

# Convert from wide to long format
long_data <- lesson_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = c("question", "lesson"),
    names_pattern = "imi(\\d+)_lesson(\\d)",
    values_to = "response"
  ) %>%
  mutate(
    question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
    lesson = factor(lesson, levels = 1:4),
    response_num = as.numeric(response)
  )

# Shapiro-Wilk test for normality per question (combining lessons)
normality_results <- long_data %>%
  group_by(question) %>%
  summarise(
    shapiro_p = if(n() >= 3) shapiro.test(response_num)$p.value else NA_real_
  )

print(normality_results)

# Prepare data wide: each row = participant & question, columns = lessons
friedman_data <- long_data %>%
  select(participant_id, question, lesson, response_num) %>%
  pivot_wider(names_from = lesson, values_from = response_num) %>%
  filter(complete.cases(.))  # only keep participants with all lessons

# Run Friedman test per question
friedman_results <- friedman_data %>%
  group_by(question) %>%
  summarise(
    p_value = {
      mat <- as.matrix(select(cur_data(), `1`, `2`, `3`, `4`))
      friedman.test(mat)$p.value
    }
  )

print(friedman_results)

# Step 2: Post-hoc pairwise Wilcoxon signed-rank tests for those questions

# Function to do pairwise Wilcoxon for a question
posthoc_wilcox <- function(df) {
  # df: filtered for one question, wide format with lessons columns
  lessons <- c("1","2","3","4")
  pairs <- combn(lessons, 2, simplify = FALSE)
  
  results <- map_dfr(pairs, function(pair) {
    res <- wilcox.test(df[[pair[1]]], df[[pair[2]]], paired = TRUE, exact = FALSE)
    mean_diff <- mean(df[[pair[1]]] - df[[pair[2]]])
    tibble(
      lesson_1 = pair[1],
      lesson_2 = pair[2],
      p_value = res$p.value,
      mean_difference = mean_diff
    )
  })
  
  # Adjust p-values for multiple comparisons (Bonferroni)
  results <- results %>%
    mutate(p_adj = p.adjust(p_value, method = "bonferroni"))
  
  return(results)
}

# Run posthoc tests for each significant question
posthoc_results <- friedman_results %>%
  pull(question) %>%
  set_names() %>%
  map_df(function(q) {
    df_q <- friedman_data %>% filter(question == q)
    res <- posthoc_wilcox(df_q)
    res$question <- q
    res
  }, .id = "question")

# Filter only pairs with significant adjusted p-values < 0.05
significant_pairs <- posthoc_results %>%
  filter(p_adj < 0.05) %>%
  select(question, lesson_1, lesson_2, mean_difference, p_value, p_adj)

print(significant_pairs)



```

## Per-condition IMI

```{r}
library(readr)
library(dplyr)
library(tidyr)
library(likert)
library(ggplot2)  # for ggtitle()

# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Select all condition columns (condition1 to condition4)
condition_cols <- imi_clean %>% 
  select(participant_id, matches("^imi\\d+_condition[1-4]$"))

# Convert from wide to long format (all conditions)
long_data <- condition_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = c("question", "condition"),
    names_pattern = "imi(\\d+)_condition(\\d)",
    values_to = "response"
  ) %>%
  mutate(
    question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
    response = factor(as.numeric(response),
                      levels = 1:7,
                      labels = c("Extremely disagree", "Disagree", "Slightly disagree",
                                 "Neutral", "Slightly agree", "Agree", "Extremely agree"),
                      ordered = TRUE)
  )

full_levels <- c("Extremely disagree", "Disagree", "Slightly disagree",
                 "Neutral", "Slightly agree", "Agree", "Extremely agree")

# Loop over each condition and create separate likert plots
for (condition_num in unique(long_data$condition)) {
  
  condition_data <- long_data %>% filter(condition == condition_num)
  
  likert_ready <- condition_data %>%
    group_by(participant_id) %>%
    select(participant_id, question, response) %>%
    pivot_wider(names_from = question, values_from = response) %>%
    select(paste0("Q", 1:25))
  
  likert_ready <- as.data.frame(likert_ready)
  
  # Ensure all columns have the same ordered factor levels:
  likert_ready[] <- lapply(likert_ready, function(x) {
    factor(x, levels = full_levels, ordered = TRUE)
  })
  
  likert_obj <- likert(likert_ready)
  
  print(plot(likert_obj, group.order = paste0("Q", 1:25)) + 
          ggtitle(paste("Likert Plot for Condition", condition_num)))
}

```

## Per-condition IMI - ANOVA

```{r}
library(readr)
library(dplyr)
library(tidyr)

# Load and clean data
imi <- read_csv("imi.csv", show_col_types = FALSE)
excluded_ids <- c("682f47a03095562bd581bd06", "674d7a89f564e8bbe0a7af1c", "65537e14439274fe5036b4d2")
imi_clean <- imi %>% filter(!prolific_id %in% excluded_ids)

# Select relevant condition columns
lesson_cols <- imi_clean %>% 
  select(participant_id, matches("^imi\\d+_condition[1-4]$"))

# Convert from wide to long format
long_data <- lesson_cols %>%
  pivot_longer(
    cols = -participant_id,
    names_to = c("question", "condition"),
    names_pattern = "imi(\\d+)_condition(\\d)",
    values_to = "response"
  ) %>%
  mutate(
    question = factor(paste0("Q", question), levels = paste0("Q", 1:25), ordered = TRUE),
    lesson = factor(condition, levels = 1:4),
    response_num = as.numeric(response)
  )

# Shapiro-Wilk test for normality per question (combining lessons)
normality_results <- long_data %>%
  group_by(question) %>%
  summarise(
    shapiro_p = if(n() >= 3) shapiro.test(response_num)$p.value else NA_real_
  )

print(normality_results)

# Prepare data wide: each row = participant & question, columns = lessons
friedman_data <- long_data %>%
  select(participant_id, question, condition, response_num) %>%
  pivot_wider(names_from = condition, values_from = response_num) %>%
  filter(complete.cases(.))  # only keep participants with all lessons

# Run Friedman test per question
friedman_results <- friedman_data %>%
  group_by(question) %>%
  summarise(
    p_value = {
      mat <- as.matrix(select(cur_data(), `1`, `2`, `3`, `4`))
      friedman.test(mat)$p.value
    }
  )

print(friedman_results)


# Step 2: Post-hoc pairwise Wilcoxon signed-rank tests for those questions

# Function to do pairwise Wilcoxon for a question
posthoc_wilcox <- function(df) {
  # df: filtered for one question, wide format with lessons columns
  conditions <- c("1","2","3","4")
  pairs <- combn(conditions, 2, simplify = FALSE)
  
  results <- map_dfr(pairs, function(pair) {
    res <- wilcox.test(df[[pair[1]]], df[[pair[2]]], paired = TRUE, exact = FALSE)
    mean_diff <- mean(df[[pair[1]]] - df[[pair[2]]])
    tibble(
      condition_1 = pair[1],
      condition_2 = pair[2],
      p_value = res$p.value,
      mean_difference = mean_diff
    )
  })
  
  # Adjust p-values for multiple comparisons (bh)
  results <- results %>%
    mutate(p_adj_bh = p.adjust(p_value, method = "BH"))
  
   results <- results %>%
    mutate(p_adj_bonferroni = p.adjust(p_value, method = "bonferroni"))
  
  return(results)
}

# Run posthoc tests for each significant question
posthoc_results <- friedman_results %>%
  pull(question) %>%
  set_names() %>%
  map_df(function(q) {
    df_q <- friedman_data %>% filter(question == q)
    res <- posthoc_wilcox(df_q)
    res$question <- q
    res
  }, .id = "question")

# Filter only pairs with significant adjusted p-values < 0.05
significant_pairs <- posthoc_results %>%
  filter(p_value < 0.1) %>%
  filter(condition_2 == 4) %>%
  select(question, condition_1, condition_2, mean_difference, p_value, p_adj_bh, p_adj_bonferroni)

print(significant_pairs)

```

# LIWC

## Correlations on Users (mid to post)

```{r}

library(readr)
library(dplyr)
library(corrr)


print(data)

# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
  select(user_id, matches("likert_(mid|post)_condition\\d+")) %>%  # keep user_id
  pivot_longer(
    cols = -user_id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff = post - mid,
    condition = case_when(
      condition == "1" ~ "Control",
      condition == "2" ~ "Reading",
      condition == "3" ~ "Writing",
      condition == "4" ~ "Chatbot",
      TRUE ~ condition
    )
  ) %>%
  select(user_id, condition, diff) %>%
  pivot_wider(names_from = condition, values_from = diff)


# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - participant_texts - LIWC Analysis.csv", delim = ";")

# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>%  # Extract numeric ID from "user_XX"
  filter(!user_id %in% excluded_ids)

# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
  mutate(user_id = as.numeric(user_id)) %>%
  filter(!user_id %in% excluded_ids)

liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
  filter(!user_id %in% excluded_ids)


# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")

print(merged_data)

# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
  select(-Segment, -user_id) %>%
  select(where(is.numeric)) %>% names()

cor_input <- merged_data %>%
  select(all_of(liwc_columns), Chatbot)

cor_results <- correlate(cor_input) %>%
  focus(Chatbot) %>%
  arrange(desc(abs(Chatbot)))  # Sort by strength of correlation

# 6. View results
print(cor_results)

# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
  filter(abs(Chatbot) > 0.3)
print(strong_corrs)

# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
  select(all_of(liwc_columns))

chatbot_scores <- merged_data$Chatbot

# Initialize a results data frame
results <- data.frame(term = character(), 
                      cor = numeric(), 
                      p_value = numeric(), 
                      stringsAsFactors = FALSE)

# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
  test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
  
  results <- rbind(results, data.frame(
    term = var,
    cor = test$estimate,
    p_value = test$p.value
  ))
}

# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))

results <- results %>%
  mutate(p_adj = p.adjust(p_value, method = "BH"))  # FDR correction

# View significant after correction
sig_results <- results %>%
  filter(p_value < 0.05)

print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)

# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
  select(user_id, Chatbot, all_of(liwc_columns))

# Convert LIWC terms to long format
long_data <- plot_data %>%
  pivot_longer(
    cols = -c(user_id, Chatbot),
    names_to = "LIWC_term",
    values_to = "LIWC_value"
  )

# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)

# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
  group_by(LIWC_term) %>%
  summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')

# Merge with the correlation results
results_with_proportion <- results %>%
  inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))

# Set thresholds
cor_threshold <- 0.3 
nz_threshold <- 0.3 

# Filter for strong correlations and non-zero inflated terms

# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
  filter(term == "health") %>%
  pull(term)

# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
  filter(LIWC_term %in% filtered_terms)

library(dplyr)
library(tidyr)
library(ggplot2)

# Add custom names mapping
custom_names <- c(
  "health" = "Health"
)

# Filter and prepare annotations
annotations <- results_with_proportion %>%
  filter(term %in% filtered_terms) %>%
  mutate(
    label = paste0("r = ", round(cor, 2), ", p = ", format(p_adj, digits = 2)),
    LIWC_term = term
  ) %>%
  select(LIWC_term, label)

# Add annotations to the long data for plotting
top_non_zero_long_data <- top_non_zero_long_data %>%
  left_join(annotations, by = "LIWC_term")

# Plot with custom facet labels
ggplot(top_non_zero_long_data, aes(x = LIWC_value, y = Chatbot)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = FALSE, color = "steelblue") +
  facet_wrap(~ LIWC_term, scales = "free_x", labeller = labeller(LIWC_term = custom_names)) +
  labs(
    title = "Correlation of LIWC 'Health' Term Value\nand Post-Attack Certainty Change",
    x = "LIWC 'Health' Term Value",
    y = "Post-Attack Certainty Change"
  ) +
  theme_minimal(base_size = 14) +
  theme(strip.text = element_text(size = 12, face = "bold")) +
  geom_label(
    data = annotations,
    mapping = aes(x = Inf, y = Inf, label = label),
    hjust = 1.1, vjust = 1.5,
    inherit.aes = FALSE,
    fill = alpha("white", 0.5)
  )


```

## Correlations on Users (pre to mid)

```{r}

library(readr)
library(dplyr)
library(corrr)


print(data)

# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
  select(user_id, matches("likert_(pre|mid)_condition\\d+")) %>%  # keep user_id
  pivot_longer(
    cols = -user_id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid)_condition(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff = mid - pre,
    condition = case_when(
      condition == "1" ~ "Control",
      condition == "2" ~ "Reading",
      condition == "3" ~ "Writing",
      condition == "4" ~ "Chatbot",
      TRUE ~ condition
    )
  ) %>%
  select(user_id, condition, diff) %>%
  pivot_wider(names_from = condition, values_from = diff)


# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - participant_texts - LIWC Analysis.csv", delim = ";")

# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>%  # Extract numeric ID from "user_XX"
  filter(!user_id %in% excluded_ids)

# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
  mutate(user_id = as.numeric(user_id)) %>%
  filter(!user_id %in% excluded_ids)

liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
  filter(!user_id %in% excluded_ids)


# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")

print(merged_data)

# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
  select(-Segment, -user_id) %>%
  select(where(is.numeric)) %>% names()

cor_input <- merged_data %>%
  select(all_of(liwc_columns), Chatbot)

cor_results <- correlate(cor_input) %>%
  focus(Chatbot) %>%
  arrange(desc(abs(Chatbot)))  # Sort by strength of correlation

# 6. View results
print(cor_results)

# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
  filter(abs(Chatbot) > 0.3)
print(strong_corrs)

# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
  select(all_of(liwc_columns))

chatbot_scores <- merged_data$Chatbot

# Initialize a results data frame
results <- data.frame(term = character(), 
                      cor = numeric(), 
                      p_value = numeric(), 
                      stringsAsFactors = FALSE)

# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
  test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
  
  results <- rbind(results, data.frame(
    term = var,
    cor = test$estimate,
    p_value = test$p.value
  ))
}

# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))

results <- results %>%
  mutate(p_adj = p.adjust(p_value, method = "BH"))  # FDR correction

# View significant after correction
sig_results <- results %>%
  filter(p_value < 0.05)

print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)

# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
  select(user_id, Chatbot, all_of(liwc_columns))

# Convert LIWC terms to long format
long_data <- plot_data %>%
  pivot_longer(
    cols = -c(user_id, Chatbot),
    names_to = "LIWC_term",
    values_to = "LIWC_value"
  )

# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)

# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
  group_by(LIWC_term) %>%
  summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')

# Merge with the correlation results
results_with_proportion <- results %>%
  inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))

# Set thresholds
cor_threshold <- 0.3 
nz_threshold <- 0.3 

# Filter for strong correlations and non-zero inflated terms

# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
  filter(term == "health") %>%
  pull(term)

# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
  filter(LIWC_term %in% filtered_terms)

```

## Correlations on Chatbot (mid to post)

```{r}

library(readr)
library(dplyr)
library(corrr)


print(data)

# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
  select(user_id, matches("likert_(mid|post)_condition\\d+")) %>%  # keep user_id
  pivot_longer(
    cols = -user_id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(mid|post)_condition(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff = post - mid,
    condition = case_when(
      condition == "1" ~ "Control",
      condition == "2" ~ "Reading",
      condition == "3" ~ "Writing",
      condition == "4" ~ "Chatbot",
      TRUE ~ condition
    )
  ) %>%
  select(user_id, condition, diff) %>%
  pivot_wider(names_from = condition, values_from = diff)


# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - bot_texts - LIWC Analysis.csv", delim = ";")

# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>%  # Extract numeric ID from "user_XX"
  filter(!user_id %in% excluded_ids)

# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
  mutate(user_id = as.numeric(user_id)) %>%
  filter(!user_id %in% excluded_ids)

liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
  filter(!user_id %in% excluded_ids)


# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")

print(merged_data)

# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
  select(-Segment, -user_id) %>%
  select(where(is.numeric)) %>% names()

cor_input <- merged_data %>%
  select(all_of(liwc_columns), Chatbot)

cor_results <- correlate(cor_input) %>%
  focus(Chatbot) %>%
  arrange(desc(abs(Chatbot)))  # Sort by strength of correlation

# 6. View results
print(cor_results)

# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
  filter(abs(Chatbot) > 0.3)
print(strong_corrs)

# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
  select(all_of(liwc_columns))

chatbot_scores <- merged_data$Chatbot

# Initialize a results data frame
results <- data.frame(term = character(), 
                      cor = numeric(), 
                      p_value = numeric(), 
                      stringsAsFactors = FALSE)

# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
  test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
  
  results <- rbind(results, data.frame(
    term = var,
    cor = test$estimate,
    p_value = test$p.value
  ))
}

# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))

results <- results %>%
  mutate(p_adj = p.adjust(p_value, method = "BH"))  # FDR correction

# View significant after correction
sig_results <- results %>%
  filter(p_value < 0.05)

print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)

# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
  select(user_id, Chatbot, all_of(liwc_columns))

# Convert LIWC terms to long format
long_data <- plot_data %>%
  pivot_longer(
    cols = -c(user_id, Chatbot),
    names_to = "LIWC_term",
    values_to = "LIWC_value"
  )

# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)

# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
  group_by(LIWC_term) %>%
  summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')

# Merge with the correlation results
results_with_proportion <- results %>%
  inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))

# Set thresholds
cor_threshold <- 0.3 
nz_threshold <- 0.3 

# Filter for strong correlations and non-zero inflated terms

# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
  filter(term == "health") %>%
  pull(term)

# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
  filter(LIWC_term %in% filtered_terms)

library(dplyr)
library(tidyr)
library(ggplot2)

# Add custom names mapping
custom_names <- c(
  "health" = "Health"
)

# Filter and prepare annotations
annotations <- results_with_proportion %>%
  filter(term %in% filtered_terms) %>%
  mutate(
    label = paste0("r = ", round(cor, 2), ", p = ", format(p_adj, digits = 2)),
    LIWC_term = term
  ) %>%
  select(LIWC_term, label)

# Add annotations to the long data for plotting
top_non_zero_long_data <- top_non_zero_long_data %>%
  left_join(annotations, by = "LIWC_term")

# Plot with custom facet labels
ggplot(top_non_zero_long_data, aes(x = LIWC_value, y = Chatbot)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = FALSE, color = "steelblue") +
  facet_wrap(~ LIWC_term, scales = "free_x", labeller = labeller(LIWC_term = custom_names)) +
  labs(
    title = "Correlation of LIWC 'Health' Term Value\nand Post-Inoculation Certainty Change",
    x = "LIWC 'Health' Term Value",
    y = "Post-Attack Certainty Change"
  ) +
  theme_minimal(base_size = 14) +
  theme(strip.text = element_text(size = 12, face = "bold")) +
  geom_label(
    data = annotations,
    mapping = aes(x = Inf, y = Inf, label = label),
    hjust = 1.1, vjust = 1.5,
    inherit.aes = FALSE,
    fill = alpha("white", 0.5)
  )


```

## Correlations on Chatbot (pre to mid)

```{r}

library(readr)
library(dplyr)
library(corrr)


print(data)

# Step 1: Reshape and compute differences
all_certainty_deltas <- data %>%
  select(user_id, matches("likert_(pre|mid)_condition\\d+")) %>%  # keep user_id
  pivot_longer(
    cols = -user_id,
    names_to = c("phase", "condition"),
    names_pattern = "likert_(pre|mid)_condition(\\d+)",
    values_to = "score"
  ) %>%
  pivot_wider(names_from = phase, values_from = score) %>%
  mutate(
    diff = mid - pre,
    condition = case_when(
      condition == "1" ~ "Control",
      condition == "2" ~ "Reading",
      condition == "3" ~ "Writing",
      condition == "4" ~ "Chatbot",
      TRUE ~ condition
    )
  ) %>%
  select(user_id, condition, diff) %>%
  pivot_wider(names_from = condition, values_from = diff)

print(all_certainty_deltas)

# 1. Load the LIWC and delta data
liwc_data <- readr::read_delim("LIWC-22 Results - bot_texts - LIWC Analysis.csv", delim = ";")

# 2. Drop unwanted user_ids (assuming Segment contains user ID or a name like "user_10")
excluded_ids <- c(10, 26, 29, 48, 49, 52)
liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("\\D+", "", Segment))) %>%  # Extract numeric ID from "user_XX"
  filter(!user_id %in% excluded_ids)

# 3. Prepare all_certainty_deltas (ensure participant_id format matches)
all_certainty_deltas <- all_certainty_deltas %>%
  mutate(user_id = as.numeric(user_id)) %>%
  filter(!user_id %in% excluded_ids)

liwc_filtered <- liwc_data %>%
  mutate(user_id = as.numeric(gsub("user_(\\d+)\\.txt", "\\1", Filename))) %>%
  filter(!user_id %in% excluded_ids)


# 4. Join LIWC and delta data
merged_data <- inner_join(liwc_filtered, all_certainty_deltas, by = "user_id")

print(merged_data)

# 5. Run correlation between all LIWC features and Chatbot deltas
# Exclude non-numeric or identifier columns first
liwc_columns <- liwc_filtered %>%
  select(-Segment, -user_id) %>%
  select(where(is.numeric)) %>% names()

cor_input <- merged_data %>%
  select(all_of(liwc_columns), Chatbot)

cor_results <- correlate(cor_input) %>%
  focus(Chatbot) %>%
  arrange(desc(abs(Chatbot)))  # Sort by strength of correlation

# 6. View results
print(cor_results)

# Optional: Filter for meaningful correlation (e.g. > 0.3 or < -0.3)
strong_corrs <- cor_results %>%
  filter(abs(Chatbot) > 0.3)
print(strong_corrs)

# Select numeric columns and Chatbot delta
numeric_vars <- merged_data %>%
  select(all_of(liwc_columns))

chatbot_scores <- merged_data$Chatbot

# Initialize a results data frame
results <- data.frame(term = character(), 
                      cor = numeric(), 
                      p_value = numeric(), 
                      stringsAsFactors = FALSE)

# Loop over each LIWC variable and run cor.test with Chatbot
for (var in colnames(numeric_vars)) {
  test <- cor.test(numeric_vars[[var]], chatbot_scores, use = "pairwise.complete.obs")
  
  results <- rbind(results, data.frame(
    term = var,
    cor = test$estimate,
    p_value = test$p.value
  ))
}

# Sort by absolute correlation
results <- results %>% arrange(desc(abs(cor)))

results <- results %>%
  mutate(p_adj = p.adjust(p_value, method = "BH"))  # FDR correction

# View significant after correction
sig_results <- results %>%
  filter(p_value < 0.05)

print(sig_results)
library(tidyr)
library(ggplot2)
library(dplyr)

# Select LIWC columns + Chatbot delta and user_id
plot_data <- merged_data %>%
  select(user_id, Chatbot, all_of(liwc_columns))

# Convert LIWC terms to long format
long_data <- plot_data %>%
  pivot_longer(
    cols = -c(user_id, Chatbot),
    names_to = "LIWC_term",
    values_to = "LIWC_value"
  )

# Assuming 'results' already contains the correlation data for LIWC terms
library(dplyr)
library(tidyr)
library(ggplot2)

# Calculate proportion of non-zero values for each LIWC term
non_zero_proportion <- long_data %>%
  group_by(LIWC_term) %>%
  summarize(non_zero_ratio = sum(LIWC_value != 0) / n(), .groups = 'drop')

# Merge with the correlation results
results_with_proportion <- results %>%
  inner_join(non_zero_proportion, by = c("term" = "LIWC_term"))

# Set thresholds
cor_threshold <- 0.3 
nz_threshold <- 0.3 

# Filter for strong correlations and non-zero inflated terms

# Filter specifically for "Health" term
filtered_terms <- results_with_proportion %>%
  filter(term == "health") %>%
  pull(term)

# Filter long_data for these terms
top_non_zero_long_data <- long_data %>%
  filter(LIWC_term %in% filtered_terms)

library(dplyr)
library(tidyr)
library(ggplot2)

# Add custom names mapping
custom_names <- c(
  "health" = "Health"
)

# Filter and prepare annotations
annotations <- results_with_proportion %>%
  filter(term %in% filtered_terms) %>%
  mutate(
    label = paste0("r = ", round(cor, 2), ", p = ", format(p_adj, digits = 2)),
    LIWC_term = term
  ) %>%
  select(LIWC_term, label)

# Add annotations to the long data for plotting
top_non_zero_long_data <- top_non_zero_long_data %>%
  left_join(annotations, by = "LIWC_term")

# Plot with custom facet labels
ggplot(top_non_zero_long_data, aes(x = LIWC_value, y = Chatbot)) +
  geom_point(alpha = 0.6) +
  geom_smooth(method = "lm", se = FALSE, color = "steelblue") +
  facet_wrap(~ LIWC_term, scales = "free_x", labeller = labeller(LIWC_term = custom_names)) +
  labs(
    title = "Correlation of LIWC 'Health' Term Value\nand Post-Inoculation Certainty Change",
    x = "LIWC 'Health' Term Value",
    y = "Post-Inoculation Certainty Change"
  ) +
  theme_minimal(base_size = 14) +
  theme(strip.text = element_text(size = 12, face = "bold")) +
  geom_label(
    data = annotations,
    mapping = aes(x = Inf, y = Inf, label = label),
    hjust = 1.1, vjust = 1.5,
    inherit.aes = FALSE,
    fill = alpha("white", 0.5)
  )


```
