Morbid Curiosity, Anxiety & VANE

Author

Brendan Jaghab & David Brocker

Hypotheses:

H1: Morbid Curiosity should correlate and predict Horror Typology.

H2: Morbid Curiosity should correlate with anxiety, using a two-tailed approach

H3: Morbidly Curious individuals are more likely to expose themselves to negative stimuli

H4: Anxious individuals should experience a decrease in anxiety after expose to (voluntary) negative stimuli

Stimuli

Table 1. Stimuli Used in Study
Negative Positive

An image of a man bleeding and screaming after a plane crash, surrounded by others running away from the crash.

(Body)

 An image of a man screaming for the winning team of a basketball game.

An image of a third world country revolting against its military on the street

(Violence)

 An image of a third world country coming together in a moment of prayer.

An image depicting an infamous, run-down haunted house

(Paranormal)

 An image depicting a newly built suburban home

An image of a prisoner being prepared to be killed by the electric chair

(Violence)

 An image of a prisoner being convicted not guilty of the charges against him.

An image of a malnourished refugee in a third-world country

(Body)

 An image of a famous model, with a face of makeup, on a beach

An image of a hunter carrying the severed, bloody head of a deer they just hunted.

(Body)

 An image of a person carrying a basket of fresh-picked fruit.

Watch a documentary explaining the murders of famous serial killer Dennis Nilsen, that includes his taped confession and how he carried out his murders.

(Mind)

 Watch a documentary following the life of former Apple CEO, Steve Jobs.

An image of a man putting a gun in a blood man’s mouth as he pleads for mercy.

(Violence)

 An image of a man comforting another man that has just been attacked.

An image of a man shooting a polar bear in the arctic.

(Violence)

 An image of a man taking a photograph of a polar bear in the arctic.
Read a book that describes the rituals and magic beliefs of witches. (Paranormal) Read a book that describes various ways to improve your gardening skills.

Read an FBI profile of serial killer John Dillinger explaining his murders and characteristics.

(Mind)

 Read a profile describing the accomplishments of a well-known astronaut.

An image of a man injecting heroin on the floor in a drug den.

(Body)

 An image of a man sleeping on the floor next to his two children.

Listen to a podcast that focuses on infamous serial killers and their crimes.

(Mind)

 Listen to a podcast that comforts people by describing how to manage their anxieties.
An interactive video of how to play the spiritual game, Ouija. (Paranormal) An interactive video of how to play the board game, Monopoly.

An image depicting a witch’s circle conducting a ritual.

(Paranormal)

 An image depicting a church.

Read an autobiography about the life of a serial killer.

(Mind)

 Read an autobiography about the life and accomplishments of Oprah Winfrey.

Data Processing

Code
# Load in Packages-------------------------------------------

library(dplyr, warn.conflicts = F) # Tidying data 
library(stringr) # String manipulation 
library(ggplot2) # Plotting 
library(purrr) # Handling lists and iterations 
library(tidytext) # Tidying text 
library(forcats) # Dealing with categories 
library(readxl) # Read Excel 
library(patchwork) # Plotting Multiple Graphs 
library(tidyr) # Reshaping 
library(huxtable, warn.conflicts = F) # Making nice tables 
library(ggcorrplot, warn.conflicts = F) # Correlation heatmap
library(skimr) # Continuous Data
library(janitor, warn.conflicts = F) # Cleaan names/Chi Square
library(colorspace)

# Establish Functions----------------------------------------

# Correlation Test Plot Function
correl_test <- function(data, col1,col2){

# Get Correlation: For x and y 
  d1 <- 
    data %>% 
    select({{col1}}) %>% 
    pull() 
  d2 <- 
    data %>% 
    select({{col2}}) %>% 
    pull()

# Store correlation test 
  ct <- cor.test(d1,d2)

# Store DF 
  degf <- ct$parameter %>% as.numeric()

# Store Confidence Intervals 
# First as numbers 
  ci <- round(ct$conf.int[1:2],2)

# Then as text: 95% CI [#,#] 
  cit <- paste0("95%CI[",ci[1],",",ci[2],"]")

# Store r value 
  rv <- round(ct$estimate,2)

# Plot and annotate 
  data %>%
    # Variable names are passed through with {{brackets}} 
    ggplot(aes({{col1}},{{col2}}))+ 
    geom_point() + # To get variable in plain text, use deparse(substitute(variable)) 
    labs(x = deparse(substitute(col1)), 
         y = deparse(substitute(col2)), 
         title = paste0( deparse( substitute(col1))," and ", deparse(substitute(col2))), 
    # Pull r value or anything else you want! 
    caption = paste0("r(",degf,")= ",rv," | ",cit)) +
    theme_minimal() + 
    theme( 
    # Center Plot 
    plot.title = element_text(hjust = .5, size = 16, face ="bold"))
}

# Standard Error Function
se <- function(x) { 
  se <- sd(x)/sqrt(length(x)) 
}

# Rescale MC and HT
# Morbid Curiosity
scale_six <- function(x) {(x/6)} 
# Horror Typology
scale_seven <- function(x) {(x/7)} 

# Discrete Scales
discrete_tab <- function(data,x,lab){
  name <- 
    data |> 
    dplyr::select({{x}}) |> 
    pull() 
  
  tab <- 
    name |> 
    tabyl() |> 
    dplyr::select(-(starts_with("val"))) |> 
    # Ignore Error for Now,,,
    adorn_pct_formatting(,,,percent) |> 
    rename_with(str_to_sentence) |> 
    hux() |> 
    theme_article() |> 
    set_align(everywhere,everywhere,".")

  # Change header row to Variable | | 
  tab[1,] <- c(str_to_sentence(x),"","")
  
  # Get Number of Rows
  to_in <- nrow(tab)
  
  tab[2:to_in,1] <- lab

  return(tab)
}

# Read and Clean Data----------------------------------------
 mcu <- read_excel("UpdatedMCdata.xlsx")

# Clean
 mcu_cln <- 
  mcu %>% 
  # Get participants who finished 
  filter(Finished == "1") %>% 
  # Get participants who consented 
  filter(`Consent?` == "1") %>% 
  # Remove unneeded columns 
  select(-DistributionChannel,-UserLanguage) %>% 
  # Clean variable names 
  rename_with(~gsub(" ","_",.x)) %>% 
  # Make Score Columns numeric 
  mutate_at(vars(SC0:SC14),as.numeric) %>% 
  # Remove SC0 and replace with scale name 
  rename( 
    Morbid_Curiosity_Overall = SC0, 
    Mind = SC1, 
    Body = SC2, 
    Violence = SC3,
    Paranormal = SC4,
    Horror_Typology_Overall = SC5, 
    AJ = SC6, 
    WK = SC7, 
    DC = SC8, 
    Trait = SC9, 
    State_Pre = SC10, 
    Positive_Choice = SC11,
    Negative_Choice = SC12,
    PN_Choice = SC13, 
    State_Post = SC14) |>
  mutate(
    # Divide all MC Total Score by 24
    Morbid_Curiosity_Overall = Morbid_Curiosity_Overall/24,
    # Divide all HT Total Score by 19
    Horror_Typology_Overall = Horror_Typology_Overall/19 ) |>
    # Divide each MC Subscale Item by 6
    mutate_at(vars(Mind:Paranormal),scale_six) |> 
    # Divide each HT Subscale Item by 6
    mutate_at(vars(AJ:DC),scale_seven)
 
# Subset Scales
 mc_scaled <- 
  mcu_cln %>% 
  select(Morbid_Curiosity_Overall:State_Post)

Demographics

Code
demo <- 
  mcu_cln |> 
  clean_names() |> 
  rename(
    major = major_of_study
  ) |> 
  select(gender,race,religion,major)

gen_lab <- 
  c("Male","Female", "Non-Binary","I prefer not to answer")


race_lab <- 
  c("Black or African American","White","Hispanic/Latino",
    "Asian","Multi-Racial","Other")

rel_lab <- 
  c("No Religion","Christian","Buddhist",
    "Hindu","Jewish","Muslim","Other","I prefer not to answer")

major_lab <- 
  c("Business Administration and Management", "Science, Technology, and Society",
    "Computer Programming", "Biology/Biological Sciences", "Applied Psychology", "Other")

# Get Tables for Gender, Age, Ethnicity, Politics, Race, and Education
gen <- discrete_tab(demo,"gender",gen_lab)

race <- discrete_tab(demo,"race",race_lab)

rel <- discrete_tab(demo,"religion",rel_lab)

major <- discrete_tab(demo,"major",major_lab)

# Build Table
gen |> 
  add_rows(race) |> 
  add_rows(rel) |> 
  add_rows(major) |> 
  # Add Header Row
  insert_row("Variable","N","Percent") |> 
  set_top_border(row = 1:3,1:3) |> 
  set_align(col = 1, value = "left") |>  
  set_align(1,1,"center") |> 
  # set_caption("Table 2: Participant Demographics") |> 
  set_width(value = 1)
Variable N Percent
Gender    
Male 26 28.0%
Female 64 68.8%
Non-Binary 2 2.2%
I prefer not to answer 1 1.1%
Race    
Black or African American 9 9.7%
White 53 57.0%
Hispanic/Latino 14 15.1%
Asian 14 15.1%
Multi-Racial 2 2.2%
Other 1 1.1%
Religion    
No Religion 11 11.8%
Christian 50 53.8%
Buddhist 1 1.1%
Hindu 1 1.1%
Jewish 3 3.2%
Muslim 5 5.4%
Other 17 18.3%
I prefer not to answer 5 5.4%
Major    
Business Administration and Management 7 7.5%
Science, Technology, and Society 14 15.1%
Computer Programming 2 2.2%
Biology/Biological Sciences 8 8.6%
Applied Psychology 24 25.8%
Other 38 40.9%

Correlations

Code
# Clean variable name 
nheat <-  
  mc_scaled |>  
  rename(`Morbid Curiosity` = Morbid_Curiosity_Overall, 
         `Horror Typology` = Horror_Typology_Overall, 
         `Adrenaline Junkies` = AJ, 
         `White Knucklers` = WK,  
         `Dark Copers` = DC, 
         `State Anxiety (Pre)` = State_Pre, 
         `State Anxiety (Post)` = State_Post, 
         `Positive` = Positive_Choice, 
         `Negative` = Negative_Choice 
     
  ) |>  
  select(-PN_Choice) |>  
  cor() 
  
# Create Correlation (p) Matrix 
nheat_pmat <-  
  nheat |> 
  cor_pmat(sig.level = .05) 
  
  
# Plot 
#cor.plot <-  
  nheat |>  
  ggcorrplot( 
    outline.color = "white", 
    colors = diverge_hcl(3, palette='Blue Red'), 
    hc.order = T, 
    type = "lower", 
    lab = TRUE, 
    p.mat = nheat_pmat, 
    insig = "blank", 
    tl.cex = 10, 
    tl.srt = 90, 
    lab_size = 2.5, 
    lab_col = "white", 
    legend.title = "Correlation", 
    ggtheme = theme_minimal()) + 
  labs(caption = "Fig 1. Correlation Matrix Across all numeric variables. Blank items represent non-significant correlations. \nHierarchical clustering is applied.") + theme(plot.caption = element_text(hjust = .5))

State and Trait Comparisons

Post vs. Pre

Code
t.test(mc_scaled$State_Post,mc_scaled$State_Pre, paired = T) |> 
  broom::tidy() |> 
     rename(
    t = statistic,
    p = p.value, 
    df = parameter,
    `mean difference` = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
    # set_caption("Table 3:  No significant difference (or decrease in anxiety)") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
mean difference t p df conf.low conf.high method alternative
-1.1 -1.76 0.0825 92 -2.34 0.144 Paired t-test two.sided

Pre vs. Trait

Code
t.test(mc_scaled$State_Pre,mc_scaled$Trait,paired = T) |> 
  tidy() |> 
    rename(
    t = statistic,
    p = p.value, 
    df = parameter,
    `mean difference` = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
#   set_caption("Table 4: Significant Difference between Pre Anxiety and Trait Anxiety
# ") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
mean difference t p df conf.low conf.high method alternative
-3.15 -4.28 4.64e-05 92 -4.61 -1.69 Paired t-test two.sided

Post vs. Trait

Code
t.test(mc_scaled$State_Post,mc_scaled$Trait,paired = T) |> 
  broom::tidy() |> 
    rename(
    t = statistic, 
    p = p.value, 
    df = parameter,
    `mean difference` = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 5: Significant Difference between Posttest Anxiety and Overall") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
mean difference t p df conf.low conf.high method alternative
-4.25 -5.38 5.6e-07 92 -5.81 -2.68 Paired t-test two.sided

STAI Measure: Low Anxiety vs. High Anxiety (Pre)

Code
mc_scaled %>% 
  select(State_Pre) %>% 
  mutate(Anxiety_Level = 
           ifelse(State_Pre> 40,"High Anxiety","Low Anxiety")) %>% 
  select(Anxiety_Level) |> 
  table() %>% 
  janitor::chisq.test() |> 
  broom::tidy() |> 
  rename(
    p = p.value, 
    df = parameter
  ) |> 
  mutate(
    method = "Chi-squared test"
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 6: There are significantly more 'high-anxiety' individuals compared to 'low'anxiety' individuals based on scores from the pretest measure of STAI") |> 
  set_caption_pos("bottom") |> 
  set_width(value = 1)
statistic p df method
25.8 3.75e-07 1 Chi-squared test

STAI Measure: Low Anxiety vs. High Anxiety (Pre)

Code
mc_scaled %>% 
  select(State_Post) %>%
  mutate(Anxiety_Level = 
           ifelse(State_Post > 40,"High Anxiety","Low Anxiety")) %>%
  select(Anxiety_Level) %>%
  table() %>%
  janitor::chisq.test() |> 
  broom::tidy() |> 
   rename(
    p = p.value, 
    df = parameter
  ) |> 
  mutate(
    method = "Chi-squared test"
  ) |> 
  hux() |> 
  theme_article() |> 
    # set_caption("Table 7: There are significantly more 'high-anxiety' individuals compared to 'low'anxiety' individuals based on scores from the posttest measure of STAI") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
statistic p df method
4.74 0.0294 1 Chi-squared test

STAI Measure: Low Anxiety vs. High Anxiety (Trait)

Code
mc_scaled %>% 
  select(Trait) %>%
  mutate(Anxiety_Level = 
           ifelse(Trait > 40,"High Anxiety","Low Anxiety")) %>%
  select(Anxiety_Level) %>%
  table() %>%
  janitor::chisq.test() |> 
  broom::tidy() |> 
   rename(
    p = p.value, 
    df = parameter
  ) |> 
  mutate(
    method = "Chi-squared test"
  ) |> 
  hux() |> 
  theme_article() |> 
    # set_caption("Table 8: There are significantly more 'high-anxiety' individuals compared to 'low'anxiety' individuals based on scores from the trait measure of STAI") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
statistic p df method
32.5 1.18e-08 1 Chi-squared test
Code
mc_scaled %>% 
  select(starts_with("State")) %>%
  rename_with(~gsub("State_","",.x)) |> 
  pivot_longer(cols = everything(),
               names_to = "time",
               values_to = "score") |>
  mutate(time = factor(time, levels = c("Pre", "Post"))) |>
  group_by(time) |> 
  summarize(mean = mean(score),
            se = se(score)) %>% 
  ggplot(aes(time,mean)) +
  geom_pointrange(aes(ymin = mean - se, ymax = mean + se)) + 
  scale_color_manual(values = c("red", "black")) +
  theme_minimal() + 
  labs(
    x = "\nTime",
    y = "Average\n",
    title = "Average State Anxiety Scores Before & After VANE\n",
    caption = "Fig. 2: No significant decrease in anxiety levels a fter exposure to VANE."
  ) +
  theme(plot.title = element_text(hjust = 0.5,
                                  face = "bold"),
        plot.caption = element_text(hjust = .2,
                                    face = "italic")) +
  ylim(30,46)

Regressions

Horror Typology as a function of Morbid Curiosity

Code
mc_ht.mod <- summary(lm(mc_scaled$Morbid_Curiosity_Overall ~ mc_scaled$Horror_Typology_Overall))

# Make Table
mc_tab <- 
  mc_ht.mod |> 
  huxreg(error_pos = "right",
         statistics = character(0)) |> 
  theme_article() |> 
  # set_caption("Table 9: Model showing Morbid Curiosity Overall score as a predictor of Horror Typology Overall score") |> 
  set_caption_pos("bottom") |> 
  set_width(value = 1)

# Rename Model Name
mc_tab[1,2] <- "Model 1"
# Rename Model Coeficients 
mc_tab[2:3,1] <- c("Morbid Curiosity Overall","Horror Typology Overall")

mc_tab
Model 1
Morbid Curiosity Overall 1.303 **  (0.390)
Horror Typology Overall 0.583 *** (0.110)
*** p < 0.001; ** p < 0.01; * p < 0.05.
Code
# Plot the relationship
correl_test(mc_scaled, Morbid_Curiosity_Overall,Horror_Typology_Overall) +
  labs(x = "\nMorbid Curiosity Overall",
       y = "Horror Typology Overall\n",
       title = "Morbid Curiosity Overall and Horror Typology Overall\n",
       caption = "Fig 3. Correlation between Morbid Curiosity and Horror Typology") +
  geom_abline(color = "darkred")

MC Overall and Pre/Post

Code
mc_stai.mod <- 
  lm(Morbid_Curiosity_Overall~ State_Pre + State_Post,data = mc_scaled) %>%
  summary() 

mc_st_tab <- 
  mc_stai.mod |> 
  huxreg(error_pos = "right",
         statistics = character(0)) |> 
  theme_article() |> 
  # set_caption("Table 10: Model showing Morbid Curiosity Overall score as a poor predictor of STAI Pre and Post") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)

mc_st_tab[1,2] <- "Model 2"

mc_st_tab[2,1] <- c("Morbid Curiosity Overall")

mc_st_tab
Model 2
Morbid Curiosity Overall 2.958 *** (0.651)
State_Pre -0.004     (0.019)
State_Post 0.013     (0.016)
*** p < 0.001; ** p < 0.01; * p < 0.05.

Negative & Positive Choice

Code
mc_scaled |>
  select(Positive_Choice, Negative_Choice) |>
  rename_with( ~ gsub("_Choice", "", .x, )) |>
  pivot_longer(cols = everything(),
               names_to = "Choice",
               values_to = "Picked") |>
  group_by(Choice) |>
  summarize(M = mean(Picked),
            SE = se(Picked)) |>
  ggplot(aes(Choice, M)) +
  geom_pointrange(aes(
    ymin = M - SE,
    ymax = M + SE,
    colour = Choice),
    fatten = 6,
    show.legend = F) +
  geom_line(group = 1) +
  theme_minimal() +
  labs(x = "\nChoice",
       y = "Average\n",
       title = "Average Choice of Stimuli Among Participants",
       caption = "Fig 4: Results indicate that on average, participants chose significantally more positive choices than negative choices.") +
  theme(axis.text = element_text(color = "black"),
        plot.title = element_text(hjust = 0.5, face = "bold"),
        axis.title.y = element_text(size = 12),
        plot.caption = element_text(hjust = .2,
                                    face = "italic")) +
  scale_color_manual(values = c("darkgreen", "maroon"))

Negative/Positive choice:

Code
choice_tt <- 
  t.test(mc_scaled$Negative_Choice,mc_scaled$Positive_Choice,
       paired = T,
       var.equal = T) 

choice_tt |> 
  tidy() |> 
  rename(
    t = statistic,
    p = p.value, 
    df = parameter,
    `mean difference` = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 11: Paired t-test showing participants chose significantally more positive items.") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
mean difference t p df conf.low conf.high method alternative
-4.86 -6.75 1.28e-09 92 -6.29 -3.43 Paired t-test two.sided
Code
mc_neg.mod <- 
  lm(data = mc_scaled, Morbid_Curiosity_Overall ~ Negative_Choice) |> 
  summary()

mc_neg.mod_tab <- 
  mc_neg.mod |> 
  huxreg(error_pos = "right",
         statistics = character(0)) |> 
  theme_article() |> 
  # set_caption("Table 12: Model showing negative choice selection is accurately predicted by Morbid Curiosity Scale score") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)

mc_neg.mod_tab[1,2] <- "Model 3"

mc_neg.mod_tab[2,1] <- "Morbid Curiosity Scale"

mc_neg.mod_tab
Model 3
Morbid Curiosity Scale 2.385 *** (0.132)
Negative_Choice 0.170 *** (0.020)
*** p < 0.001; ** p < 0.01; * p < 0.05.
Code
mcu_cln |> 
  ggplot(aes(Morbid_Curiosity_Overall,Negative_Choice)) + 
  geom_point() + 
  theme_minimal() +
  geom_smooth(method = "lm") +
  labs(x = "\nMorbid Curiosity Score", 
       y = "Number of Negative Choices\n",
       caption = "Fig 5: Correlation between overall Morbid Curiosity and subsequent number of negative items picked: (r = .66, r2 = .43)") +
  theme(
    plot.caption = element_text(face = "italic",
                                hjust = .2),
     axis.text = element_text(color = "black")
  )

Morbid Curiosity and Gender

Code
# Separate Gender and MC
gen_tt <- 
  mcu_cln %>% 
  select(Gender,Morbid_Curiosity_Overall) %>% 
  filter(Gender %in% 1:2) |> 
  mutate(Gender = 
           ifelse(Gender == 1,"Male","Female"))

t.test(gen_tt$Morbid_Curiosity_Overall~gen_tt$Gender) |> 
  tidy() |> 
    rename(
    t = statistic,
    p = p.value, 
    df = parameter,
    `mean difference` = estimate,
    Female = estimate1,
    Male = estimate2
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 12: Independent t-test shows no significant difference between scores on Morbid Curiosity Scale.") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
mean difference Female Male t p df conf.low conf.high method alternative
-0.228 3.23 3.46 -0.999 0.324 39.3 -0.689 0.233 Welch Two Sample t-test two.sided

Horror Typology and Gender

Code
genht_tt <- 
  mcu_cln %>% 
  select(Gender,Horror_Typology_Overall) %>% 
  filter(Gender %in% 1:2) |> 
  mutate(Gender = 
           ifelse(Gender == 1,"Male","Female")) 

t.test(genht_tt$Horror_Typology_Overall~genht_tt$Gender) |> 
  broom::tidy() |> 
    rename(
    t = statistic,
    p = p.value, 
    df = parameter,
    `mean difference` = estimate,
    Female = estimate1,
    Male = estimate2
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 13: Independent t-test shows no significant difference between scores on Horror Typology Scale.") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
mean difference Female Male t p df conf.low conf.high method alternative
-0.0118 3.47 3.48 -0.076 0.94 62.5 -0.323 0.299 Welch Two Sample t-test two.sided

General Questions:

I consider myself to be morbidly curious.

Code
gen_quest <- 
  mcu_cln |> 
  clean_names() |> 
  rename(
    people_mc = a_d1, 
    self_mc = a_d2, 
    self_anx = a_d3,
    self_hor = a_d4
  ) |> 
  mutate_at(vars(people_mc:self_hor),as.numeric)

cor.test(gen_quest$self_mc,gen_quest$morbid_curiosity_overall) |> 
  tidy() |> 
  rename(
    df = parameter,
    p = p.value,
    t = statistic,
    r = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 14: Participants self rating of being morbidly curious was significantlly positivly correlated with morbid curiosity score") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
r t p df conf.low conf.high method alternative
0.525 5.89 6.4e-08 91 0.36 0.659 Pearson's product-moment correlation two.sided

I consider myself to enjoy horror related content such Books, TV shows, Movies, Attractions, Podcast, etc.

Code
cor.test(gen_quest$self_hor,gen_quest$horror_typology_overall) |> 
    tidy() |>
    rename(
    df = parameter,
    p = p.value,
    t = statistic,
    r = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 15: Participants self rating of enjoying the horror genre was significantlly positivly correlated with horror typology score") |> 
  # set_caption_pos("bottom")|> 
  set_width(value = 1)
r t p df conf.low conf.high method alternative
0.521 5.82 8.87e-08 91 0.354 0.655 Pearson's product-moment correlation two.sided

I consider myself to be an anxious person.

Code
cor.test(gen_quest$self_anx, gen_quest$trait) |> 
    tidy() |>   
  rename(
    df = parameter,
    p = p.value,
    t = statistic,
    r = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 16: Participants self rating of anxiety was significantly positively correlated with the trait measure of anxiety.") |> 
  set_caption_pos("bottom")|> 
  set_width(value = 1)
r t p df conf.low conf.high method alternative
0.11 1.06 0.292 91 -0.0955 0.307 Pearson's product-moment correlation two.sided

I find people to be morbidly curious.

Code
t.test(gen_quest$people_mc, mu = 4, alternative = "greater") |> 
    broom::tidy() |> 
    rename(
    t = statistic,
    p = p.value, 
    df = parameter,
    `mean difference` = estimate
  ) |> 
  hux() |> 
  theme_article() |> 
  # set_caption("Table 17: Single-sample t-test against the midpoint of 4 showed that people generally believe others to be morbidly curious.") |> 
  set_caption_pos("bottom") |> 
  set_width(value = 1)
mean difference t p df conf.low conf.high method alternative
4.73 4.44 1.26e-05 92 4.46 Inf One Sample t-test greater

Descriptive Statistics of All Variables

Code
gen_quest |> 
  select(age,self_mc:trait_questionnaire_20,
         post_state_1:post_state_20,
         morbid_curiosity_overall:state_post,-pn_choice) |>
  mutate_all(as.numeric) |> 
  rename_at(vars(starts_with("state_")),~gsub("questionnaire_","",.x)) |> 
    rename_at(vars(starts_with("trait_")),~gsub("questionnaire_","",.x)) |> 
  relocate(state_pre, .before = trait) |> 
  relocate(state_post, .before = trait) |> 
  skim() |> 
  yank("numeric") |> 
  select(-starts_with("p"), 
         -n_missing, 
         -complete_rate) |> 
  rename( 
    variable = skim_variable,
    M = mean, 
    SD = sd
    ) |>
  mutate(
    `M(SD)` = paste0(round(M,2),"(",round(SD,2),")")) |> 
  select(-M,-SD) |>
  select(variable, `M(SD)`, hist) |> 
  hux() |> 
  theme_article() |> 
  set_width(value = 1) |> 
  set_align(col = 3, value = "center")
variable M(SD) hist
age 21.86(5.4) ▇▁▁▁▁
self_mc 4.6(1.92) ▃▁▂▃▇
self_anx 5.27(1.63) ▁▂▁▃▇
self_hor 4.31(2.15) ▆▂▂▃▇
b1 2.51(1.65) ▇▂▂▁▁
b2 3.8(1.72) ▇▃▇▇▆
b3 2.97(1.7) ▇▂▃▂▂
b4 2.57(1.62) ▇▁▂▂▁
b5 2.62(1.57) ▇▁▃▁▁
b6 3.49(1.65) ▇▂▇▅▃
m1 3.69(1.79) ▇▂▃▃▅
m2 4.49(1.58) ▃▂▂▆▇
m3 4.45(1.37) ▅▂▇▇▇
m4 4.54(1.51) ▃▂▅▆▇
m5 4.47(1.53) ▃▁▅▅▇
m6 4.51(1.46) ▃▂▆▆▇
p1 4.4(1.55) ▅▂▆▇▇
p2 2.84(1.66) ▇▂▃▂▁
p3 3.9(1.6) ▇▂▇▆▆
p4 2.96(1.61) ▇▃▅▂▂
p5 3.39(1.73) ▇▃▅▃▃
v1 2.11(1.4) ▇▁▂▁▁
v2 2.94(1.72) ▇▂▃▂▂
v3 3(1.52) ▇▃▅▃▁
v4 3.35(1.66) ▇▂▇▃▂
v5 3.3(1.62) ▇▅▆▃▃
v6 3.42(1.64) ▇▃▆▃▃
aj1 3.41(1.98) ▇▂▂▃▃
aj2 3.74(2.17) ▇▁▂▂▆
aj3 3.27(2.07) ▇▂▂▁▃
aj4 4.39(2.16) ▆▁▁▃▇
aj5 3.1(2.03) ▇▁▂▂▂
aj6 4.13(2.12) ▇▁▂▅▇
wk1 3.75(2.03) ▇▁▃▅▅
wk2 4.52(1.95) ▆▁▂▆▇
wk3 4.66(1.93) ▅▁▂▃▇
wk4 2.6(1.88) ▇▁▁▁▂
wk5 4.76(1.71) ▃▂▂▇▇
wk6 4.38(1.89) ▅▂▅▃▇
dc1 1.86(1.32) ▇▁▁▁▁
dc2 2.45(1.71) ▇▁▂▁▁
dc3 1.89(1.32) ▇▁▁▁▁
dc4 3.76(1.9) ▇▂▇▅▅
dc5 3.59(1.75) ▇▁▇▃▃
dc6 3.3(1.92) ▇▁▅▂▂
dc7 2.25(1.45) ▇▁▂▁▁
state_1 2.96(0.91) ▁▆▁▇▇
state_2 2.94(0.93) ▁▇▁▇▇
state_3 1.86(0.88) ▇▇▁▃▁
state_4 1.72(0.83) ▇▅▁▃▁
state_5 2.59(0.94) ▃▆▁▇▃
state_6 1.35(0.75) ▇▂▁▁▁
state_7 1.98(1.02) ▇▅▁▃▂
state_8 2.6(0.97) ▃▇▁▇▅
state_9 1.23(0.57) ▇▁▁▁▁
state_10 2.98(0.82) ▁▆▁▇▆
state_11 2.62(1.04) ▅▇▁▇▆
state_12 1.72(0.88) ▇▅▁▂▁
state_13 1.72(0.9) ▇▆▁▁▁
state_14 2.03(1.09) ▇▃▁▃▂
state_15 2.76(0.93) ▂▇▁▇▆
state_16 2.68(0.86) ▂▇▁▇▃
state_17 1.69(0.91) ▇▅▁▁▁
state_18 1.6(0.93) ▇▂▁▂▁
state_19 2.54(1.06) ▆▇▁▇▆
state_20 2.58(0.96) ▃▇▁▇▅
trait_1 2.69(0.94) ▂▇▁▆▅
trait_2 2.3(1) ▆▇▁▆▃
trait_3 2.45(0.92) ▂▇▁▆▃
trait_4 2.46(1.09) ▆▇▁▅▆
trait_5 1.85(0.95) ▇▆▁▂▂
trait_6 2.04(0.97) ▇▆▁▆▂
trait_7 2.61(0.93) ▃▇▁▇▅
trait_8 2.24(1.09) ▇▇▁▅▅
trait_9 2.86(1.09) ▂▆▁▃▇
trait_10 2.74(0.92) ▂▇▁▇▆
trait_11 1.48(0.73) ▇▃▁▁▁
trait_12 2.29(1.06) ▆▇▁▅▅
trait_13 2.72(0.96) ▂▇▁▅▆
trait_14 2.18(0.95) ▆▇▁▆▂
trait_15 1.87(0.95) ▇▆▁▃▂
trait_16 2.74(0.91) ▂▇▁▇▆
trait_17 2.35(1.04) ▇▇▁▇▅
trait_18 2.42(1.11) ▇▇▁▇▆
trait_19 2.63(0.96) ▃▆▁▇▅
trait_20 2.35(1.08) ▇▅▁▇▃
post_state_1 2.84(0.9) ▁▇▁▆▆
post_state_2 2.85(0.91) ▁▇▁▆▆
post_state_3 1.89(0.96) ▇▇▁▂▂
post_state_4 1.63(0.89) ▇▃▁▂▁
post_state_5 2.56(0.96) ▃▇▁▇▅
post_state_6 1.32(0.63) ▇▂▁▁▁
post_state_7 1.94(1) ▇▅▁▃▂
post_state_8 2.58(0.99) ▃▇▁▇▅
post_state_9 1.3(0.6) ▇▁▂▁▁
post_state_10 2.81(0.91) ▂▇▁▇▆
post_state_11 2.46(1.07) ▆▇▁▇▆
post_state_12 1.65(0.8) ▇▅▁▂▁
post_state_13 1.62(0.85) ▇▅▁▁▁
post_state_14 1.89(0.96) ▇▇▁▂▂
post_state_15 2.7(0.96) ▃▇▁▇▆
post_state_16 2.63(0.93) ▂▇▁▇▅
post_state_17 1.66(0.77) ▇▇▁▁▁
post_state_18 1.55(0.83) ▇▃▁▁▁
post_state_19 2.56(1.08) ▆▇▁▇▇
post_state_20 2.61(1) ▃▇▁▇▆
morbid_curiosity_overall 3.32(0.9) ▃▇▇▇▅
mind 4.36(1.3) ▂▃▃▆▇
body 2.99(1.23) ▇▇▇▅▃
violence 3.02(1.24) ▆▆▇▃▂
paranormal 2.91(1.11) ▅▅▇▇▂
horror_typology_overall 3.46(0.75) ▃▇▆▂▁
aj 3.15(1.52) ▇▅▆▅▅
wk 3.52(1.19) ▂▆▇▅▃
dc 2.73(0.98) ▃▇▅▂▁
state_pre 44.15(6.73) ▁▃▇▅▁
state_post 43.05(7.64) ▂▆▇▂▁
trait 47.3(6.8) ▆▇▇▃▂
positive_choice 10.38(3.45) ▁▆▇▇▇
negative_choice 5.52(3.52) ▇▇▇▃▁
Code
  # set_caption("Table 18: Descriptive statistics of all numeric variables.")