knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
df1 <- read.csv("c_llm.csv")
df2 <- read.csv("c_rbs.csv")
merged_df <- inner_join(df1, df2, by = "PROLIFIC_PID", suffix = c("_1", "_2"))
print(merged_df)
tests <- list(
overall = t.test(merged_df$OVERALL_1, merged_df$OVERALL_2, paired = TRUE),
believability1 = t.test(merged_df$BELIEVABILITY1_1, merged_df$BELIEVABILITY1_2, paired = TRUE),
believability2 = t.test(merged_df$BELIEVABILITY2_1, merged_df$BELIEVABILITY2_2, paired = TRUE),
engagement = t.test(merged_df$ENGAGEMENT_1, merged_df$ENGAGEMENT_2, paired = TRUE),
attitude = t.test(merged_df$ATTITUDE_1, merged_df$ATTITUDE_2, paired = TRUE)
)
results_df <- data.frame(
Measure = names(tests),
t_statistic = sapply(tests, function(t) round(t$statistic, 3)),
p_value = sapply(tests, function(t) formatC(t$p.value, format = "e", digits = 2)),
mean_difference = sapply(tests, function(t) round(t$estimate, 3)),
conf_low = sapply(tests, function(t) round(t$conf.int[1], 3)),
conf_high = sapply(tests, function(t) round(t$conf.int[2], 3))
)
print(results_df, row.names = FALSE)
library(dplyr)
df1 <- read.csv("c_llm.csv")
df2 <- read.csv("c_rbs.csv")
merged_df <- inner_join(df1, df2, by = "PROLIFIC_PID", suffix = c("_1", "_2"))
print(merged_df)
tests <- list(
overall = t.test(merged_df$OVERALL_1, merged_df$OVERALL_2, paired = TRUE),
believability1 = t.test(merged_df$BELIEVABILITY1_1, merged_df$BELIEVABILITY1_2, paired = TRUE),
believability2 = t.test(merged_df$BELIEVABILITY2_1, merged_df$BELIEVABILITY2_2, paired = TRUE),
engagement = t.test(merged_df$ENGAGEMENT_1, merged_df$ENGAGEMENT_2, paired = TRUE),
attitude = t.test(merged_df$ATTITUDE_1, merged_df$ATTITUDE_2, paired = TRUE)
)
results_df <- data.frame(
Measure = names(tests),
t_statistic = sapply(tests, function(t) round(t$statistic, 3)),
p_value = sapply(tests, function(t) formatC(t$p.value, format = "e", digits = 2)),
mean_difference = sapply(tests, function(t) round(t$estimate, 3)),
conf_low = sapply(tests, function(t) round(t$conf.int[1], 3)),
conf_high = sapply(tests, function(t) round(t$conf.int[2], 3))
)
print(results_df, row.names = FALSE)
binom.test(26, 37, p = 0.5, alternative = "two.sided")
knitr::opts_chunk$set(echo = TRUE)
# Perform binomial test
binom.test(26, 37, p = 0.5, alternative = "two.sided")
library(dplyr)
# Read data files for the LLM and rule-based conditions
df1 <- read.csv("c_llm.csv")
library(dplyr)
# Read data files for the LLM and rule-based conditions
df1 <- read.csv("constructs_averaged_llm.csv")
df2 <- read.csv("constructs_averaged_rbs.csv")
# Combine data from both files based on partipant ID
merged_df <- inner_join(df1, df2, by = "PROLIFIC_PID", suffix = c("_1", "_2"))
# Perform T-Tests for the five constructs
tests <- list(
overall = t.test(merged_df$OVERALL_1, merged_df$OVERALL_2, paired = TRUE),
believability1 = t.test(merged_df$BELIEVABILITY1_1, merged_df$BELIEVABILITY1_2, paired = TRUE),
believability2 = t.test(merged_df$BELIEVABILITY2_1, merged_df$BELIEVABILITY2_2, paired = TRUE),
engagement = t.test(merged_df$ENGAGEMENT_1, merged_df$ENGAGEMENT_2, paired = TRUE),
attitude = t.test(merged_df$ATTITUDE_1, merged_df$ATTITUDE_2, paired = TRUE)
)
# Gather relevant results
results_df <- data.frame(
Measure = names(tests),
t_statistic = sapply(tests, function(t) round(t$statistic, 3)),
p_value = sapply(tests, function(t) formatC(t$p.value, format = "e", digits = 2)),
mean_difference = sapply(tests, function(t) round(t$estimate, 3)),
conf_low = sapply(tests, function(t) round(t$conf.int[1], 3)),
conf_high = sapply(tests, function(t) round(t$conf.int[2], 3))
)
# Print results
print(results_df, row.names = FALSE)
# Perform binomial test
binom.test(26, 37, p = 0.5, alternative = "two.sided")
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
knitr::opts_chunk$set(echo = TRUE)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
knitr::opts_chunk$set(echo = TRUE)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Perform binomial test
binom.test(26, 37, p = 0.5, alternative = "two.sided")
# Perform binomial test
binom_results <- binom.test(26, 37, p = 0.5, alternative = "two.sided")
# Print results
print(binom_results)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
knitr::opts_chunk$set(echo = TRUE)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
cat(power_analysis_result)
install.packages("pwr")
# Load the pwr package
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
cat(power_analysis_result)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
knitr::opts_chunk$set(echo = TRUE)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
install.packages("pwr")
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
knitr::opts_chunk$set(echo = TRUE)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
# Load the pwr package
install.packages("pwr")
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
knitr::opts_chunk$set(echo = TRUE)
# Load the pwr package
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
# Load the pwr package
library(pwr)
# Perform power analysis for Two-Tailed Paired-Samples T-Test
# Effect Size = 0.5 (Medium)
# Target Power = 0.80
# Alpha Error = 0.05
# Type = Two-Tailed
power_analysis_result <- pwr.t.test(d = 0.5, power = 0.80, sig.level = 0.05, type = "paired", alternative = "two.sided")
# Print results
print(power_analysis_result)
# Perform binomial test
binom_results <- binom.test(26, 37, p = 0.5, alternative = "two.sided")
# Print results
print(binom_results)
library(dplyr)
# Read data files for the LLM and rule-based conditions
df1 <- read.csv("constructs_averaged_llm.csv")
df2 <- read.csv("constructs_averaged_rbs.csv")
# Combine data from both files based on partipant ID
merged_df <- inner_join(df1, df2, by = "PROLIFIC_PID", suffix = c("_1", "_2"))
# Perform T-Tests for the five constructs
tests <- list(
overall = t.test(merged_df$OVERALL_1, merged_df$OVERALL_2, paired = TRUE),
believability1 = t.test(merged_df$BELIEVABILITY1_1, merged_df$BELIEVABILITY1_2, paired = TRUE),
believability2 = t.test(merged_df$BELIEVABILITY2_1, merged_df$BELIEVABILITY2_2, paired = TRUE),
engagement = t.test(merged_df$ENGAGEMENT_1, merged_df$ENGAGEMENT_2, paired = TRUE),
attitude = t.test(merged_df$ATTITUDE_1, merged_df$ATTITUDE_2, paired = TRUE)
)
# Gather relevant results
results_df <- data.frame(
Measure = names(tests),
t_statistic = sapply(tests, function(t) round(t$statistic, 3)),
p_value = sapply(tests, function(t) formatC(t$p.value, format = "e", digits = 2)),
mean_difference = sapply(tests, function(t) round(t$estimate, 3)),
conf_low = sapply(tests, function(t) round(t$conf.int[1], 3)),
conf_high = sapply(tests, function(t) round(t$conf.int[2], 3))
)
# Print results
print(results_df, row.names = FALSE)
library(dplyr)
# Read data files for the LLM and rule-based conditions
df1 <- read.csv("constructs_averaged_llm.csv")
df2 <- read.csv("constructs_averaged_rbs.csv")
# Combine data from both files based on partipant ID
merged_df <- inner_join(df1, df2, by = "PROLIFIC_PID", suffix = c("_1", "_2"))
# Perform T-Tests for the five constructs
tests <- list(
overall = t.test(merged_df$OVERALL_1, merged_df$OVERALL_2, paired = TRUE),
believability1 = t.test(merged_df$BELIEVABILITY1_1, merged_df$BELIEVABILITY1_2, paired = TRUE),
believability2 = t.test(merged_df$BELIEVABILITY2_1, merged_df$BELIEVABILITY2_2, paired = TRUE),
engagement = t.test(merged_df$ENGAGEMENT_1, merged_df$ENGAGEMENT_2, paired = TRUE),
attitude = t.test(merged_df$ATTITUDE_1, merged_df$ATTITUDE_2, paired = TRUE)
)
# Gather relevant results
results_df <- data.frame(
Measure = names(tests),
t_statistic = sapply(tests, function(t) round(t$statistic, 3)),
p_value = sapply(tests, function(t) formatC(t$p.value, format = "e", digits = 2)),
mean_difference = sapply(tests, function(t) round(t$estimate, 3)),
conf_low = sapply(tests, function(t) round(t$conf.int[1], 3)),
conf_high = sapply(tests, function(t) round(t$conf.int[2], 3))
)
# Print results
print(results_df, row.names = FALSE)
