setwd("C:/Users/emily_2vx2daj/OneDrive - IL State University/Documents/Info/School/College/Grad School/ISU/Thesis/Kahn Lab 2025 - Budik Thesis Study")
#library downloads
library(dplyr)
library(ggplot2)
library(tidyr)
library(effectsize)
library(apaTables)
#read file
thesis <- read.csv("thesis.csv")
#ORDER OF FILE:
# #Demographics (First)
# #Creating ANOVAs and analyses (meat+potatoes)
# # Mean, SD, Correlations of Vairbles (Last)
# The following Variables all have #a Mean, #b ANOVA,
# #c Summary #d Bar graph Plot #e Interaction plot if significant
# #1 Well-being, #2 Stress,
# #3 Perceived Difficulty, #4, Perceived Enjoyment #5 Perceived Control
# #6 Perceived Effort #7 Perceived Performance
# BEGIN DEMOGRAPHICS
#AGE
age_summary <- thesis %>%
summarise(
Mean = mean(AGE, na.rm = TRUE),
Median = median(AGE, na.rm = TRUE),
SD = sd(AGE, na.rm = TRUE),
Min = min(AGE, na.rm = TRUE),
Max = max(AGE, na.rm = TRUE))
print(age_summary)
## Mean Median SD Min Max
## 1 19.50562 19 1.431176 17 26
# 89 total particip, find percentages by x/89 * 100
#GENDER
#1 = Male, 2 = Female, #3 = Other (nonbinary, fluid, thirdgender, etc.)
gend_counts <- thesis %>%
count(GEND, sort = TRUE)
print(gend_counts)
## GEND n
## 1 2 69
## 2 1 19
## 3 3 1
#YEAR IN SCHOOL
#1 = Freshmen #2 = sophomore
#3 = junior #4 = senior #5 = grad student
yr_counts <- thesis %>%
count(YR, sort = TRUE)
print(yr_counts)
## YR n
## 1 1 28
## 2 3 25
## 3 2 21
## 4 4 15
#ETHNICITY
#1 = Asian #2 = Biracial / Multiracial #3 = Black or African American
#4 = Latinx #5 = American Indian or Alaskan Native
#6 = Pacific Islander / Native Hawaiian #7 = White #8 = Other
eth_counts <- thesis %>%
count(ETH, sort = TRUE)
print(eth_counts)
## ETH n
## 1 7 45
## 2 4 18
## 3 3 17
## 4 2 5
## 5 1 2
## 6 5 1
## 7 8 1
#ANOVA #creating circumstances, music yes/no, ae yes/no #ae yes/no
thesis <-
thesis %>% mutate(ae = case_when(COND %in% c(1, 2) ~ "No",
COND %in% c(3, 4) ~ "Yes"))
#music yes/no
thesis <- thesis %>%
mutate(mus = case_when(COND %in%c(1, 3) ~ "No",
COND %in% c(2, 4) ~ "Yes"))
#creating them as factors for analysis
thesis$ae <- as.factor(thesis$ae)
thesis$mus <- as.factor(thesis$mus)
#Begin wellbeing eval
# Create a new column for wellbeing averages
thesis <- thesis %>%
mutate(wellbeing_avg = rowMeans(select(., starts_with("WB_")), na.rm = TRUE) / 10)
#begin ANOVA for wellbeing
wellbeing_aov <- aov(wellbeing_avg ~ ae * mus, data = thesis)
summary(wellbeing_aov)
## Df Sum Sq Mean Sq F value Pr(>F)
## ae 1 0.13 0.130 0.050 0.824
## mus 1 5.19 5.194 1.998 0.161
## ae:mus 1 2.20 2.205 0.848 0.360
## Residuals 85 221.00 2.600
#Summary for Wellbeing
wellbeing_summary <- thesis %>%
group_by(ae, mus) %>%
summarise(
N = n(),
Mean = mean(wellbeing_avg, na.rm = TRUE),
SD = sd(wellbeing_avg, na.rm = TRUE),
SE = SD / sqrt(N), # Standard Error for error bars
.groups = 'drop'
)
print(wellbeing_summary)
## # A tibble: 4 × 6
## ae mus N Mean SD SE
## <fct> <fct> <int> <dbl> <dbl> <dbl>
## 1 No No 22 7.33 1.54 0.328
## 2 No Yes 22 6.53 1.34 0.285
## 3 Yes No 23 7.09 1.47 0.307
## 4 Yes Yes 22 6.92 2.02 0.431
#plot for WB
wellbeing_plot <- ggplot(wellbeing_summary, aes(x = ae, y = Mean, fill = mus)) +
# Create the bar chart, specifying "dodge" for side-by-side bars
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black") +
geom_errorbar(
aes(ymin = Mean - SE, ymax = Mean + SE),
width = 0.2, # Width of the error bar caps
position = position_dodge(width = 0.9)
) + labs(
title = "Wellbeing Average",
x = "Aesthetic Enrichment (AE)",
y = "Average Wellbeing Score",
fill = "Music" ) +
scale_fill_manual(values = c("No" = "lightblue", "Yes" = "darkblue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
print(wellbeing_plot)
#Begin Stress eval
# Create a new column for stress averages
thesis <- thesis %>%
mutate(stress_avgscores = rowMeans(select(., starts_with("DASS_")),
na.rm = TRUE))
#begin ANOVA for stress
stress_aov <- aov(stress_avgscores ~ ae * mus, data = thesis)
print(summary(stress_aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## ae 1 0.155 0.1550 0.562 0.456
## mus 1 0.293 0.2925 1.060 0.306
## ae:mus 1 0.343 0.3432 1.244 0.268
## Residuals 85 23.454 0.2759
#Summary for Stress
stress_summary <- thesis %>%
group_by(ae, mus) %>%
summarise(
N = n(),
Mean = mean(stress_avgscores, na.rm = TRUE),
SD = sd(stress_avgscores, na.rm = TRUE),
SE = SD / sqrt(N),
.groups = 'drop')
#begin plot for stress
stress_plot <- ggplot(stress_summary, aes(x = ae, y = Mean, fill = mus)) +
# Create the bar chart
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black") +
geom_errorbar(
aes(ymin = Mean - SE, ymax = Mean + SE),
width = 0.2,
position = position_dodge(width = 0.9)
) + labs(
title = "Stress Average",
x = "Aesthetic Enrichment (AE)",
y = "Average Stress Score (Lower is Better)",
fill = "Music"
) +
scale_fill_manual(values = c("No" = "lightcoral", "Yes" = "red4")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
print(stress_plot)
#Begin perceived difficulty eval
#level of perceived difficulty
mean(thesis$DIFF)
## [1] 2.865169
# ANOVA for perceived difficulty - 11/13; AE*, everything reaching sig
difficulty_aov <- aov(DIFF ~ ae * mus, data = thesis)
print(summary(difficulty_aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## ae 1 4.43 4.435 5.375 0.0228 *
## mus 1 2.75 2.751 3.334 0.0714 .
## ae:mus 1 3.07 3.070 3.722 0.0571 .
## Residuals 85 70.13 0.825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
diff_ome <- omega_squared(difficulty_aov, partial = TRUE)
#Summary for perceived difficulty
diff_summary <- thesis %>%
group_by(ae, mus) %>%
summarise(
N = n(),
Mean = mean(DIFF, na.rm = TRUE),
SD = sd(DIFF, na.rm = TRUE),
SE = SD / sqrt(N),
.groups = 'drop')
diff_plot <- ggplot(diff_summary, aes(x = ae, y = Mean, fill = mus)) +
# Create the bar chart, specifying "dodge" for side-by-side bars
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black") +
geom_errorbar(
aes(ymin = Mean - SE, ymax = Mean + SE),
width = 0.2, # Width of the error bar caps
position = position_dodge(width = 0.9) # Match bar position
) +
labs(title = "Perceived Difficulty Average",
x = "Aesthetic Enrichment (AE)",
y = "Perceived Difficulty Score",
fill = "Music" # 'fill' is used for the legend when bars are filled
) +
scale_fill_manual(values = c("No" = "lightblue", "Yes" = "darkblue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
print(diff_plot)
#diffifculty interaction plot since marginally signif trend
#Interaction Plot for perceived difficulty
diff_intplot <- ggplot(diff_summary, aes(x = ae, y = Mean, group = mus, color = mus)) +
geom_line(linewidth = 1) +
geom_point(size = 3) +
# Add error bars (using 95% CI is common, but SE is shown here for simplicity)
geom_errorbar(aes(ymin = Mean - SE, ymax = Mean + SE), width = 0.15) +
labs(
title = "Perceived Difficulty Average Interaction",
x = "Aesthetic Enrichment (AE)",
y = "Average Perceived Difficulty Score",
color = "Music"
) +
scale_color_manual(values = c("No" = "red", "Yes" = "blue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
# Display the plot
print(diff_intplot)
#Begin percevied enjoyment eval
# avg level of perceived enjoyment
mean(thesis$ENJ)
## [1] 2.988764
#begin ANOVA for perceived enjoyment - 11/13 -
enjoyment_aov <- aov(ENJ ~ ae * mus, data = thesis)
print(summary(enjoyment_aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## ae 1 0.55 0.5524 0.549 0.461
## mus 1 0.90 0.8969 0.891 0.348
## ae:mus 1 0.01 0.0078 0.008 0.930
## Residuals 85 85.53 1.0063
enj_summary <- thesis %>%
group_by(ae, mus) %>%
summarise(
N = n(),
Mean = mean(ENJ, na.rm = TRUE),
SD = sd(ENJ, na.rm = TRUE),
SE = SD / sqrt(N),
.groups = 'drop')
enj_plot <- ggplot(enj_summary, aes(x = ae, y = Mean, fill = mus)) +
# Create the bar chart, specifying "dodge" for side-by-side bars
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black") +
geom_errorbar(
aes(ymin = Mean - SE, ymax = Mean + SE),
width = 0.2, # Width of the error bar caps
position = position_dodge(width = 0.9) # Match bar position
) +
labs(title = "Perceived Enjoyment Average",
x = "Aesthetic Enrichment (AE)",
y = "Perceived Enjoyment Score",
fill = "Music" # 'fill' is used for the legend when bars are filled
) +
scale_fill_manual(values = c("No" = "lightblue", "Yes" = "darkblue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
print(enj_plot)
#Begin perceived control eval
#level of perceived control
mean(thesis$CNTRL)
## [1] 3.41573
#begin ANOVA for perceived control of the environment - 11/13 mus*, ae:mus*
control_aov <- aov(CNTRL ~ ae * mus, data = thesis)
print(summary(control_aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## ae 1 3.09 3.091 3.412 0.0682 .
## mus 1 3.80 3.805 4.200 0.0435 *
## ae:mus 1 3.72 3.722 4.109 0.0458 *
## Residuals 85 77.00 0.906
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
control_ome <- omega_squared(control_aov, partial = TRUE)
#Summary for perceived control
cntrl_summary <- thesis %>%
group_by(ae, mus) %>%
summarise(
N = n(),
Mean = mean(CNTRL, na.rm = TRUE),
SD = sd(CNTRL, na.rm = TRUE),
SE = SD / sqrt(N),
.groups = 'drop')
cntrl_plot <- ggplot(cntrl_summary, aes(x = ae, y = Mean, fill = mus)) +
# Create the bar chart, specifying "dodge" for side-by-side bars
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black") +
geom_errorbar(
aes(ymin = Mean - SE, ymax = Mean + SE),
width = 0.2, # Width of the error bar caps
position = position_dodge(width = 0.9) # Match bar position
) + labs(
title = "Perceived Control Average",
x = "Aesthetic Enrichment (AE)",
y = "Perceived Control Score",
fill = "Music" # 'fill' is used for the legend when bars are filled
) +
scale_fill_manual(values = c("No" = "lightblue", "Yes" = "darkblue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
print(cntrl_plot)
#Interaction Plot for CNTRL
cntrl_intplot <- ggplot(cntrl_summary, aes(x = ae, y = Mean, group = mus, color = mus)) +
geom_line(linewidth = 1) +
geom_point(size = 3) +
# Add error bars (using 95% CI is common, but SE is shown here for simplicity)
geom_errorbar(aes(ymin = Mean - SE, ymax = Mean + SE), width = 0.15) +
labs(
title = "Perceived Control Average Interaction",
x = "Aesthetic Enrichment (AE)",
y = "Average Perceived Control Score",
color = "Music"
) +
scale_color_manual(values = c("No" = "red", "Yes" = "blue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
# Display the plot
print(cntrl_intplot)
#Begin effort eval
#level of perceived effort
mean(thesis$EFF)
## [1] 3.595506
#begin ANOVA for perceived effort - 11/13 mus*
effort_aov <- aov(EFF ~ ae * mus, data = thesis)
print(summary(effort_aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## ae 1 0.35 0.3518 0.695 0.4068
## mus 1 2.06 2.0584 4.067 0.0469 *
## ae:mus 1 0.00 0.0043 0.008 0.9271
## Residuals 85 43.02 0.5062
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
effort_ome <- omega_squared(effort_aov, partial = TRUE)
#Summary for effort
eff_summary <- thesis %>%
group_by(ae, mus) %>%
summarise(
N = n(),
Mean = mean(EFF, na.rm = TRUE),
SD = sd(EFF, na.rm = TRUE),
SE = SD / sqrt(N),
.groups = 'drop')
#plot effort eval
eff_plot <- ggplot(eff_summary, aes(x = ae, y = Mean, fill = mus)) +
# Create the bar chart, specifying "dodge" for side-by-side bars
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black") +
geom_errorbar(
aes(ymin = Mean - SE, ymax = Mean + SE),
width = 0.2, # Width of the error bar caps
position = position_dodge(width = 0.9) # Match bar position
) + labs(
title = "Effort Average",
x = "Aesthetic Enrichment (AE)",
y = "Effort Score",
fill = "Music" # 'fill' is used for the legend when bars are filled
) +
scale_fill_manual(values = c("No" = "lightblue", "Yes" = "darkblue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
print(eff_plot)
#level of perceived performance
mean(thesis$SBP)
## [1] 3.101124
#begin ANOVA for perceived subjective performance - 11/13 -
subjectiveperf_aov <- aov(SBP ~ ae * mus, data = thesis)
print(summary(subjectiveperf_aov))
## Df Sum Sq Mean Sq F value Pr(>F)
## ae 1 0.09 0.0944 0.194 0.661
## mus 1 0.09 0.0924 0.190 0.664
## ae:mus 1 0.58 0.5770 1.187 0.279
## Residuals 85 41.33 0.4862
#Summary for perceived subjective performance
sbp_summary <- thesis %>%
group_by(ae, mus) %>%
summarise(
N = n(),
Mean = mean(SBP, na.rm = TRUE),
SD = sd(SBP, na.rm = TRUE),
SE = SD / sqrt(N),
.groups = 'drop')
#plot Subjective performance
sbp_plot <- ggplot(sbp_summary, aes(x = ae, y = Mean, fill = mus)) +
# Create the bar chart, specifying "dodge" for side-by-side bars
geom_bar(stat = "identity", position = position_dodge(width = 0.9), color = "black") +
# Add error bars (SE)
geom_errorbar(
aes(ymin = Mean - SE, ymax = Mean + SE),
width = 0.2, # Width of the error bar caps
position = position_dodge(width = 0.9) # Match bar position
) +
labs(
title = "Perceived Performance Average",
x = "Aesthetic Enrichment (AE)",
y = "Perceived Performance Score",
fill = "Music" # 'fill' is used for the legend when bars are filled
) +
scale_fill_manual(values = c("No" = "lightblue", "Yes" = "darkblue")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
print(sbp_plot)
#Begin Descriptive Statistics
#Mean / SD table
descriptive_table <- thesis %>%
# Select only the measures you're interested in
select(
`Well-being` = wellbeing_avg,
`Stress` = stress_avgscores,
`Perceived Difficulty` = DIFF,
`Perceived Enjoyment` = ENJ,
`Perceived Control` = CNTRL,
`Perceived Effort` = EFF,
`Perceived Performance` = SBP
) %>%
summarise(across(
.cols = everything(),
.fns = list(
Mean = ~mean(.x, na.rm = TRUE),
SD = ~sd(.x, na.rm = TRUE)
),
.names = "{.col}---{.fn}"
)) %>%
pivot_longer(
cols = everything(),
names_to = c("Measure", ".value"), # ".value" takes the part after "---" (Mean, SD)
names_sep = "---"
)
print(descriptive_table)
## # A tibble: 7 × 3
## Measure Mean SD
## <chr> <dbl> <dbl>
## 1 Well-being 6.97 1.61
## 2 Stress 1.71 0.525
## 3 Perceived Difficulty 2.87 0.956
## 4 Perceived Enjoyment 2.99 0.994
## 5 Perceived Control 3.42 0.998
## 6 Perceived Effort 3.60 0.719
## 7 Perceived Performance 3.10 0.692
#begin correlation analysis
correlation_data <- thesis %>%
select(
`Wellbeing` = wellbeing_avg,
`Stress` = stress_avgscores,
`Difficulty` = DIFF,
`Enjoyment` = ENJ,
`Control` = CNTRL,
`Effort` = EFF,
`Performance` = SBP
)
# Create the correlation table
# This function calculates correlations, p-values, and formats it
correlation_results <- apa.cor.table(correlation_data,
filename = "apa_correlation_table.doc",
show.conf.interval = FALSE) # Don't show confidence intervals
## The ability to suppress reporting of reporting confidence intervals has been deprecated in this version.
## The function argument show.conf.interval will be removed in a later version.
print(correlation_results)
##
##
## Means, standard deviations, and correlations with confidence intervals
##
##
## Variable M SD 1 2 3 4
## 1. Wellbeing 6.97 1.61
##
## 2. Stress 1.71 0.52 -.10
## [-.31, .11]
##
## 3. Difficulty 2.87 0.96 -.11 .55**
## [-.31, .10] [.39, .68]
##
## 4. Enjoyment 2.99 0.99 .36** -.33** -.31**
## [.16, .53] [-.51, -.13] [-.49, -.11]
##
## 5. Control 3.42 1.00 .27* -.27* -.26* .33**
## [.06, .45] [-.45, -.06] [-.45, -.06] [.13, .50]
##
## 6. Effort 3.60 0.72 .25* .10 .15 .15
## [.05, .44] [-.11, .30] [-.06, .35] [-.06, .35]
##
## 7. Performance 3.10 0.69 .20 -.24* -.43** .38**
## [-.01, .39] [-.42, -.03] [-.58, -.24] [.19, .55]
##
## 5 6
##
##
##
##
##
##
##
##
##
##
##
##
##
##
## -.05
## [-.25, .16]
##
## .17 .20
## [-.04, .36] [-.01, .39]
##
##
## Note. M and SD are used to represent mean and standard deviation, respectively.
## Values in square brackets indicate the 95% confidence interval.
## The confidence interval is a plausible range of population correlations
## that could have caused the sample correlation (Cumming, 2014).
## * indicates p < .05. ** indicates p < .01.
##