library(emmeans)
library(tidyverse)
library(dplyr)
library(car)
library(stringr)
library(lmerTest)
library(MASS)
library(ordinal)
library(MuMIn)
library(psych)
library(effectsize)
library(jtools)
library(binom)
STUDY 1 - PREPROCESSING
##### Import data
raw_data_1 = read.csv('asd1_merged.csv')
##### Clean data
##### Exclude according to 3 criteria:
##### 1) Fail to complete study (or took it multiple times);
length(unique(raw_data_1$PROLIFIC_PID)) # 403 different people took the survey
## [1] 403
max(table(raw_data_1$PROLIFIC_PID)) # The max number of trials a participant had was 32
## [1] 32
sum(table(raw_data_1$PROLIFIC_PID) == 20) # 398 participants had 20 trials
## [1] 398
table(raw_data_1$PROLIFIC_PID)[table(raw_data_1$PROLIFIC_PID) != 20] # 5 had a different number of trials
##
## 5589cdf8fdf99b18bd86d031 5e55463217d334029c5d9a35 62029c754dc28df6b6246e96
## 1 4 4
## 667ad3a189817cf129ae61e6 66e06758c868de3476e86e02
## 32 26
# I keep only those with 20 trials (to avoid incomplete runs and participants taking the survey multiple times)
clean_data_1 <- raw_data_1[raw_data_1$PROLIFIC_PID %in% names(table(raw_data_1$PROLIFIC_PID)[table(raw_data_1$PROLIFIC_PID) == 20]), ]
any(table(clean_data_1$PROLIFIC_PID) != 20)
## [1] FALSE
length(unique(clean_data_1$PROLIFIC_PID)) # 398 participants are left
## [1] 398
##### 2) Complete whole study in < 120 seconds (minimum completion time)
# I cannot find this info in the data, so I do not perform exclusions
##### 3) Provide four or more responses in under 500 milliseconds (minimum response time, for each DV)
# Violation judgment
sum(clean_data_1$rt < 500, na.rm = TRUE) # There are 6 trials with rt < 500 ms for violation judgments
## [1] 6
table(clean_data_1$PROLIFIC_PID[clean_data_1$rt < 500]) # 5 of those trials come from 1 participant alone
##
## 66904bbd1eb9bb790b9b0f6f 66bf5fde0a42438253e0d94e
## 5 1
# Self-reported confidence
sum(clean_data_1$rt_conf < 500, na.rm = TRUE) # There are 0 trials with rt < 500 ms for self-reported confidence judgments
## [1] 0
# Literal and Moral judgments
sum(clean_data_1$lm_rt < 500, na.rm = TRUE) # There are 0 trials with rt < 500 ms for literal & moral judgments
## [1] 0
# I exclude that 1 participant with 5 trials < 500 ms (all 20 trials, not just those 5)
clean_data_1 <- clean_data_1[!(clean_data_1$PROLIFIC_PID %in%
names(table(clean_data_1$PROLIFIC_PID[clean_data_1$rt < 500])[
table(clean_data_1$PROLIFIC_PID[clean_data_1$rt < 500]) >= 4
])), ]
length(unique(clean_data_1$PROLIFIC_PID)) # 397 participants are left
## [1] 397
##### Additionally exclude those whose Prolific says they are ASD, but they claim they are not
#sum(clean_data_1$group == "ASD" &
# !str_detect(clean_data_1$comorbidities, fixed("ASD (Autism Spectrum Disorder)")),
# na.rm = TRUE) # 1038 rows (52 participants, with 1 showing 18 instead of 20 trials because they seemingly did not answer the comorbidity question at all)
#
#unique(clean_data_1$PROLIFIC_PID[
# clean_data_1$group == "ASD" &
# !str_detect(clean_data_1$comorbidities, fixed("ASD (Autism Spectrum Disorder)"))
#])
#
#table(clean_data_1$PROLIFIC_PID[
# clean_data_1$group == "ASD" &
# !str_detect(clean_data_1$comorbidities, fixed("ASD (Autism Spectrum Disorder)"))
#])
# I exclude those 52 participants
#clean_data_1 <- clean_data_1[!(
# clean_data_1$PROLIFIC_PID %in% unique(clean_data_1$PROLIFIC_PID[
# clean_data_1$group == "ASD" &
# !str_detect(clean_data_1$comorbidities, fixed("ASD (Autism Spectrum Disorder)"))
# ])
#), ]
#length(unique(clean_data_1$PROLIFIC_PID)) # 345 participants are left
# Make categorical variables factors
str(clean_data_1$gender)
## chr [1:7940] "Female" "Female" "Female" "Female" "Female" "Female" ...
clean_data_1$gender <- factor(clean_data_1$gender)
str(clean_data_1$gender)
## Factor w/ 5 levels "","Female","Male",..: 2 2 2 2 2 2 2 2 2 2 ...
str(clean_data_1$group)
## chr [1:7940] "NT" "NT" "NT" "NT" "NT" "NT" "NT" "NT" "NT" "NT" "NT" "NT" ...
clean_data_1$group <- factor(clean_data_1$group, levels = c("NT", "ASD"))
str(clean_data_1$group)
## Factor w/ 2 levels "NT","ASD": 1 1 1 1 1 1 1 1 1 1 ...
str(clean_data_1$text)
## int [1:7940] 1 0 0 1 0 1 0 1 0 0 ...
clean_data_1$text <- factor(clean_data_1$text)
str(clean_data_1$text)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 1 1 ...
str(clean_data_1$purpose)
## int [1:7940] 0 1 0 1 1 0 0 1 0 1 ...
clean_data_1$purpose <- factor(clean_data_1$purpose)
str(clean_data_1$purpose)
## Factor w/ 2 levels "0","1": 1 2 1 2 2 1 1 2 1 2 ...
str(clean_data_1$letter_response)
## int [1:7940] 5 0 0 5 0 5 0 5 0 0 ...
clean_data_1$letter_response <- factor(clean_data_1$letter_response)
str(clean_data_1$letter_response)
## Factor w/ 6 levels "0","1","2","3",..: 6 1 1 6 1 6 1 6 1 1 ...
class(clean_data_1$letter_response)
## [1] "factor"
clean_data_1$letter_response <- as.ordered(clean_data_1$letter_response)
class(clean_data_1$letter_response)
## [1] "ordered" "factor"
str(clean_data_1$moral_response)
## int [1:7940] 4 3 0 4 4 0 0 5 0 4 ...
clean_data_1$moral_response <- factor(clean_data_1$moral_response)
str(clean_data_1$moral_response)
## Factor w/ 6 levels "0","1","2","3",..: 5 4 1 5 5 1 1 6 1 5 ...
class(clean_data_1$moral_response)
## [1] "factor"
clean_data_1$moral_response <- as.ordered(clean_data_1$moral_response)
class(clean_data_1$moral_response)
## [1] "ordered" "factor"
# Recompute cw_resp with the new formula (response_confidence * (response - .5) * 2)
clean_data_1 <- clean_data_1 %>%
mutate(cw_resp = confidence * (response - 0.5) * 2)
# Anonymize participants’ ID
clean_data_1$subject_nr <- match(clean_data_1$PROLIFIC_PID,
unique(clean_data_1$PROLIFIC_PID))
clean_data_1$PROLIFIC_PID <- NULL
# Correct columns’ names
clean_data_1 <- clean_data_1 %>%
rename(scene = rule)
# Create binary comorbidity flags
str(clean_data_1)
## 'data.frame': 7940 obs. of 37 variables:
## $ condition : chr "purpose_comply_text_violate" "purpose_violate_text_comply" "purpose_and_text_comply" "purpose_and_text_violate" ...
## $ group : Factor w/ 2 levels "NT","ASD": 1 1 1 1 1 1 1 1 1 1 ...
## $ text : Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 1 1 ...
## $ purpose : Factor w/ 2 levels "0","1": 1 2 1 2 2 1 1 2 1 2 ...
## $ scene : chr "shoes" "shoes" "shoes" "shoes" ...
## $ run_id : int 10 10 10 10 10 10 10 10 10 10 ...
## $ response : int 1 0 0 1 0 1 0 1 1 0 ...
## $ rt : int 4587 6423 5911 3660 4987 5378 6498 2735 5403 5925 ...
## $ confidence : int 97 98 98 98 95 35 96 99 98 30 ...
## $ rt_conf : int 3337 3743 2677 2654 3561 3352 3356 2499 1943 2843 ...
## $ cw_response : num 48.5 -49 -49 49 -47.5 17.5 -48 49.5 49 -15 ...
## $ theory : chr "Spirit" "Spirit" "Spirit" "Spirit" ...
## $ theory_bipolar : int 5 5 5 5 5 5 5 5 5 5 ...
## $ aq1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq2 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq4 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq5 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq6 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq7 : int 3 3 3 3 3 3 3 3 3 3 ...
## $ aq8 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq9 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq10 : int 3 3 3 3 3 3 3 3 3 3 ...
## $ gender : Factor w/ 5 levels "","Female","Male",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ education : chr "Doctoral degree or higher" "Doctoral degree or higher" "Doctoral degree or higher" "Doctoral degree or higher" ...
## $ ethnicity : chr "[\"White\"]" "[\"White\"]" "[\"White\"]" "[\"White\"]" ...
## $ income : chr "$50,000 - $100,000" "$50,000 - $100,000" "$50,000 - $100,000" "$50,000 - $100,000" ...
## $ employment : chr "Employed full-time" "Employed full-time" "Employed full-time" "Employed full-time" ...
## $ comorbidities : chr "[\"None of the above\"]" "[\"None of the above\"]" "[\"None of the above\"]" "[\"None of the above\"]" ...
## $ remarks : chr "" "" "" "" ...
## $ moral_response : Ord.factor w/ 6 levels "0"<"1"<"2"<"3"<..: 5 4 1 5 5 1 1 6 1 5 ...
## $ letter_response: Ord.factor w/ 6 levels "0"<"1"<"2"<"3"<..: 6 1 1 6 1 6 1 6 1 1 ...
## $ theory2 : chr "Spirit" "Spirit" "Spirit" "Spirit" ...
## $ lm_rt : int 5480 8307 8613 4384 6311 5792 6904 3687 5369 10675 ...
## $ aq_score : num 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ...
## $ cw_resp : num 97 -98 -98 98 -95 35 -96 99 98 -30 ...
## $ subject_nr : int 1 1 1 1 1 1 1 1 1 1 ...
clean_data_1 <- clean_data_1 %>%
mutate(
autism = factor(as.integer(str_detect(comorbidities, fixed("ASD (Autism Spectrum Disorder)")))),
adhd = factor(as.integer(str_detect(comorbidities, fixed("Attention Deficit Hyperactivity Disorder")))),
anxiety = factor(as.integer(str_detect(comorbidities, fixed("Anxiety Disorder")))),
depression = factor(as.integer(str_detect(comorbidities, fixed("Depression")))),
ocd = factor(as.integer(str_detect(comorbidities, fixed("Obsessive-Compulsive Disorder")))),
epilepsy = factor(as.integer(str_detect(comorbidities, fixed("Epilepsy or Seizure Disorder")))),
gastrointestinal = factor(as.integer(str_detect(comorbidities, fixed("Gastrointestinal Issues")))),
sleep_dis = factor(as.integer(str_detect(comorbidities, fixed("Sleep Disorders")))),
sensory_process_dis = factor(as.integer(str_detect(comorbidities, fixed("Sensory Processing Disorder")))),
learn_dis = factor(as.integer(str_detect(comorbidities, fixed("Learning Disability (e.g., Dyslexia)"))))
)
# Make rule violation judgment a factor
str(clean_data_1$response)
## int [1:7940] 1 0 0 1 0 1 0 1 1 0 ...
clean_data_1$response <- factor(clean_data_1$response)
# Recompute AQ-10 scores
# 0 = Definitely Agree
# 1 = Slightly Agree
# 2 = Slightly Disagree
# 3 = Definitely Disagree
# SCORING: Only 1 point can be scored for each question. Score 1 point for Definitely or
# Slightly Agree on each of items 1, 7, 8, and 10. Score 1 point for Definitely or Slightly
# Disagree on each of items 2, 3, 4, 5, 6, and 9. If the individual scores 6 or above, consider
# referring them for a specialist diagnostic assessment.
clean_data_1 <- clean_data_1 %>%
rename(aq_score_old = aq_score)
clean_data_1$aq_score <-
rowSums(sapply(clean_data_1[c("aq1","aq7","aq8","aq10")], function(x) x %in% c(0,1))) +
rowSums(sapply(clean_data_1[c("aq2","aq3","aq4","aq5","aq6","aq9")], function(x) x %in% c(2,3)))
str(clean_data_1$aq_score)
## num [1:7940] 2 2 2 2 2 2 2 2 2 2 ...
# Reverse code items 2, 3, 4, 5, 6, and 9.
items_to_invert <- c("aq2", "aq3", "aq4", "aq5", "aq6", "aq9")
for (item in items_to_invert) {
new_col <- paste0(item, "_inv")
clean_data_1[[new_col]] <- 3 - clean_data_1[[item]]
}
# Fix row names/numbers
tail(rownames(clean_data_1))
## [1] "8022" "8023" "8024" "8025" "8026" "8027"
rownames(clean_data_1) <- NULL
table(clean_data_1$group)/20 # There is something wrong with a participant in the ASD group: it is due to NAs
##
## NT ASD
## 200.0 196.9
##### Deal with participants with NAs (hence those who skipped questions or took only 1/2 surveys)
# Remove the column remarks, since it obviously has many NAs
clean_data_1$remarks <- NULL
# Identify NA columns per participant
na_summary <- clean_data_1 %>%
group_by(subject_nr) %>%
summarise(
na_columns = list(names(.)[sapply(across(everything()), function(x) any(is.na(x)))]),
.groups = "drop"
) %>%
# Keep only participants with at least one NA
dplyr::filter(lengths(na_columns) > 0) %>%
# Convert the list of NA columns into a single string per participant
dplyr::mutate(na_columns_str = sapply(na_columns, paste, collapse = ", ")) %>%
dplyr::select(subject_nr, na_columns_str)
glimpse(na_summary)
## Rows: 80
## Columns: 2
## $ subject_nr <int> 9, 12, 14, 17, 27, 29, 30, 31, 37, 46, 54, 57, 64, 67, …
## $ na_columns_str <chr> "moral_response, letter_response, theory2, lm_rt", "mor…
# Most of them simply did not take Part 2 of Study 1 (78/80), but some have other missing values as well (2/80)
na_summary %>%
filter(
sapply(str_split(na_columns_str, ",\\s*"), function(cols) any(!cols %in% c("moral_response", "letter_response", "theory2", "lm_rt")))
) %>%
nrow() # I confirm there are 2 who somehow did not complete the survey
## [1] 2
# Who are these 2?
na_summary$subject_nr[
sapply(str_split(na_summary$na_columns_str, ",\\s*"),
function(cols) any(!cols %in% c("moral_response", "letter_response", "theory2", "lm_rt")))
]
## [1] 391 397
# Which rows in clean_data_1 do they correspond to?
which(clean_data_1$subject_nr == "391") # The rows are not continuous, so they quit and rejoined the survey, but did not finish answering
## [1] 7801 7802 7803 7804 7805 7806 7807 7808 7809 7810 7811 7812 7813 7814 7815
## [16] 7816 7817 7818 7939 7940
which(clean_data_1$subject_nr == "397") # The rows are continuous, so they just skipped questions
## [1] 7919 7920 7921 7922 7923 7924 7925 7926 7927 7928 7929 7930 7931 7932 7933
## [16] 7934 7935 7936 7937 7938
# I exclude data from those two participants
clean_data_1 <- clean_data_1 %>%
filter(!subject_nr %in% c("391", "397"))
# Rename clean_data_1 to clean_data_1a (containing all people who took Part 1 of Study 1)
clean_data_1a <- clean_data_1
rm(clean_data_1)
length(unique(clean_data_1a$subject_nr)) # 395 participants are left
## [1] 395
table(clean_data_1a$group)/20 # 200 NT and 195 ASD took Part 1 of Study 1
##
## NT ASD
## 200 195
# Create a second dataset, named clean_data_1b, containing only participants who took both Part 1 and Part 2
clean_data_1b <- clean_data_1a %>%
filter(!subject_nr %in% na_summary$subject_nr)
length(unique(clean_data_1b$subject_nr)) # 317 participants are left
## [1] 317
table(clean_data_1b$group)/20 # 164 NT and 153 ASD took Part 1 of Study 1
##
## NT ASD
## 164 153
##### Compute Cronbach’s alpha of AQ-10 for Study 1
alpha_result <- psych::alpha(clean_data_1a[, c("aq1", "aq2_inv", "aq3_inv", "aq4_inv", "aq5_inv", "aq6_inv",
"aq7", "aq8", "aq9_inv", "aq10")])
alpha_result$total$raw_alpha
## [1] 0.7986231
##### Create cases variable
clean_data_1a <- clean_data_1a %>%
mutate(
case = case_when(
text == 0 & purpose == 0 ~ "compl",
text == 1 & purpose == 1 ~ "viol",
text == 1 & purpose == 0 ~ "over",
text == 0 & purpose == 1 ~ "under"
),
case = factor(case, levels = c("compl", "viol", "over", "under"))
)
clean_data_1b <- clean_data_1b %>%
mutate(
case = case_when(
text == 0 & purpose == 0 ~ "compl",
text == 1 & purpose == 1 ~ "viol",
text == 1 & purpose == 0 ~ "over",
text == 0 & purpose == 1 ~ "under"
),
case = factor(case, levels = c("compl", "viol", "over", "under"))
)
STUDY 2 - PREPROCESSING
##### Import data
raw_data_2 = bind_rows(read.csv('study2a-batch1-asd.csv') %>%
mutate(group = 'ASD'),
read.csv('study2a-batch2-nt2.csv') %>%
mutate(group = 'NT'))
##### Rename variables, keep only relevant columns, and create comorbidity flags
clean_data_2 = raw_data_2 %>%
mutate(relabel_literal = str_detect(stimulus, "<div>\n <p>How"),
relabel_upset = str_detect(stimulus, "<div>\n <p style=\"color: grey;\">How")) %>%
mutate(task = if_else(relabel_literal, 'upset_rating',
if_else(relabel_upset, 'literal_meaning', task)))
clean_data_2 <- clean_data_2 %>%
filter(task %in% c('literal_meaning', 'upset_rating', 'violation') | str_detect(stimulus, 'confident')) %>%
mutate(task = if_else(str_detect(stimulus, 'confident'), 'confidence', task)) %>%
dplyr::select(group, gender, comorbidities, purpose_present, lateralization,
trial_index, rule, condition, run_id, PROLIFIC_PID, task, rt, response,
aq1, aq2, aq3, aq4, aq5, aq6, aq7, aq8, aq9, aq10) %>%
mutate(condition = na_if(condition, "1"),
rule = na_if(rule, ""),
rt = as.numeric(rt)) %>% # Convert "" to NA
fill(condition, .direction = "down") %>%
fill(rule, .direction = "down") %>%
pivot_wider(names_from = 'task', values_from = c('trial_index', 'response', 'rt')) %>%
mutate(response = case_when(lateralization == 'Left0' ~ 1 - as.numeric(response_violation),
lateralization == 'Right1' ~ as.numeric(response_violation)),
response_confidence = as.numeric(response_confidence),
response_literal_meaning = 1 - as.numeric(response_literal_meaning),
response_upset_rating = as.numeric(response_upset_rating),
text = case_when(condition %in% c('purpose_and_text_violate',
'purpose_comply_text_violate') ~ 1,
condition %in% c('purpose_and_text_comply',
'purpose_violate_text_comply') ~ 0),
purpose = case_when(condition %in% c('purpose_and_text_comply',
'purpose_comply_text_violate') ~ 0,
condition %in% c('purpose_and_text_violate',
'purpose_violate_text_comply') ~ 1),
cw_resp = response_confidence * (response - .5) * 2) %>%
group_by(run_id, PROLIFIC_PID) %>%
mutate(trial = dense_rank(trial_index_violation)) %>%
ungroup() %>%
mutate(purpose_display = case_when(purpose_present == 'Block1' & trial <= 16 ~ 1,
purpose_present == 'Block1' & trial > 16 ~ 0,
purpose_present == 'Block2' & trial <= 16 ~ 0,
purpose_present == 'Block2' & trial > 16 ~ 1))
clean_data_2 <- clean_data_2 %>%
rowwise() %>%
mutate(aq_score = sum(c_across(aq1:aq10), na.rm = TRUE)) %>%
ungroup() %>%
mutate(
# Binary comorbidity flags as 0/1 factors
autism = factor(as.integer(str_detect(comorbidities, fixed("ASD (Autism Spectrum Disorder)")))),
adhd = factor(as.integer(str_detect(comorbidities, fixed("Attention Deficit Hyperactivity Disorder")))),
anxiety = factor(as.integer(str_detect(comorbidities, fixed("Anxiety Disorder")))),
depression = factor(as.integer(str_detect(comorbidities, fixed("Depression")))),
ocd = factor(as.integer(str_detect(comorbidities, fixed("Obsessive-Compulsive Disorder")))),
epilepsy = factor(as.integer(str_detect(comorbidities, fixed("Epilepsy or Seizure Disorder")))),
gastrointestinal = factor(as.integer(str_detect(comorbidities, fixed("Gastrointestinal Issues")))),
sleep_dis = factor(as.integer(str_detect(comorbidities, fixed("Sleep Disorders")))),
sensory_process_dis = factor(as.integer(str_detect(comorbidities, fixed("Sensory Processing Disorder")))),
learn_dis = factor(as.integer(str_detect(comorbidities, fixed("Learning Disability (e.g., Dyslexia)")))),
group = factor(group), # NT vs ASD
gender = factor(gender) # ensure categorical
)
clean_data_2 <- clean_data_2 %>%
left_join(
raw_data_2 %>%
group_by(PROLIFIC_PID) %>%
summarize(time_elapsed = max(time_elapsed, na.rm = TRUE)),
by = "PROLIFIC_PID"
)
# Make rule violation judgment a factor
str(clean_data_2$response)
## num [1:13009] 0 0 1 0 1 1 1 0 1 1 ...
clean_data_2$response <- factor(clean_data_2$response)
str(clean_data_2$response)
## Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 1 2 2 ...
##### Exclude according to 3 criteria:
##### 1) Fail to complete study (or took it multiple times);
length(unique(clean_data_2$PROLIFIC_PID)) # 417 different people took the survey
## [1] 417
table(table(clean_data_2$PROLIFIC_PID)) # 384 participants have 32 rows (trials)
##
## 1 3 4 8 9 15 16 18 19 27 28 32 33 34 35 36 39 42 44 49
## 5 1 4 1 2 1 1 2 1 1 2 384 1 1 2 1 1 2 1 2
## 64
## 1
# I keep only those with 32 trials (to avoid incomplete runs and participants taking the survey multiple times)
clean_data_2 <- clean_data_2[clean_data_2$PROLIFIC_PID %in% names(table(clean_data_2$PROLIFIC_PID)[table(clean_data_2$PROLIFIC_PID) == 32]), ]
any(table(clean_data_2$PROLIFIC_PID) != 32)
## [1] FALSE
length(unique(clean_data_2$PROLIFIC_PID)) # 384 participants are left
## [1] 384
##### 2) Complete whole study in < 180 seconds (minimum completion time)
sum(clean_data_2$time_elapsed < 180000) # No participant completed the study in < 180 seconds
## [1] 0
##### 3) Provide four or more responses in under 500 milliseconds (minimum response time)
# Violation judgment
sum(clean_data_2$rt_violation < 500, na.rm = TRUE) # There are 6 trials with rt < 500 ms for violation judgments
## [1] 6
# Self-reported confidence
sum(clean_data_2$rt_confidence < 500, na.rm = TRUE) # 0
## [1] 0
# Literal judgments
sum(clean_data_2$rt_literal_meaning < 500, na.rm = TRUE) # 1
## [1] 1
# Upsetness judgments
sum(clean_data_2$rt_upset_rating < 500, na.rm = TRUE) # 4
## [1] 4
# Who are these people and how many trials with rt < 500 ms are they associated with?
clean_data_2 %>%
mutate(fast_count = (rt_violation < 500) +
(rt_confidence < 500) +
(rt_literal_meaning < 500) +
(rt_upset_rating < 500)) %>%
group_by(PROLIFIC_PID) %>%
summarise(n_occurrences = sum(fast_count)) %>%
filter(n_occurrences > 0)
## # A tibble: 8 × 2
## PROLIFIC_PID n_occurrences
## <chr> <int>
## 1 5be44162fa676700011d80d7 2
## 2 6128188a420f8d3f27f8d8b1 1
## 3 63d3fa5e6c990bc96e11beec 1
## 4 66ba4608bc582e7db54007e5 3
## 5 671bbaf1bec15d847b871b9b 1
## 6 672e5f7de203449f90bf93a0 1
## 7 6735b2a6112cf17b7b8df8d1 1
## 8 677b2d64aaa09f45f5a53d3e 1
# No participant had more than 4 trials with rt < 500 ms, so no exclusions are required
##### Additionally exclude those whose Prolific says they are ASD, but they claim they are not
#sum(clean_data_2$group == "ASD" &
# !str_detect(clean_data_2$comorbidities, fixed("ASD (Autism Spectrum Disorder)")),
# na.rm = TRUE) # 3712 rows (116 participants)
#sum(clean_data_2$group == "ASD" & clean_data_2$autism == 0, na.rm = TRUE) # I confirm 3712 rows (116 participants)
#clean_data_2 <- clean_data_2[!(clean_data_2$group == "ASD" & clean_data_2$autism == 0), ]
#length(unique(clean_data_2$PROLIFIC_PID)) # 268 participants are left
# Make categorical variables factors
str(clean_data_2$text)
## num [1:12288] 1 0 0 1 1 0 1 0 1 1 ...
clean_data_2$text <- factor(clean_data_2$text)
str(clean_data_2$text)
## Factor w/ 2 levels "0","1": 2 1 1 2 2 1 2 1 2 2 ...
str(clean_data_2$purpose)
## num [1:12288] 1 0 1 0 1 1 0 0 1 0 ...
clean_data_2$purpose <- factor(clean_data_2$purpose)
str(clean_data_2$purpose)
## Factor w/ 2 levels "0","1": 2 1 2 1 2 2 1 1 2 1 ...
str(clean_data_2$purpose_display)
## num [1:12288] 1 1 1 1 1 1 1 1 1 1 ...
clean_data_2$purpose_display <- factor(clean_data_2$purpose_display)
str(clean_data_2$purpose_display)
## Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
str(clean_data_2$response_upset_rating)
## num [1:12288] 3 3 3 0 3 3 1 0 3 2 ...
clean_data_2$response_upset_rating <- factor(clean_data_2$response_upset_rating)
str(clean_data_2$response_upset_rating)
## Factor w/ 4 levels "0","1","2","3": 4 4 4 1 4 4 2 1 4 3 ...
class(clean_data_2$response_upset_rating)
## [1] "factor"
clean_data_2$response_upset_rating <- as.ordered(clean_data_2$response_upset_rating)
class(clean_data_2$response_upset_rating)
## [1] "ordered" "factor"
# Anonymize participants’ ID
clean_data_2$subject_nr <- match(clean_data_2$PROLIFIC_PID,
unique(clean_data_2$PROLIFIC_PID))
clean_data_2$PROLIFIC_PID <- NULL
# Correct columns’ names
clean_data_2 <- clean_data_2 %>%
rename(scene = rule)
# Create a new column with Upsetness as a factor
clean_data_2$response_upset_factor <- as.factor(clean_data_2$response_upset_rating)
str(clean_data_2$response_upset_factor)
## Ord.factor w/ 4 levels "0"<"1"<"2"<"3": 4 4 4 1 4 4 2 1 4 3 ...
# Recompute AQ-10 scores
# 0 = Definitely Agree
# 1 = Slightly Agree
# 2 = Slightly Disagree
# 3 = Definitely Disagree
# SCORING: Only 1 point can be scored for each question. Score 1 point for Definitely or
# Slightly Agree on each of items 1, 7, 8, and 10. Score 1 point for Definitely or Slightly
# Disagree on each of items 2, 3, 4, 5, 6, and 9. If the individual scores 6 or above, consider
# referring them for a specialist diagnostic assessment.
clean_data_2 <- clean_data_2 %>%
rename(aq_score_old = aq_score)
clean_data_2$aq_score <-
rowSums(sapply(clean_data_2[c("aq1","aq7","aq8","aq10")], function(x) x %in% c(0,1))) +
rowSums(sapply(clean_data_2[c("aq2","aq3","aq4","aq5","aq6","aq9")], function(x) x %in% c(2,3)))
str(clean_data_2$aq_score)
## num [1:12288] 2 2 2 2 2 2 2 2 2 2 ...
# Reverse code items 2, 3, 4, 5, 6, and 9.
items_to_invert <- c("aq2", "aq3", "aq4", "aq5", "aq6", "aq9")
for (item in items_to_invert) {
new_col <- paste0(item, "_inv")
clean_data_2[[new_col]] <- 3 - clean_data_2[[item]]
}
table(clean_data_2$group)/32 # There is something wrong with a participant in the ASD group: it is due to NAs
##
## ASD NT
## 190 194
##### Deal with participants with NAs (hence those who skipped questions or took only 1/2 surveys)
# Identify NA columns per participant
na_summary <- clean_data_2 %>%
group_by(subject_nr) %>%
summarise(
na_columns = list(names(.)[sapply(across(everything()), function(x) any(is.na(x)))]),
.groups = "drop"
) %>%
# Keep only participants with at least one NA
dplyr::filter(lengths(na_columns) > 0) %>%
# Convert the list of NA columns into a single string per participant
dplyr::mutate(na_columns_str = sapply(na_columns, paste, collapse = ", ")) %>%
dplyr::select(subject_nr, na_columns_str)
glimpse(na_summary)
## Rows: 2
## Columns: 2
## $ subject_nr <int> 190, 382
## $ na_columns_str <chr> "aq1, aq2, aq3, aq4, aq5, aq6, aq7, aq8, aq9, aq10, aq_…
# 2 people did not complete the survey.
# I exclude data from those two participants
clean_data_2 <- clean_data_2 %>%
filter(!subject_nr %in% c("190", "382"))
table(clean_data_2$group)/32 # Final N = 382: 189 ASD, 193 NT.
##
## ASD NT
## 189 193
##### Compute Cronbach’s alpha of AQ-10 for Study 2
alpha_result <- psych::alpha(clean_data_2[, c("aq1", "aq2_inv", "aq3_inv", "aq4_inv", "aq5_inv", "aq6_inv",
"aq7", "aq8", "aq9_inv", "aq10")])
alpha_result$total$raw_alpha
## [1] 0.7331627
##### Compute total alpha of AQ-10 for Study 1 and Study 2 together
alpha_columns <- c("aq1", "aq2_inv", "aq3_inv", "aq4_inv", "aq5_inv", "aq6_inv",
"aq7", "aq8", "aq9_inv", "aq10")
cd1_alpha <- clean_data_1a[!duplicated(clean_data_1a$subject_nr), ]
cd1_alpha <- cd1_alpha[, alpha_columns]
cd2_alpha <- clean_data_2[!duplicated(clean_data_2$subject_nr), ]
cd2_alpha <- cd2_alpha[, alpha_columns]
AQ_tot_alpha <- rbind(cd1_alpha, cd2_alpha)
alpha_result <- psych::alpha(AQ_tot_alpha)
alpha_result$total$raw_alpha
## [1] 0.7728161
# Correctly factor the group variable (in conformity with Study 1 data)
levels(clean_data_2$group)
## [1] "ASD" "NT"
clean_data_2$group <- relevel(clean_data_2$group, ref = "NT")
# Recode (1) Literal violation judgments, (2) Moral judgments and (3) Affective inference
levels(clean_data_1a$moral_response)
## [1] "0" "1" "2" "3" "4" "5"
levels(clean_data_1a$letter_response)
## [1] "0" "1" "2" "3" "4" "5"
clean_data_1a$moral_response <-
as.numeric(as.character(clean_data_1a$moral_response)) - 2.5
clean_data_1a$letter_response <-
as.numeric(as.character(clean_data_1a$letter_response)) - 2.5
levels(clean_data_1b$moral_response)
## [1] "0" "1" "2" "3" "4" "5"
levels(clean_data_1b$letter_response)
## [1] "0" "1" "2" "3" "4" "5"
clean_data_1b$moral_response <-
as.numeric(as.character(clean_data_1b$moral_response)) - 2.5
clean_data_1b$letter_response <-
as.numeric(as.character(clean_data_1b$letter_response)) - 2.5
str(clean_data_2$response_upset_rating)
## Ord.factor w/ 4 levels "0"<"1"<"2"<"3": 4 4 4 1 4 4 2 1 4 3 ...
levels(clean_data_2$response_upset_rating)
## [1] "0" "1" "2" "3"
clean_data_2$response_upset_rating <-
as.numeric(as.character(clean_data_2$response_upset_rating)) - 1.5
### Check if Prolific screening is identical to self-reported
# Study 1
str(clean_data_1a)
## 'data.frame': 7900 obs. of 54 variables:
## $ condition : chr "purpose_comply_text_violate" "purpose_violate_text_comply" "purpose_and_text_comply" "purpose_and_text_violate" ...
## $ group : Factor w/ 2 levels "NT","ASD": 1 1 1 1 1 1 1 1 1 1 ...
## $ text : Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 1 1 ...
## $ purpose : Factor w/ 2 levels "0","1": 1 2 1 2 2 1 1 2 1 2 ...
## $ scene : chr "shoes" "shoes" "shoes" "shoes" ...
## $ run_id : int 10 10 10 10 10 10 10 10 10 10 ...
## $ response : Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 2 1 ...
## $ rt : int 4587 6423 5911 3660 4987 5378 6498 2735 5403 5925 ...
## $ confidence : int 97 98 98 98 95 35 96 99 98 30 ...
## $ rt_conf : int 3337 3743 2677 2654 3561 3352 3356 2499 1943 2843 ...
## $ cw_response : num 48.5 -49 -49 49 -47.5 17.5 -48 49.5 49 -15 ...
## $ theory : chr "Spirit" "Spirit" "Spirit" "Spirit" ...
## $ theory_bipolar : int 5 5 5 5 5 5 5 5 5 5 ...
## $ aq1 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq2 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq3 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq4 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq5 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq6 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aq7 : int 3 3 3 3 3 3 3 3 3 3 ...
## $ aq8 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq9 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ aq10 : int 3 3 3 3 3 3 3 3 3 3 ...
## $ gender : Factor w/ 5 levels "","Female","Male",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ education : chr "Doctoral degree or higher" "Doctoral degree or higher" "Doctoral degree or higher" "Doctoral degree or higher" ...
## $ ethnicity : chr "[\"White\"]" "[\"White\"]" "[\"White\"]" "[\"White\"]" ...
## $ income : chr "$50,000 - $100,000" "$50,000 - $100,000" "$50,000 - $100,000" "$50,000 - $100,000" ...
## $ employment : chr "Employed full-time" "Employed full-time" "Employed full-time" "Employed full-time" ...
## $ comorbidities : chr "[\"None of the above\"]" "[\"None of the above\"]" "[\"None of the above\"]" "[\"None of the above\"]" ...
## $ moral_response : num 1.5 0.5 -2.5 1.5 1.5 -2.5 -2.5 2.5 -2.5 1.5 ...
## $ letter_response : num 2.5 -2.5 -2.5 2.5 -2.5 2.5 -2.5 2.5 -2.5 -2.5 ...
## $ theory2 : chr "Spirit" "Spirit" "Spirit" "Spirit" ...
## $ lm_rt : int 5480 8307 8613 4384 6311 5792 6904 3687 5369 10675 ...
## $ aq_score_old : num 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ...
## $ cw_resp : num 97 -98 -98 98 -95 35 -96 99 98 -30 ...
## $ subject_nr : int 1 1 1 1 1 1 1 1 1 1 ...
## $ autism : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ adhd : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ anxiety : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ depression : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ ocd : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ epilepsy : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ gastrointestinal : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ sleep_dis : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ sensory_process_dis: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ learn_dis : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ aq_score : num 2 2 2 2 2 2 2 2 2 2 ...
## $ aq2_inv : num 2 2 2 2 2 2 2 2 2 2 ...
## $ aq3_inv : num 3 3 3 3 3 3 3 3 3 3 ...
## $ aq4_inv : num 2 2 2 2 2 2 2 2 2 2 ...
## $ aq5_inv : num 3 3 3 3 3 3 3 3 3 3 ...
## $ aq6_inv : num 3 3 3 3 3 3 3 3 3 3 ...
## $ aq9_inv : num 2 2 2 2 2 2 2 2 2 2 ...
## $ case : Factor w/ 4 levels "compl","viol",..: 3 4 1 2 4 3 1 2 1 4 ...
all_asd_have_asd <- all(
grepl("ASD", clean_data_1a$comorbidities[clean_data_1a$group == "ASD"])
)
all_asd_have_asd
## [1] FALSE
subset(
clean_data_1a,
group == "ASD" & !grepl("ASD", comorbidities)
)[, c("subject_nr", "group", "comorbidities")] # There are various people with group = ASD but no
## subject_nr group
## 4001 201 ASD
## 4002 201 ASD
## 4003 201 ASD
## 4004 201 ASD
## 4005 201 ASD
## 4006 201 ASD
## 4007 201 ASD
## 4008 201 ASD
## 4009 201 ASD
## 4010 201 ASD
## 4011 201 ASD
## 4012 201 ASD
## 4013 201 ASD
## 4014 201 ASD
## 4015 201 ASD
## 4016 201 ASD
## 4017 201 ASD
## 4018 201 ASD
## 4019 201 ASD
## 4020 201 ASD
## 4261 214 ASD
## 4262 214 ASD
## 4263 214 ASD
## 4264 214 ASD
## 4265 214 ASD
## 4266 214 ASD
## 4267 214 ASD
## 4268 214 ASD
## 4269 214 ASD
## 4270 214 ASD
## 4271 214 ASD
## 4272 214 ASD
## 4273 214 ASD
## 4274 214 ASD
## 4275 214 ASD
## 4276 214 ASD
## 4277 214 ASD
## 4278 214 ASD
## 4279 214 ASD
## 4280 214 ASD
## 4361 219 ASD
## 4362 219 ASD
## 4363 219 ASD
## 4364 219 ASD
## 4365 219 ASD
## 4366 219 ASD
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## 4399 220 ASD
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## 4641 233 ASD
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## 4771 239 ASD
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## 4921 247 ASD
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## 5502 276 ASD
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## 5601 281 ASD
## 5602 281 ASD
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## 6041 303 ASD
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## 6261 314 ASD
## 6262 314 ASD
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## 6270 314 ASD
## 6271 314 ASD
## 6272 314 ASD
## 6273 314 ASD
## 6274 314 ASD
## 6275 314 ASD
## 6276 314 ASD
## 6277 314 ASD
## 6278 314 ASD
## 6279 314 ASD
## 6280 314 ASD
## 6321 317 ASD
## 6322 317 ASD
## 6323 317 ASD
## 6324 317 ASD
## 6325 317 ASD
## 6326 317 ASD
## 6327 317 ASD
## 6328 317 ASD
## 6329 317 ASD
## 6330 317 ASD
## 6331 317 ASD
## 6332 317 ASD
## 6333 317 ASD
## 6334 317 ASD
## 6335 317 ASD
## 6336 317 ASD
## 6337 317 ASD
## 6338 317 ASD
## 6339 317 ASD
## 6340 317 ASD
## 6341 318 ASD
## 6342 318 ASD
## 6343 318 ASD
## 6344 318 ASD
## 6345 318 ASD
## 6346 318 ASD
## 6347 318 ASD
## 6348 318 ASD
## 6349 318 ASD
## 6350 318 ASD
## 6351 318 ASD
## 6352 318 ASD
## 6353 318 ASD
## 6354 318 ASD
## 6355 318 ASD
## 6356 318 ASD
## 6357 318 ASD
## 6358 318 ASD
## 6359 318 ASD
## 6360 318 ASD
## 6401 321 ASD
## 6402 321 ASD
## 6403 321 ASD
## 6404 321 ASD
## 6405 321 ASD
## 6406 321 ASD
## 6407 321 ASD
## 6408 321 ASD
## 6409 321 ASD
## 6410 321 ASD
## 6411 321 ASD
## 6412 321 ASD
## 6413 321 ASD
## 6414 321 ASD
## 6415 321 ASD
## 6416 321 ASD
## 6417 321 ASD
## 6418 321 ASD
## 6419 321 ASD
## 6420 321 ASD
## 6461 324 ASD
## 6462 324 ASD
## 6463 324 ASD
## 6464 324 ASD
## 6465 324 ASD
## 6466 324 ASD
## 6467 324 ASD
## 6468 324 ASD
## 6469 324 ASD
## 6470 324 ASD
## 6471 324 ASD
## 6472 324 ASD
## 6473 324 ASD
## 6474 324 ASD
## 6475 324 ASD
## 6476 324 ASD
## 6477 324 ASD
## 6478 324 ASD
## 6479 324 ASD
## 6480 324 ASD
## 6541 328 ASD
## 6542 328 ASD
## 6543 328 ASD
## 6544 328 ASD
## 6545 328 ASD
## 6546 328 ASD
## 6547 328 ASD
## 6548 328 ASD
## 6549 328 ASD
## 6550 328 ASD
## 6551 328 ASD
## 6552 328 ASD
## 6553 328 ASD
## 6554 328 ASD
## 6555 328 ASD
## 6556 328 ASD
## 6557 328 ASD
## 6558 328 ASD
## 6559 328 ASD
## 6560 328 ASD
## 6601 331 ASD
## 6602 331 ASD
## 6603 331 ASD
## 6604 331 ASD
## 6605 331 ASD
## 6606 331 ASD
## 6607 331 ASD
## 6608 331 ASD
## 6609 331 ASD
## 6610 331 ASD
## 6611 331 ASD
## 6612 331 ASD
## 6613 331 ASD
## 6614 331 ASD
## 6615 331 ASD
## 6616 331 ASD
## 6617 331 ASD
## 6618 331 ASD
## 6619 331 ASD
## 6620 331 ASD
## 6621 332 ASD
## 6622 332 ASD
## 6623 332 ASD
## 6624 332 ASD
## 6625 332 ASD
## 6626 332 ASD
## 6627 332 ASD
## 6628 332 ASD
## 6629 332 ASD
## 6630 332 ASD
## 6631 332 ASD
## 6632 332 ASD
## 6633 332 ASD
## 6634 332 ASD
## 6635 332 ASD
## 6636 332 ASD
## 6637 332 ASD
## 6638 332 ASD
## 6639 332 ASD
## 6640 332 ASD
## 6781 340 ASD
## 6782 340 ASD
## 6783 340 ASD
## 6784 340 ASD
## 6785 340 ASD
## 6786 340 ASD
## 6787 340 ASD
## 6788 340 ASD
## 6789 340 ASD
## 6790 340 ASD
## 6791 340 ASD
## 6792 340 ASD
## 6793 340 ASD
## 6794 340 ASD
## 6795 340 ASD
## 6796 340 ASD
## 6797 340 ASD
## 6798 340 ASD
## 6799 340 ASD
## 6800 340 ASD
## 6981 350 ASD
## 6982 350 ASD
## 6983 350 ASD
## 6984 350 ASD
## 6985 350 ASD
## 6986 350 ASD
## 6987 350 ASD
## 6988 350 ASD
## 6989 350 ASD
## 6990 350 ASD
## 6991 350 ASD
## 6992 350 ASD
## 6993 350 ASD
## 6994 350 ASD
## 6995 350 ASD
## 6996 350 ASD
## 6997 350 ASD
## 6998 350 ASD
## 6999 350 ASD
## 7000 350 ASD
## 7021 352 ASD
## 7022 352 ASD
## 7023 352 ASD
## 7024 352 ASD
## 7025 352 ASD
## 7026 352 ASD
## 7027 352 ASD
## 7028 352 ASD
## 7029 352 ASD
## 7030 352 ASD
## 7031 352 ASD
## 7032 352 ASD
## 7033 352 ASD
## 7034 352 ASD
## 7035 352 ASD
## 7036 352 ASD
## 7037 352 ASD
## 7038 352 ASD
## 7039 352 ASD
## 7040 352 ASD
## 7041 353 ASD
## 7042 353 ASD
## 7043 353 ASD
## 7044 353 ASD
## 7045 353 ASD
## 7046 353 ASD
## 7047 353 ASD
## 7048 353 ASD
## 7049 353 ASD
## 7050 353 ASD
## 7051 353 ASD
## 7052 353 ASD
## 7053 353 ASD
## 7054 353 ASD
## 7055 353 ASD
## 7056 353 ASD
## 7057 353 ASD
## 7058 353 ASD
## 7059 353 ASD
## 7060 353 ASD
## 7161 359 ASD
## 7162 359 ASD
## 7163 359 ASD
## 7164 359 ASD
## 7165 359 ASD
## 7166 359 ASD
## 7167 359 ASD
## 7168 359 ASD
## 7169 359 ASD
## 7170 359 ASD
## 7171 359 ASD
## 7172 359 ASD
## 7173 359 ASD
## 7174 359 ASD
## 7175 359 ASD
## 7176 359 ASD
## 7177 359 ASD
## 7178 359 ASD
## 7179 359 ASD
## 7180 359 ASD
## 7301 366 ASD
## 7302 366 ASD
## 7303 366 ASD
## 7304 366 ASD
## 7305 366 ASD
## 7306 366 ASD
## 7307 366 ASD
## 7308 366 ASD
## 7309 366 ASD
## 7310 366 ASD
## 7311 366 ASD
## 7312 366 ASD
## 7313 366 ASD
## 7314 366 ASD
## 7315 366 ASD
## 7316 366 ASD
## 7317 366 ASD
## 7318 366 ASD
## 7319 366 ASD
## 7320 366 ASD
## 7441 373 ASD
## 7442 373 ASD
## 7443 373 ASD
## 7444 373 ASD
## 7445 373 ASD
## 7446 373 ASD
## 7447 373 ASD
## 7448 373 ASD
## 7449 373 ASD
## 7450 373 ASD
## 7451 373 ASD
## 7452 373 ASD
## 7453 373 ASD
## 7454 373 ASD
## 7455 373 ASD
## 7456 373 ASD
## 7457 373 ASD
## 7458 373 ASD
## 7459 373 ASD
## 7460 373 ASD
## 7461 374 ASD
## 7462 374 ASD
## 7463 374 ASD
## 7464 374 ASD
## 7465 374 ASD
## 7466 374 ASD
## 7467 374 ASD
## 7468 374 ASD
## 7469 374 ASD
## 7470 374 ASD
## 7471 374 ASD
## 7472 374 ASD
## 7473 374 ASD
## 7474 374 ASD
## 7475 374 ASD
## 7476 374 ASD
## 7477 374 ASD
## 7478 374 ASD
## 7479 374 ASD
## 7480 374 ASD
## 7581 380 ASD
## 7582 380 ASD
## 7583 380 ASD
## 7584 380 ASD
## 7585 380 ASD
## 7586 380 ASD
## 7587 380 ASD
## 7588 380 ASD
## 7589 380 ASD
## 7590 380 ASD
## 7591 380 ASD
## 7592 380 ASD
## 7593 380 ASD
## 7594 380 ASD
## 7595 380 ASD
## 7596 380 ASD
## 7597 380 ASD
## 7598 380 ASD
## 7599 380 ASD
## 7600 380 ASD
## 7621 382 ASD
## 7622 382 ASD
## 7623 382 ASD
## 7624 382 ASD
## 7625 382 ASD
## 7626 382 ASD
## 7627 382 ASD
## 7628 382 ASD
## 7629 382 ASD
## 7630 382 ASD
## 7631 382 ASD
## 7632 382 ASD
## 7633 382 ASD
## 7634 382 ASD
## 7635 382 ASD
## 7636 382 ASD
## 7637 382 ASD
## 7638 382 ASD
## 7639 382 ASD
## 7640 382 ASD
## 7761 389 ASD
## 7762 389 ASD
## 7763 389 ASD
## 7764 389 ASD
## 7765 389 ASD
## 7766 389 ASD
## 7767 389 ASD
## 7768 389 ASD
## 7769 389 ASD
## 7770 389 ASD
## 7771 389 ASD
## 7772 389 ASD
## 7773 389 ASD
## 7774 389 ASD
## 7775 389 ASD
## 7776 389 ASD
## 7777 389 ASD
## 7778 389 ASD
## 7779 389 ASD
## 7780 389 ASD
## 7801 392 ASD
## 7802 392 ASD
## 7803 392 ASD
## 7804 392 ASD
## 7805 392 ASD
## 7806 392 ASD
## 7807 392 ASD
## 7808 392 ASD
## 7809 392 ASD
## 7810 392 ASD
## 7811 392 ASD
## 7812 392 ASD
## 7813 392 ASD
## 7814 392 ASD
## 7815 392 ASD
## 7816 392 ASD
## 7817 392 ASD
## 7818 392 ASD
## 7819 392 ASD
## 7820 392 ASD
## 7861 395 ASD
## 7862 395 ASD
## 7863 395 ASD
## 7864 395 ASD
## 7865 395 ASD
## 7866 395 ASD
## 7867 395 ASD
## 7868 395 ASD
## 7869 395 ASD
## 7870 395 ASD
## 7871 395 ASD
## 7872 395 ASD
## 7873 395 ASD
## 7874 395 ASD
## 7875 395 ASD
## 7876 395 ASD
## 7877 395 ASD
## 7878 395 ASD
## 7879 395 ASD
## 7880 395 ASD
## comorbidities
## 4001 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4002 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4003 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4004 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4005 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4006 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4007 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4008 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4009 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4010 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4011 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4012 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4013 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4014 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4015 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4016 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4017 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4018 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4019 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4020 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 4261 ["Other"]
## 4262 ["Other"]
## 4263 ["Other"]
## 4264 ["Other"]
## 4265 ["Other"]
## 4266 ["Other"]
## 4267 ["Other"]
## 4268 ["Other"]
## 4269 ["Other"]
## 4270 ["Other"]
## 4271 ["Other"]
## 4272 ["Other"]
## 4273 ["Other"]
## 4274 ["Other"]
## 4275 ["Other"]
## 4276 ["Other"]
## 4277 ["Other"]
## 4278 ["Other"]
## 4279 ["Other"]
## 4280 ["Other"]
## 4361 ["None of the above"]
## 4362 ["None of the above"]
## 4363 ["None of the above"]
## 4364 ["None of the above"]
## 4365 ["None of the above"]
## 4366 ["None of the above"]
## 4367 ["None of the above"]
## 4368 ["None of the above"]
## 4369 ["None of the above"]
## 4370 ["None of the above"]
## 4371 ["None of the above"]
## 4372 ["None of the above"]
## 4373 ["None of the above"]
## 4374 ["None of the above"]
## 4375 ["None of the above"]
## 4376 ["None of the above"]
## 4377 ["None of the above"]
## 4378 ["None of the above"]
## 4379 ["None of the above"]
## 4380 ["None of the above"]
## 4381 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4382 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4383 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4384 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4385 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4386 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4387 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4388 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4389 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4390 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4391 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4392 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4393 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4394 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4395 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4396 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4397 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4398 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4399 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4400 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","OCD (Obsessive-Compulsive Disorder)","Sleep Disorders"]
## 4421 ["Depression"]
## 4422 ["Depression"]
## 4423 ["Depression"]
## 4424 ["Depression"]
## 4425 ["Depression"]
## 4426 ["Depression"]
## 4427 ["Depression"]
## 4428 ["Depression"]
## 4429 ["Depression"]
## 4430 ["Depression"]
## 4431 ["Depression"]
## 4432 ["Depression"]
## 4433 ["Depression"]
## 4434 ["Depression"]
## 4435 ["Depression"]
## 4436 ["Depression"]
## 4437 ["Depression"]
## 4438 ["Depression"]
## 4439 ["Depression"]
## 4440 ["Depression"]
## 4441 ["Gastrointestinal Issues"]
## 4442 ["Gastrointestinal Issues"]
## 4443 ["Gastrointestinal Issues"]
## 4444 ["Gastrointestinal Issues"]
## 4445 ["Gastrointestinal Issues"]
## 4446 ["Gastrointestinal Issues"]
## 4447 ["Gastrointestinal Issues"]
## 4448 ["Gastrointestinal Issues"]
## 4449 ["Gastrointestinal Issues"]
## 4450 ["Gastrointestinal Issues"]
## 4451 ["Gastrointestinal Issues"]
## 4452 ["Gastrointestinal Issues"]
## 4453 ["Gastrointestinal Issues"]
## 4454 ["Gastrointestinal Issues"]
## 4455 ["Gastrointestinal Issues"]
## 4456 ["Gastrointestinal Issues"]
## 4457 ["Gastrointestinal Issues"]
## 4458 ["Gastrointestinal Issues"]
## 4459 ["Gastrointestinal Issues"]
## 4460 ["Gastrointestinal Issues"]
## 4461 ["Anxiety Disorder"]
## 4462 ["Anxiety Disorder"]
## 4463 ["Anxiety Disorder"]
## 4464 ["Anxiety Disorder"]
## 4465 ["Anxiety Disorder"]
## 4466 ["Anxiety Disorder"]
## 4467 ["Anxiety Disorder"]
## 4468 ["Anxiety Disorder"]
## 4469 ["Anxiety Disorder"]
## 4470 ["Anxiety Disorder"]
## 4471 ["Anxiety Disorder"]
## 4472 ["Anxiety Disorder"]
## 4473 ["Anxiety Disorder"]
## 4474 ["Anxiety Disorder"]
## 4475 ["Anxiety Disorder"]
## 4476 ["Anxiety Disorder"]
## 4477 ["Anxiety Disorder"]
## 4478 ["Anxiety Disorder"]
## 4479 ["Anxiety Disorder"]
## 4480 ["Anxiety Disorder"]
## 4641 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4642 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4643 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4644 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4645 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4646 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4647 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4648 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4649 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4650 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4651 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4652 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4653 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4654 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4655 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4656 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4657 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4658 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4659 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4660 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 4681 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4682 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4683 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4684 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4685 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4686 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4687 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4688 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4689 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4690 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4691 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4692 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4693 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4694 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4695 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4696 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4697 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4698 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4699 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4700 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Learning Disability (e.g., Dyslexia)"]
## 4761 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4762 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4763 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4764 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4765 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4766 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4767 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4768 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4769 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4770 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4771 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4772 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4773 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4774 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4775 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4776 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4777 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4778 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4779 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4780 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 4921 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4922 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4923 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4924 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4925 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4926 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4927 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4928 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4929 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4930 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4931 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4932 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4933 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4934 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4935 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4936 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4937 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4938 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4939 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4940 ["ADHD (Attention Deficit Hyperactivity Disorder)","OCD (Obsessive-Compulsive Disorder)","Epilepsy or Seizure Disorder","Gastrointestinal Issues"]
## 4961 ["None of the above"]
## 4962 ["None of the above"]
## 4963 ["None of the above"]
## 4964 ["None of the above"]
## 4965 ["None of the above"]
## 4966 ["None of the above"]
## 4967 ["None of the above"]
## 4968 ["None of the above"]
## 4969 ["None of the above"]
## 4970 ["None of the above"]
## 4971 ["None of the above"]
## 4972 ["None of the above"]
## 4973 ["None of the above"]
## 4974 ["None of the above"]
## 4975 ["None of the above"]
## 4976 ["None of the above"]
## 4977 ["None of the above"]
## 4978 ["None of the above"]
## 4979 ["None of the above"]
## 4980 ["None of the above"]
## 4981 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4982 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4983 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4984 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4985 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4986 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4987 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4988 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4989 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4990 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4991 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4992 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4993 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4994 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4995 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4996 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4997 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4998 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 4999 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 5000 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Gastrointestinal Issues","Sleep Disorders"]
## 5061 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5062 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5063 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5064 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5065 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5066 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5067 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5068 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5069 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5070 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5071 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5072 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5073 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5074 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5075 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5076 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5077 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5078 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5079 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5080 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5121 ["None of the above"]
## 5122 ["None of the above"]
## 5123 ["None of the above"]
## 5124 ["None of the above"]
## 5125 ["None of the above"]
## 5126 ["None of the above"]
## 5127 ["None of the above"]
## 5128 ["None of the above"]
## 5129 ["None of the above"]
## 5130 ["None of the above"]
## 5131 ["None of the above"]
## 5132 ["None of the above"]
## 5133 ["None of the above"]
## 5134 ["None of the above"]
## 5135 ["None of the above"]
## 5136 ["None of the above"]
## 5137 ["None of the above"]
## 5138 ["None of the above"]
## 5139 ["None of the above"]
## 5140 ["None of the above"]
## 5141 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5142 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5143 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5144 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5145 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5146 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5147 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5148 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5149 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5150 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5151 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5152 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5153 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5154 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5155 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5156 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5157 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5158 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5159 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5160 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5161 ["Anxiety Disorder","Depression"]
## 5162 ["Anxiety Disorder","Depression"]
## 5163 ["Anxiety Disorder","Depression"]
## 5164 ["Anxiety Disorder","Depression"]
## 5165 ["Anxiety Disorder","Depression"]
## 5166 ["Anxiety Disorder","Depression"]
## 5167 ["Anxiety Disorder","Depression"]
## 5168 ["Anxiety Disorder","Depression"]
## 5169 ["Anxiety Disorder","Depression"]
## 5170 ["Anxiety Disorder","Depression"]
## 5171 ["Anxiety Disorder","Depression"]
## 5172 ["Anxiety Disorder","Depression"]
## 5173 ["Anxiety Disorder","Depression"]
## 5174 ["Anxiety Disorder","Depression"]
## 5175 ["Anxiety Disorder","Depression"]
## 5176 ["Anxiety Disorder","Depression"]
## 5177 ["Anxiety Disorder","Depression"]
## 5178 ["Anxiety Disorder","Depression"]
## 5179 ["Anxiety Disorder","Depression"]
## 5180 ["Anxiety Disorder","Depression"]
## 5181 ["None of the above"]
## 5182 ["None of the above"]
## 5183 ["None of the above"]
## 5184 ["None of the above"]
## 5185 ["None of the above"]
## 5186 ["None of the above"]
## 5187 ["None of the above"]
## 5188 ["None of the above"]
## 5189 ["None of the above"]
## 5190 ["None of the above"]
## 5191 ["None of the above"]
## 5192 ["None of the above"]
## 5193 ["None of the above"]
## 5194 ["None of the above"]
## 5195 ["None of the above"]
## 5196 ["None of the above"]
## 5197 ["None of the above"]
## 5198 ["None of the above"]
## 5199 ["None of the above"]
## 5200 ["None of the above"]
## 5421 ["None of the above"]
## 5422 ["None of the above"]
## 5423 ["None of the above"]
## 5424 ["None of the above"]
## 5425 ["None of the above"]
## 5426 ["None of the above"]
## 5427 ["None of the above"]
## 5428 ["None of the above"]
## 5429 ["None of the above"]
## 5430 ["None of the above"]
## 5431 ["None of the above"]
## 5432 ["None of the above"]
## 5433 ["None of the above"]
## 5434 ["None of the above"]
## 5435 ["None of the above"]
## 5436 ["None of the above"]
## 5437 ["None of the above"]
## 5438 ["None of the above"]
## 5439 ["None of the above"]
## 5440 ["None of the above"]
## 5481 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5482 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5483 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5484 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5485 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5486 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5487 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5488 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5489 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5490 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5491 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5492 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5493 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5494 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5495 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5496 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5497 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5498 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5499 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5500 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5501 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5502 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5503 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5504 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5505 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5506 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5507 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5508 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5509 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5510 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5511 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5512 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5513 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5514 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5515 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5516 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5517 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5518 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5519 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5520 ["Anxiety Disorder","Depression","Gastrointestinal Issues"]
## 5521 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5522 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5523 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5524 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5525 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5526 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5527 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5528 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5529 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5530 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5531 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5532 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5533 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5534 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5535 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5536 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5537 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5538 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5539 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5540 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Sleep Disorders"]
## 5601 ["Sleep Disorders"]
## 5602 ["Sleep Disorders"]
## 5603 ["Sleep Disorders"]
## 5604 ["Sleep Disorders"]
## 5605 ["Sleep Disorders"]
## 5606 ["Sleep Disorders"]
## 5607 ["Sleep Disorders"]
## 5608 ["Sleep Disorders"]
## 5609 ["Sleep Disorders"]
## 5610 ["Sleep Disorders"]
## 5611 ["Sleep Disorders"]
## 5612 ["Sleep Disorders"]
## 5613 ["Sleep Disorders"]
## 5614 ["Sleep Disorders"]
## 5615 ["Sleep Disorders"]
## 5616 ["Sleep Disorders"]
## 5617 ["Sleep Disorders"]
## 5618 ["Sleep Disorders"]
## 5619 ["Sleep Disorders"]
## 5620 ["Sleep Disorders"]
## 5641 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5642 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5643 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5644 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5645 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5646 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5647 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5648 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5649 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5650 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5651 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5652 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5653 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5654 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5655 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5656 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5657 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5658 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5659 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5660 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression"]
## 5721 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5722 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5723 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5724 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5725 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5726 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5727 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5728 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5729 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5730 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5731 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5732 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5733 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5734 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5735 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5736 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5737 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5738 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5739 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5740 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder"]
## 5761 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5762 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5763 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5764 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5765 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5766 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5767 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5768 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5769 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5770 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5771 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5772 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5773 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5774 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5775 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5776 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5777 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5778 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5779 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5780 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders"]
## 5801 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5802 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5803 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5804 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5805 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5806 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5807 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5808 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5809 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5810 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5811 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5812 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5813 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5814 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5815 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5816 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5817 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5818 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5819 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5820 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 5941 ["Anxiety Disorder","Depression"]
## 5942 ["Anxiety Disorder","Depression"]
## 5943 ["Anxiety Disorder","Depression"]
## 5944 ["Anxiety Disorder","Depression"]
## 5945 ["Anxiety Disorder","Depression"]
## 5946 ["Anxiety Disorder","Depression"]
## 5947 ["Anxiety Disorder","Depression"]
## 5948 ["Anxiety Disorder","Depression"]
## 5949 ["Anxiety Disorder","Depression"]
## 5950 ["Anxiety Disorder","Depression"]
## 5951 ["Anxiety Disorder","Depression"]
## 5952 ["Anxiety Disorder","Depression"]
## 5953 ["Anxiety Disorder","Depression"]
## 5954 ["Anxiety Disorder","Depression"]
## 5955 ["Anxiety Disorder","Depression"]
## 5956 ["Anxiety Disorder","Depression"]
## 5957 ["Anxiety Disorder","Depression"]
## 5958 ["Anxiety Disorder","Depression"]
## 5959 ["Anxiety Disorder","Depression"]
## 5960 ["Anxiety Disorder","Depression"]
## 6041 ["None of the above"]
## 6042 ["None of the above"]
## 6043 ["None of the above"]
## 6044 ["None of the above"]
## 6045 ["None of the above"]
## 6046 ["None of the above"]
## 6047 ["None of the above"]
## 6048 ["None of the above"]
## 6049 ["None of the above"]
## 6050 ["None of the above"]
## 6051 ["None of the above"]
## 6052 ["None of the above"]
## 6053 ["None of the above"]
## 6054 ["None of the above"]
## 6055 ["None of the above"]
## 6056 ["None of the above"]
## 6057 ["None of the above"]
## 6058 ["None of the above"]
## 6059 ["None of the above"]
## 6060 ["None of the above"]
## 6261 ["None of the above"]
## 6262 ["None of the above"]
## 6263 ["None of the above"]
## 6264 ["None of the above"]
## 6265 ["None of the above"]
## 6266 ["None of the above"]
## 6267 ["None of the above"]
## 6268 ["None of the above"]
## 6269 ["None of the above"]
## 6270 ["None of the above"]
## 6271 ["None of the above"]
## 6272 ["None of the above"]
## 6273 ["None of the above"]
## 6274 ["None of the above"]
## 6275 ["None of the above"]
## 6276 ["None of the above"]
## 6277 ["None of the above"]
## 6278 ["None of the above"]
## 6279 ["None of the above"]
## 6280 ["None of the above"]
## 6321 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6322 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6323 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6324 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6325 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6326 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6327 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6328 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6329 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6330 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6331 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6332 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6333 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6334 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6335 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6336 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6337 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6338 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6339 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6340 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Gastrointestinal Issues","Sleep Disorders","Learning Disability (e.g., Dyslexia)"]
## 6341 ["None of the above"]
## 6342 ["None of the above"]
## 6343 ["None of the above"]
## 6344 ["None of the above"]
## 6345 ["None of the above"]
## 6346 ["None of the above"]
## 6347 ["None of the above"]
## 6348 ["None of the above"]
## 6349 ["None of the above"]
## 6350 ["None of the above"]
## 6351 ["None of the above"]
## 6352 ["None of the above"]
## 6353 ["None of the above"]
## 6354 ["None of the above"]
## 6355 ["None of the above"]
## 6356 ["None of the above"]
## 6357 ["None of the above"]
## 6358 ["None of the above"]
## 6359 ["None of the above"]
## 6360 ["None of the above"]
## 6401 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6402 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6403 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6404 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6405 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6406 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6407 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6408 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6409 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6410 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6411 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6412 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6413 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6414 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6415 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6416 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6417 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6418 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6419 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6420 ["Anxiety Disorder","Depression","Sleep Disorders"]
## 6461 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6462 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6463 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6464 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6465 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6466 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6467 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6468 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6469 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6470 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6471 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6472 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6473 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6474 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6475 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6476 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6477 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6478 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6479 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6480 ["ADHD (Attention Deficit Hyperactivity Disorder)","Depression","Sleep Disorders"]
## 6541 ["None of the above"]
## 6542 ["None of the above"]
## 6543 ["None of the above"]
## 6544 ["None of the above"]
## 6545 ["None of the above"]
## 6546 ["None of the above"]
## 6547 ["None of the above"]
## 6548 ["None of the above"]
## 6549 ["None of the above"]
## 6550 ["None of the above"]
## 6551 ["None of the above"]
## 6552 ["None of the above"]
## 6553 ["None of the above"]
## 6554 ["None of the above"]
## 6555 ["None of the above"]
## 6556 ["None of the above"]
## 6557 ["None of the above"]
## 6558 ["None of the above"]
## 6559 ["None of the above"]
## 6560 ["None of the above"]
## 6601 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6602 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6603 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6604 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6605 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6606 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6607 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6608 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6609 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6610 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6611 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6612 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6613 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6614 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6615 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6616 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6617 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6618 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6619 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6620 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6621 ["None of the above"]
## 6622 ["None of the above"]
## 6623 ["None of the above"]
## 6624 ["None of the above"]
## 6625 ["None of the above"]
## 6626 ["None of the above"]
## 6627 ["None of the above"]
## 6628 ["None of the above"]
## 6629 ["None of the above"]
## 6630 ["None of the above"]
## 6631 ["None of the above"]
## 6632 ["None of the above"]
## 6633 ["None of the above"]
## 6634 ["None of the above"]
## 6635 ["None of the above"]
## 6636 ["None of the above"]
## 6637 ["None of the above"]
## 6638 ["None of the above"]
## 6639 ["None of the above"]
## 6640 ["None of the above"]
## 6781 ["None of the above"]
## 6782 ["None of the above"]
## 6783 ["None of the above"]
## 6784 ["None of the above"]
## 6785 ["None of the above"]
## 6786 ["None of the above"]
## 6787 ["None of the above"]
## 6788 ["None of the above"]
## 6789 ["None of the above"]
## 6790 ["None of the above"]
## 6791 ["None of the above"]
## 6792 ["None of the above"]
## 6793 ["None of the above"]
## 6794 ["None of the above"]
## 6795 ["None of the above"]
## 6796 ["None of the above"]
## 6797 ["None of the above"]
## 6798 ["None of the above"]
## 6799 ["None of the above"]
## 6800 ["None of the above"]
## 6981 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6982 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6983 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6984 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6985 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6986 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6987 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6988 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6989 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6990 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6991 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6992 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6993 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6994 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6995 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6996 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6997 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6998 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 6999 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7000 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7021 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7022 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7023 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7024 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7025 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7026 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7027 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7028 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7029 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7030 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7031 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7032 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7033 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7034 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7035 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7036 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7037 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7038 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7039 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7040 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7041 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7042 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7043 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7044 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7045 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7046 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7047 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7048 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7049 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7050 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7051 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7052 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7053 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7054 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7055 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7056 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7057 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7058 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7059 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7060 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7161 ["None of the above"]
## 7162 ["None of the above"]
## 7163 ["None of the above"]
## 7164 ["None of the above"]
## 7165 ["None of the above"]
## 7166 ["None of the above"]
## 7167 ["None of the above"]
## 7168 ["None of the above"]
## 7169 ["None of the above"]
## 7170 ["None of the above"]
## 7171 ["None of the above"]
## 7172 ["None of the above"]
## 7173 ["None of the above"]
## 7174 ["None of the above"]
## 7175 ["None of the above"]
## 7176 ["None of the above"]
## 7177 ["None of the above"]
## 7178 ["None of the above"]
## 7179 ["None of the above"]
## 7180 ["None of the above"]
## 7301 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7302 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7303 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7304 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7305 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7306 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7307 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7308 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7309 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7310 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7311 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7312 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7313 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7314 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7315 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7316 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7317 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7318 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7319 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7320 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7441 ["Depression"]
## 7442 ["Depression"]
## 7443 ["Depression"]
## 7444 ["Depression"]
## 7445 ["Depression"]
## 7446 ["Depression"]
## 7447 ["Depression"]
## 7448 ["Depression"]
## 7449 ["Depression"]
## 7450 ["Depression"]
## 7451 ["Depression"]
## 7452 ["Depression"]
## 7453 ["Depression"]
## 7454 ["Depression"]
## 7455 ["Depression"]
## 7456 ["Depression"]
## 7457 ["Depression"]
## 7458 ["Depression"]
## 7459 ["Depression"]
## 7460 ["Depression"]
## 7461 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7462 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7463 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7464 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7465 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7466 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7467 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7468 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7469 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7470 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7471 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7472 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7473 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7474 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7475 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7476 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7477 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7478 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7479 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7480 ["ADHD (Attention Deficit Hyperactivity Disorder)"]
## 7581 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7582 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7583 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7584 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7585 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7586 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7587 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7588 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7589 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7590 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7591 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7592 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7593 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7594 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7595 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7596 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7597 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7598 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7599 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7600 ["ADHD (Attention Deficit Hyperactivity Disorder)","Anxiety Disorder","Depression","Epilepsy or Seizure Disorder","Sleep Disorders"]
## 7621 ["None of the above"]
## 7622 ["None of the above"]
## 7623 ["None of the above"]
## 7624 ["None of the above"]
## 7625 ["None of the above"]
## 7626 ["None of the above"]
## 7627 ["None of the above"]
## 7628 ["None of the above"]
## 7629 ["None of the above"]
## 7630 ["None of the above"]
## 7631 ["None of the above"]
## 7632 ["None of the above"]
## 7633 ["None of the above"]
## 7634 ["None of the above"]
## 7635 ["None of the above"]
## 7636 ["None of the above"]
## 7637 ["None of the above"]
## 7638 ["None of the above"]
## 7639 ["None of the above"]
## 7640 ["None of the above"]
## 7761
## 7762
## 7763
## 7764
## 7765
## 7766
## 7767
## 7768
## 7769
## 7770
## 7771
## 7772
## 7773
## 7774
## 7775
## 7776
## 7777
## 7778
## 7779
## 7780
## 7801
## 7802
## 7803
## 7804
## 7805
## 7806
## 7807
## 7808
## 7809
## 7810
## 7811
## 7812
## 7813
## 7814
## 7815
## 7816
## 7817
## 7818
## 7819
## 7820
## 7861
## 7862
## 7863
## 7864
## 7865
## 7866
## 7867
## 7868
## 7869
## 7870
## 7871
## 7872
## 7873
## 7874
## 7875
## 7876
## 7877
## 7878
## 7879
## 7880
# ASD in the comorbidities
length(unique(clean_data_1a$subject_nr[
clean_data_1a$group == "ASD" & !grepl("ASD", clean_data_1a$comorbidities)
])) # These are 50 participants
## [1] 50
any_nt_with_asd <- any(
grepl("ASD", clean_data_1a$comorbidities[clean_data_1a$group == "NT"])
)
any_nt_with_asd
## [1] FALSE
subset(
clean_data_1a,
group == "NT" & grepl("ASD", comorbidities)
)[, c("subject_nr", "group", "comorbidities")]
## [1] subject_nr group comorbidities
## <0 rows> (or 0-length row.names)
with(clean_data_1a,
table(group, grepl("ASD", comorbidities))) # There are no group = NT participants
##
## group FALSE TRUE
## NT 4000 0
## ASD 1000 2900
# with ASD in the comorbidities
# Study 2
str(clean_data_2)
## tibble [12,224 × 57] (S3: tbl_df/tbl/data.frame)
## $ group : Factor w/ 2 levels "NT","ASD": 2 2 2 2 2 2 2 2 2 2 ...
## $ gender : Factor w/ 4 levels "","Female","Male",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ comorbidities : chr [1:12224] "[\"ASD (Autism Spectrum Disorder)\",\"ADHD (Attention Deficit Hyperactivity Disorder)\"]" "[\"ASD (Autism Spectrum Disorder)\",\"ADHD (Attention Deficit Hyperactivity Disorder)\"]" "[\"ASD (Autism Spectrum Disorder)\",\"ADHD (Attention Deficit Hyperactivity Disorder)\"]" "[\"ASD (Autism Spectrum Disorder)\",\"ADHD (Attention Deficit Hyperactivity Disorder)\"]" ...
## $ purpose_present : chr [1:12224] "Block1" "Block1" "Block1" "Block1" ...
## $ lateralization : chr [1:12224] "Left0" "Left0" "Left0" "Left0" ...
## $ scene : chr [1:12224] "food or drink" "food or drink" "food or drink" "food or drink" ...
## $ condition : chr [1:12224] "purpose_and_text_violate" "purpose_and_text_comply" "purpose_violate_text_comply" "purpose_comply_text_violate" ...
## $ run_id : int [1:12224] 104 104 104 104 104 104 104 104 104 104 ...
## $ aq1 : int [1:12224] 2 2 2 2 2 2 2 2 2 2 ...
## $ aq2 : int [1:12224] 0 0 0 0 0 0 0 0 0 0 ...
## $ aq3 : int [1:12224] 3 3 3 3 3 3 3 3 3 3 ...
## $ aq4 : int [1:12224] 1 1 1 1 1 1 1 1 1 1 ...
## $ aq5 : int [1:12224] 3 3 3 3 3 3 3 3 3 3 ...
## $ aq6 : int [1:12224] 0 0 0 0 0 0 0 0 0 0 ...
## $ aq7 : int [1:12224] 3 3 3 3 3 3 3 3 3 3 ...
## $ aq8 : int [1:12224] 2 2 2 2 2 2 2 2 2 2 ...
## $ aq9 : int [1:12224] 1 1 1 1 1 1 1 1 1 1 ...
## $ aq10 : int [1:12224] 3 3 3 3 3 3 3 3 3 3 ...
## $ trial_index_violation : int [1:12224] 4 9 14 19 25 30 35 40 46 51 ...
## $ trial_index_confidence : int [1:12224] 5 10 15 20 26 31 36 41 47 52 ...
## $ trial_index_literal_meaning: int [1:12224] 6 11 16 22 27 33 38 42 49 53 ...
## $ trial_index_upset_rating : int [1:12224] 7 12 17 21 28 32 37 43 48 54 ...
## $ response_violation : chr [1:12224] "1" "1" "0" "1" ...
## $ response_confidence : num [1:12224] 100 71 100 100 100 100 100 100 100 100 ...
## $ response_literal_meaning : num [1:12224] 1 1 1 0 1 0 1 0 1 1 ...
## $ response_upset_rating : num [1:12224] 1.5 1.5 1.5 -1.5 1.5 1.5 -0.5 -1.5 1.5 0.5 ...
## $ rt_violation : num [1:12224] 13431 14438 15393 10146 18748 ...
## $ rt_confidence : num [1:12224] 10168 3376 3739 4964 6596 ...
## $ rt_literal_meaning : num [1:12224] 8109 9870 3647 3611 3991 ...
## $ rt_upset_rating : num [1:12224] 10307 1751 2939 5100 2098 ...
## $ response : Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 1 2 2 ...
## $ text : Factor w/ 2 levels "0","1": 2 1 1 2 2 1 2 1 2 2 ...
## $ purpose : Factor w/ 2 levels "0","1": 2 1 2 1 2 2 1 1 2 1 ...
## $ cw_resp : num [1:12224] -100 -71 100 -100 100 100 100 -100 100 100 ...
## $ trial : int [1:12224] 1 2 3 4 5 6 7 8 9 10 ...
## $ purpose_display : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ aq_score_old : int [1:12224] 18 18 18 18 18 18 18 18 18 18 ...
## $ autism : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ adhd : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ anxiety : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ depression : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ ocd : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ epilepsy : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ gastrointestinal : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ sleep_dis : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ sensory_process_dis : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ learn_dis : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ time_elapsed : int [1:12224] 1149300 1149300 1149300 1149300 1149300 1149300 1149300 1149300 1149300 1149300 ...
## $ subject_nr : int [1:12224] 1 1 1 1 1 1 1 1 1 1 ...
## $ response_upset_factor : Ord.factor w/ 4 levels "0"<"1"<"2"<"3": 4 4 4 1 4 4 2 1 4 3 ...
## $ aq_score : num [1:12224] 2 2 2 2 2 2 2 2 2 2 ...
## $ aq2_inv : num [1:12224] 3 3 3 3 3 3 3 3 3 3 ...
## $ aq3_inv : num [1:12224] 0 0 0 0 0 0 0 0 0 0 ...
## $ aq4_inv : num [1:12224] 2 2 2 2 2 2 2 2 2 2 ...
## $ aq5_inv : num [1:12224] 0 0 0 0 0 0 0 0 0 0 ...
## $ aq6_inv : num [1:12224] 3 3 3 3 3 3 3 3 3 3 ...
## $ aq9_inv : num [1:12224] 2 2 2 2 2 2 2 2 2 2 ...
all_asd_have_asd <- all(
grepl("ASD", clean_data_2$comorbidities[clean_data_2$group == "ASD"])
)
all_asd_have_asd
## [1] FALSE
subset(
clean_data_2,
group == "ASD" & !grepl("ASD", comorbidities)
)[, c("subject_nr", "group", "comorbidities")] # There are various people with group = ASD but no
## # A tibble: 3,680 × 3
## subject_nr group comorbidities
## <int> <fct> <chr>
## 1 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 2 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 3 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 4 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 5 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 6 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 7 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 8 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 9 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## 10 2 ASD "[\"ADHD (Attention Deficit Hyperactivity Disorder)\"]"
## # ℹ 3,670 more rows
# ASD in the comorbidities
length(unique(clean_data_2$subject_nr[
clean_data_2$group == "ASD" & !grepl("ASD", clean_data_2$comorbidities)
])) # These are 115 participants
## [1] 115
data.frame(
condition = c("group = ASD & autism = 0",
"group = NT & autism = 1"),
n_subjects = c(
length(unique(clean_data_2$subject_nr[clean_data_2$group == "ASD" & clean_data_2$autism == 0])),
length(unique(clean_data_2$subject_nr[clean_data_2$group == "NT" & clean_data_2$autism == 1]))
)
) # I confirm: it is 115
## condition n_subjects
## 1 group = ASD & autism = 0 115
## 2 group = NT & autism = 1 0
any_nt_with_asd <- any(
grepl("ASD", clean_data_2$comorbidities[clean_data_2$group == "NT"])
)
any_nt_with_asd
## [1] FALSE
subset(
clean_data_2,
group == "NT" & grepl("ASD", comorbidities)
)[, c("subject_nr", "group", "comorbidities")]
## # A tibble: 0 × 3
## # ℹ 3 variables: subject_nr <int>, group <fct>, comorbidities <chr>
with(clean_data_2,
table(group, grepl("ASD", comorbidities))) # There are no group = NT participants
##
## group FALSE TRUE
## NT 6176 0
## ASD 3680 2368
# with ASD in the comorbidities
##### Create cases variable
clean_data_2 <- clean_data_2 %>%
mutate(
case = case_when(
text == 0 & purpose == 0 ~ "compl",
text == 1 & purpose == 1 ~ "viol",
text == 1 & purpose == 0 ~ "over",
text == 0 & purpose == 1 ~ "under"
),
case = factor(case, levels = c("compl", "viol", "over", "under"))
)
##### Create collapsed dataset clean_data_viol
clean_data_viol <- bind_rows(
clean_data_1a,
clean_data_2 %>%
mutate(
subject_nr = subject_nr + max(unique(clean_data_1a$subject_nr), na.rm = TRUE)
)
)
# Check that all participant IDs are unique
anyDuplicated(unique(clean_data_viol$subject_nr))
## [1] 0
# Eliminate irrelevant columns
clean_data_viol <- dplyr::select(
clean_data_viol,
text, purpose, case, group, response, scene, aq_score, subject_nr, cw_resp
)
ANALYSES FROM MANUSCRIPT
1. Autism and AQ-10 Scores
### STUDY 1
# Create dataset with 1 row per participant only ASD data (group and aq_score)
aq_data_1 <- clean_data_1a %>%
group_by(group, subject_nr) %>%
summarise(
aq_score = first(aq_score),
gender = first(gender), # keep gender
comorbid = first(comorbidities), # for flag creation
.groups = "drop"
) %>%
mutate(
# Binary comorbidity flags as 0/1 factors
autism = factor(as.integer(str_detect(comorbid, fixed("ASD (Autism Spectrum Disorder)")))),
adhd = factor(as.integer(str_detect(comorbid, fixed("Attention Deficit Hyperactivity Disorder")))),
anxiety = factor(as.integer(str_detect(comorbid, fixed("Anxiety Disorder")))),
depression = factor(as.integer(str_detect(comorbid, fixed("Depression")))),
ocd = factor(as.integer(str_detect(comorbid, fixed("Obsessive-Compulsive Disorder")))),
epilepsy = factor(as.integer(str_detect(comorbid, fixed("Epilepsy or Seizure Disorder")))),
gastrointestinal = factor(as.integer(str_detect(comorbid, fixed("Gastrointestinal Issues")))),
sleep_dis = factor(as.integer(str_detect(comorbid, fixed("Sleep Disorders")))),
sensory_process_dis = factor(as.integer(str_detect(comorbid, fixed("Sensory Processing Disorder")))),
learn_dis = factor(as.integer(str_detect(comorbid, fixed("Learning Disability (e.g., Dyslexia)"))))
) %>%
dplyr::select(-comorbid)
table(aq_data_1$gender)
##
## Female Male No answer Non-binary
## 0 156 228 1 10
# Compute statistics per group levels (NT vs. ASD)
aq_by_group_1 <- aq_data_1 %>%
group_by(group) %>%
summarise(
n = n(),
mean = mean(aq_score, na.rm = TRUE),
sd = sd(aq_score, na.rm = TRUE),
se = sd / sqrt(n),
ci95_low = mean - qt(.975, df = n - 1) * se,
ci95_high = mean + qt(.975, df = n - 1) * se,
.groups = "drop"
)
aq_by_group_1
## # A tibble: 2 × 7
## group n mean sd se ci95_low ci95_high
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NT 200 2.90 1.95 0.138 2.62 3.17
## 2 ASD 195 5.65 2.63 0.188 5.28 6.02
### STUDY 2
# Create dataset with 1 row per participant only ASD data (group and aq_score)
aq_data_2 <- clean_data_2 %>%
dplyr::select(group, subject_nr, aq_score, gender, autism, adhd, anxiety, depression,
ocd, epilepsy, gastrointestinal, sleep_dis, sensory_process_dis, learn_dis) %>%
dplyr::group_by(subject_nr) %>%
dplyr::slice(1) %>%
dplyr::ungroup()
table(aq_data_2$gender)
##
## Female Male Non-binary
## 1 184 195 2
# Compute statistics per group levels (NT vs. ASD)
aq_by_group_2 <- aq_data_2 %>%
group_by(group) %>%
summarise(
n = n(),
mean = mean(aq_score, na.rm = TRUE),
sd = sd(aq_score, na.rm = TRUE),
se = sd / sqrt(n),
ci95_low = mean - qt(.975, df = n - 1) * se,
ci95_high = mean + qt(.975, df = n - 1) * se,
.groups = "drop"
)
aq_by_group_2
## # A tibble: 2 × 7
## group n mean sd se ci95_low ci95_high
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NT 193 2.80 1.77 0.128 2.55 3.05
## 2 ASD 189 4.46 2.38 0.173 4.12 4.80
### CREATE AGGREGATE DATASET
# Create aggregate aq_data_total
# Find the max subject_nr in aq_data_1
max_id <- max(aq_data_1$subject_nr, na.rm = TRUE)
# Offset subject_nr in aq_data_2
aq_data_2_offset <- aq_data_2 %>%
dplyr::mutate(subject_nr = subject_nr + max_id)
# Combine the datasets
aq_data_total <- dplyr::bind_rows(aq_data_1, aq_data_2_offset)
aq_data_total
## # A tibble: 777 × 14
## group subject_nr aq_score gender autism adhd anxiety depression ocd
## <fct> <int> <dbl> <fct> <fct> <fct> <fct> <fct> <fct>
## 1 NT 1 2 Female 0 0 0 0 0
## 2 NT 2 6 Female 0 0 0 0 0
## 3 NT 3 2 Female 0 0 0 0 0
## 4 NT 4 2 Male 0 0 0 0 0
## 5 NT 5 2 Female 0 0 0 0 0
## 6 NT 6 2 Female 0 0 0 0 0
## 7 NT 7 2 Male 0 0 0 0 0
## 8 NT 8 3 Female 0 0 0 0 0
## 9 NT 9 1 Female 0 0 0 1 1
## 10 NT 10 4 Male 0 0 0 0 0
## # ℹ 767 more rows
## # ℹ 5 more variables: epilepsy <fct>, gastrointestinal <fct>, sleep_dis <fct>,
## # sensory_process_dis <fct>, learn_dis <fct>
# # Create aggregate aq_by_group_total
aq_by_group_total <- aq_data_total %>%
group_by(group) %>%
summarise(
n = n(),
mean = mean(aq_score, na.rm = TRUE),
sd = sd(aq_score, na.rm = TRUE),
se = sd / sqrt(n),
ci95_low = mean - qt(.975, df = n - 1) * se,
ci95_high = mean + qt(.975, df = n - 1) * se,
.groups = "drop"
)
aq_by_group_total
## # A tibble: 2 × 7
## group n mean sd se ci95_low ci95_high
## <fct> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 NT 393 2.85 1.86 0.0939 2.67 3.03
## 2 ASD 384 5.07 2.58 0.131 4.81 5.32
##### 1.1) AQ10 by group and self-report (Studies 1 and 2) (after exclusions, group and self-report coincide)
### STUDY 1
model_1_1 <- lm(aq_score ~ group + autism, data = aq_data_1)
Anova(model_1_1, type = 3) # Both main effects
## Anova Table (Type III tests)
##
## Response: aq_score
## Sum Sq Df F value Pr(>F)
## (Intercept) 1676.21 1 334.741 < 2.2e-16 ***
## group 74.53 1 14.884 0.0001337 ***
## autism 130.16 1 25.993 5.345e-07 ***
## Residuals 1962.93 392
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### STUDY 2
model_1_2 <- lm(aq_score ~ group + autism, data = aq_data_2)
Anova(model_1_2, type = 3) # Both main effects
## Anova Table (Type III tests)
##
## Response: aq_score
## Sum Sq Df F value Pr(>F)
## (Intercept) 1516.48 1 380.816 < 2.2e-16 ***
## group 60.81 1 15.271 0.0001103 ***
## autism 160.22 1 40.235 6.421e-10 ***
## Residuals 1509.25 379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### AGGREGATE (BOTH STUDIES TOGETHER)
model_1_3 <- lm(aq_score ~ group + autism, data = aq_data_total)
Anova(model_1_3, type = 3) # Both main effects
## Anova Table (Type III tests)
##
## Response: aq_score
## Sum Sq Df F value Pr(>F)
## (Intercept) 3191.9 1 706.563 < 2.2e-16 ***
## group 124.5 1 27.556 1.973e-07 ***
## autism 403.0 1 89.214 < 2.2e-16 ***
## Residuals 3496.5 774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_1_3, digits = 3, confint = TRUE) # From the jtools library
## MODEL INFO:
## Observations: 777
## Dependent Variable: aq_score
## Type: OLS linear regression
##
## MODEL FIT:
## F(2,774) = 150.099, p = 0.000
## R² = 0.279
## Adj. R² = 0.278
##
## Standard errors:OLS
## ----------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ----------------- ------- ------- ------- -------- -------
## (Intercept) 2.850 2.639 3.060 26.581 0.000
## groupASD 1.035 0.648 1.422 5.249 0.000
## autism1 2.069 1.639 2.500 9.445 0.000
## ----------------------------------------------------------
eta_squared(model_1_3, partial = TRUE)
## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------
## group | 0.21 | [0.17, 1.00]
## autism | 0.10 | [0.07, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
##### 1.2) Multiple regression of AQ10 (Studies 1 and 2)
### STUDY 1
model_1_4 <- lm(aq_score ~ autism + adhd + anxiety + depression +
ocd + epilepsy + gastrointestinal + sleep_dis +
sensory_process_dis + learn_dis + gender, data = aq_data_1)
Anova(model_1_4, type = 3) # Effect of self-report autism, and marginal effect of adhd and learning disorder
## Anova Table (Type III tests)
##
## Response: aq_score
## Sum Sq Df F value Pr(>F)
## (Intercept) 984.46 1 191.7956 < 2e-16 ***
## autism 518.44 1 101.0041 < 2e-16 ***
## adhd 14.92 1 2.9069 0.08902 .
## anxiety 13.61 1 2.6516 0.10427
## depression 0.02 1 0.0044 0.94729
## ocd 5.93 1 1.1557 0.28304
## epilepsy 3.02 1 0.5881 0.44363
## gastrointestinal 0.55 1 0.1069 0.74385
## sleep_dis 0.72 1 0.1399 0.70858
## sensory_process_dis 1.01 1 0.1961 0.65812
## learn_dis 14.77 1 2.8771 0.09066 .
## gender 10.91 3 0.7086 0.54733
## Residuals 1955.62 381
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### STUDY 2
model_1_5 <- lm(aq_score ~ autism + adhd + anxiety + depression +
ocd + epilepsy + gastrointestinal + sleep_dis +
sensory_process_dis + learn_dis + gender, data = aq_data_2)
Anova(model_1_5, type = 3) # Effect of self-report autism, and marginal effect of anxiety
## Anova Table (Type III tests)
##
## Response: aq_score
## Sum Sq Df F value Pr(>F)
## (Intercept) 9.00 1 2.1804 0.14063
## autism 259.20 1 62.7969 2.755e-14 ***
## adhd 1.52 1 0.3678 0.54456
## anxiety 11.82 1 2.8633 0.09147 .
## depression 2.75 1 0.6674 0.41448
## ocd 0.50 1 0.1206 0.72857
## epilepsy 1.56 1 0.3780 0.53904
## gastrointestinal 1.73 1 0.4185 0.51809
## sleep_dis 3.11 1 0.7537 0.38587
## sensory_process_dis 1.76 1 0.4258 0.51447
## learn_dis 4.49 1 1.0885 0.29749
## gender 22.80 3 1.8415 0.13918
## Residuals 1518.96 368
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
### AGGREGATE (BOTH STUDIES TOGETHER)
model_1_6 <- lm(aq_score ~ autism + adhd + anxiety + depression +
ocd + epilepsy + gastrointestinal + sleep_dis +
sensory_process_dis + learn_dis + gender, data = aq_data_total)
Anova(model_1_6, type = 3) # Effect of self-report autism, and significant effect of anxiety
## Anova Table (Type III tests)
##
## Response: aq_score
## Sum Sq Df F value Pr(>F)
## (Intercept) 9.0 1 1.9415 0.16392
## autism 870.7 1 187.8171 < 2e-16 ***
## adhd 11.0 1 2.3826 0.12310
## anxiety 22.9 1 4.9390 0.02655 *
## depression 1.1 1 0.2364 0.62694
## ocd 6.4 1 1.3806 0.24037
## epilepsy 0.3 1 0.0540 0.81635
## gastrointestinal 2.0 1 0.4273 0.51353
## sleep_dis 5.7 1 1.2267 0.26839
## sensory_process_dis 0.9 1 0.1951 0.65886
## learn_dis 2.3 1 0.4921 0.48320
## gender 23.9 4 1.2914 0.27178
## Residuals 3532.4 762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_1_6, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 777
## Dependent Variable: aq_score
## Type: OLS linear regression
##
## MODEL FIT:
## F(14,762) = 20.343, p = 0.000
## R² = 0.272
## Adj. R² = 0.259
##
## Standard errors:OLS
## ---------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## -------------------------- -------- -------- ------- -------- -------
## (Intercept) 3.000 -1.227 7.227 1.393 0.164
## autism1 2.583 2.213 2.953 13.705 0.000
## adhd1 0.319 -0.087 0.724 1.544 0.123
## anxiety1 0.473 0.055 0.891 2.222 0.027
## depression1 -0.104 -0.523 0.315 -0.486 0.627
## ocd1 -0.395 -1.055 0.265 -1.175 0.240
## epilepsy1 -0.145 -1.367 1.078 -0.232 0.816
## gastrointestinal1 -0.195 -0.783 0.392 -0.654 0.514
## sleep_dis1 -0.301 -0.833 0.232 -1.108 0.268
## sensory_process_dis1 0.237 -0.816 1.290 0.442 0.659
## learn_dis1 0.291 -0.524 1.106 0.702 0.483
## genderFemale 0.132 -4.104 4.368 0.061 0.951
## genderMale 0.022 -4.212 4.255 0.010 0.992
## genderNo answer -2.000 -7.977 3.977 -0.657 0.511
## genderNon-binary 1.433 -3.010 5.876 0.633 0.527
## ---------------------------------------------------------------------
model_1_7 <- lm(aq_score ~ autism + anxiety, data = aq_data_total)
Anova(model_1_7, type = 3)
## Anova Table (Type III tests)
##
## Response: aq_score
## Sum Sq Df F value Pr(>F)
## (Intercept) 4354.7 1 937.7836 <2e-16 ***
## autism 1070.5 1 230.5245 <2e-16 ***
## anxiety 26.8 1 5.7743 0.0165 *
## Residuals 3594.2 774
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
2. Do Autistic Adults Judge Rule Violations Differently? (Study 1
and 2)
2.1 Text and Purpose Violation on Rule Violation Judgments (Study 1
and 2)
str(clean_data_1a$text)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 1 1 ...
str(clean_data_1b$text)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 1 1 ...
str(clean_data_2$text)
## Factor w/ 2 levels "0","1": 2 1 1 2 2 1 2 1 2 2 ...
str(clean_data_viol$text)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 1 1 ...
str(clean_data_1a$purpose)
## Factor w/ 2 levels "0","1": 1 2 1 2 2 1 1 2 1 2 ...
str(clean_data_1b$purpose)
## Factor w/ 2 levels "0","1": 1 2 1 2 2 1 1 2 1 2 ...
str(clean_data_2$purpose)
## Factor w/ 2 levels "0","1": 2 1 2 1 2 2 1 1 2 1 ...
str(clean_data_viol$purpose)
## Factor w/ 2 levels "0","1": 1 2 1 2 2 1 1 2 1 2 ...
str(clean_data_1a$response)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 2 1 ...
str(clean_data_1b$response)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 2 1 ...
str(clean_data_2$response)
## Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 1 2 2 ...
str(clean_data_viol$response)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 2 1 ...
str(clean_data_1a$cw_resp)
## num [1:7900] 97 -98 -98 98 -95 35 -96 99 98 -30 ...
str(clean_data_1b$cw_resp)
## num [1:6340] 97 -98 -98 98 -95 35 -96 99 98 -30 ...
str(clean_data_2$cw_resp)
## num [1:12224] -100 -71 100 -100 100 100 100 -100 100 100 ...
str(clean_data_viol$cw_resp)
## num [1:20124] 97 -98 -98 98 -95 35 -96 99 98 -30 ...
str(clean_data_1a$group)
## Factor w/ 2 levels "NT","ASD": 1 1 1 1 1 1 1 1 1 1 ...
str(clean_data_1b$group)
## Factor w/ 2 levels "NT","ASD": 1 1 1 1 1 1 1 1 1 1 ...
str(clean_data_2$group)
## Factor w/ 2 levels "NT","ASD": 2 2 2 2 2 2 2 2 2 2 ...
str(clean_data_viol$group)
## Factor w/ 2 levels "NT","ASD": 1 1 1 1 1 1 1 1 1 1 ...
str(clean_data_1a$aq_score)
## num [1:7900] 2 2 2 2 2 2 2 2 2 2 ...
str(clean_data_1b$aq_score)
## num [1:6340] 2 2 2 2 2 2 2 2 2 2 ...
str(clean_data_2$aq_score)
## num [1:12224] 2 2 2 2 2 2 2 2 2 2 ...
str(clean_data_viol$aq_score)
## num [1:20124] 2 2 2 2 2 2 2 2 2 2 ...
### STUDY 1
str(clean_data_1a$response)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 2 1 ...
clean_data_1a$response <- factor(clean_data_1a$response)
str(clean_data_1a$response)
## Factor w/ 2 levels "0","1": 2 1 1 2 1 2 1 2 2 1 ...
# Main effects and interaction
model_2_11 <- glmer(response ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_1a, family = 'binomial')
Anova(model_2_11, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 318.021 1 < 2e-16 ***
## text 769.625 1 < 2e-16 ***
## purpose 623.305 1 < 2e-16 ***
## text:purpose 2.852 1 0.09126 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_11, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 7900
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 6390.192, BIC = 6432.040
## Pseudo-R² (fixed effects) = 0.647
## Pseudo-R² (total) = 0.661
##
## FIXED EFFECTS:
## --------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## -------------------- ----------- -------- -------- --------- -------
## (Intercept) 0.029 0.020 0.043 -17.833 0.000
## text1 49.713 37.724 65.512 27.742 0.000
## purpose1 33.045 25.110 43.488 24.966 0.000
## text1:purpose1 0.716 0.486 1.055 -1.689 0.091
## --------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.171
## scene (Intercept) 0.327
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 395 0.009
## scene 5 0.031
## -------------------------------
clean_data_1a %>%
ungroup() %>%
group_by(text, purpose) %>%
summarise(
success = sum(response == 1),
total = n(),
.groups = "drop"
) %>%
mutate(
percent = 100 * success / total,
ci = list(binom.confint(success, total, method = "wilson")),
lower_CI = 100 * ci[[1]]$lower,
upper_CI = 100 * ci[[1]]$upper
) %>%
dplyr::select(text, purpose, percent, lower_CI, upper_CI) %>%
mutate(
percent = format(round(percent, 2), nsmall = 2),
lower_CI = format(round(lower_CI, 2), nsmall = 2),
upper_CI = format(round(upper_CI, 2), nsmall = 2)
)
## # A tibble: 4 × 5
## text purpose percent lower_CI upper_CI
## <fct> <fct> <chr> <chr> <chr>
## 1 0 0 " 3.04" " 2.37" " 3.89"
## 2 0 1 "49.42" "47.22" "51.62"
## 3 1 0 "59.19" "57.01" "61.34"
## 4 1 1 "97.01" "96.17" "97.68"
# Moderation of group
model_2_12 <- glmer(response ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1a, family = 'binomial')
Anova(model_2_12, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 296.679 1 < 2.2e-16 ***
## group 12.885 1 0.0003313 ***
## text 616.202 1 < 2.2e-16 ***
## purpose 507.942 1 < 2.2e-16 ***
## group:text 11.756 1 0.0006065 ***
## group:purpose 12.839 1 0.0003395 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_12, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 7900
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 6382.459, BIC = 6438.256
## Pseudo-R² (fixed effects) = 0.658
## Pseudo-R² (total) = 0.672
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- -------- -------- --------- -------
## (Intercept) 0.022 0.014 0.034 -17.224 0.000
## groupASD 2.087 1.396 3.118 3.590 0.000
## text1 64.303 46.287 89.331 24.823 0.000
## purpose1 43.449 31.299 60.315 22.538 0.000
## groupASD:text1 0.490 0.326 0.737 -3.429 0.001
## groupASD:purpose1 0.475 0.316 0.714 -3.583 0.000
## -----------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.169
## scene (Intercept) 0.328
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 395 0.008
## scene 5 0.031
## -------------------------------
emm <- emmeans(model_2_12, ~ group | text * purpose)
pairs(emm, type = "response") #
## text = 0, purpose = 0:
## contrast odds.ratio SE df null z.ratio p.value
## NT / ASD 0.479 0.0982 Inf 1 -3.590 0.0003
##
## text = 1, purpose = 0:
## contrast odds.ratio SE df null z.ratio p.value
## NT / ASD 0.978 0.0899 Inf 1 -0.243 0.8082
##
## text = 0, purpose = 1:
## contrast odds.ratio SE df null z.ratio p.value
## NT / ASD 1.010 0.0916 Inf 1 0.105 0.9161
##
## text = 1, purpose = 1:
## contrast odds.ratio SE df null z.ratio p.value
## NT / ASD 2.060 0.4330 Inf 1 3.438 0.0006
##
## Tests are performed on the log odds ratio scale
emm <- emmeans(model_2_12, ~ group | text * purpose)
pairs(emm, reverse = TRUE, type = "response", infer = TRUE)
## text = 0, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 2.087 0.4280 Inf 1.396 3.118 1 3.590 0.0003
##
## text = 1, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 1.023 0.0940 Inf 0.854 1.225 1 0.243 0.8082
##
## text = 0, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 0.990 0.0898 Inf 0.829 1.183 1 -0.105 0.9161
##
## text = 1, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 0.485 0.1020 Inf 0.321 0.733 1 -3.438 0.0006
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log odds ratio scale
## Tests are performed on the log odds ratio scale
# Moderation of AQ-10
str(clean_data_1a$aq_score)
## num [1:7900] 2 2 2 2 2 2 2 2 2 2 ...
model_2_13 <- glmer(response ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1a, family = 'binomial')
Anova(model_2_13, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 182.2152 1 < 2e-16 ***
## aq_score 3.5176 1 0.06072 .
## text 363.8658 1 < 2e-16 ***
## purpose 310.4897 1 < 2e-16 ***
## aq_score:text 3.9230 1 0.04763 *
## aq_score:purpose 1.2567 1 0.26228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_13, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 7900
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 6391.346, BIC = 6447.143
## Pseudo-R² (fixed effects) = 0.650
## Pseudo-R² (total) = 0.664
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- -------- -------- --------- -------
## (Intercept) 0.045 0.029 0.071 -13.499 0.000
## aq_score 0.932 0.865 1.003 -1.876 0.061
## text1 31.176 21.895 44.393 19.075 0.000
## purpose1 23.858 16.765 33.952 17.621 0.000
## aq_score:text1 1.079 1.001 1.163 1.981 0.048
## aq_score:purpose1 1.044 0.968 1.125 1.121 0.262
## -----------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.167
## scene (Intercept) 0.328
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 395 0.008
## scene 5 0.031
## -------------------------------
### STUDY 2
str(clean_data_2$response)
## Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 1 2 2 ...
clean_data_2$response <- factor(clean_data_2$response)
str(clean_data_2$response)
## Factor w/ 2 levels "0","1": 1 1 2 1 2 2 2 1 2 2 ...
# Main effects and interaction
model_2_14 <- glmer(response ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_2_14, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 524.5024 1 < 2e-16 ***
## text 1916.5411 1 < 2e-16 ***
## purpose 692.0763 1 < 2e-16 ***
## text:purpose 4.1278 1 0.04218 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_14, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 9182.638, BIC = 9227.105
## Pseudo-R² (fixed effects) = 0.623
## Pseudo-R² (total) = 0.663
##
## FIXED EFFECTS:
## ---------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## -------------------- ----------- -------- --------- --------- -------
## (Intercept) 0.041 0.031 0.054 -22.902 0.000
## text1 86.348 70.724 105.424 43.778 0.000
## purpose1 12.683 10.496 15.326 26.307 0.000
## text1:purpose1 0.744 0.560 0.990 -2.032 0.042
## ---------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.552
## scene (Intercept) 0.292
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.083
## scene 8 0.023
## -------------------------------
clean_data_2 %>%
ungroup() %>%
group_by(text, purpose) %>%
summarise(
success = sum(response == 1),
total = n(),
.groups = "drop"
) %>%
mutate(
percent = 100 * success / total,
ci = list(binom.confint(success, total, method = "wilson")),
lower_CI = 100 * ci[[1]]$lower,
upper_CI = 100 * ci[[1]]$upper
) %>%
dplyr::select(text, purpose, percent, lower_CI, upper_CI) %>%
mutate(
percent = format(round(percent, 2), nsmall = 2),
lower_CI = format(round(lower_CI, 2), nsmall = 2),
upper_CI = format(round(upper_CI, 2), nsmall = 2)
)
## # A tibble: 4 × 5
## text purpose percent lower_CI upper_CI
## <fct> <fct> <chr> <chr> <chr>
## 1 0 0 " 4.71" " 4.02" " 5.52"
## 2 0 1 "35.47" "33.79" "37.18"
## 3 1 0 "76.37" "74.84" "77.85"
## 4 1 1 "96.60" "95.89" "97.18"
# Moderation of group
model_2_15 <- glmer(response ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_2_15, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 504.626 1 < 2.2e-16 ***
## group 44.781 1 2.204e-11 ***
## text 1488.558 1 < 2.2e-16 ***
## purpose 504.504 1 < 2.2e-16 ***
## group:text 40.198 1 2.295e-10 ***
## group:purpose 14.549 1 0.0001366 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_15, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 9131.048, BIC = 9190.337
## Pseudo-R² (fixed effects) = 0.640
## Pseudo-R² (total) = 0.675
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- --------- --------- --------- -------
## (Intercept) 0.025 0.018 0.035 -22.464 0.000
## groupASD 2.859 2.102 3.888 6.692 0.000
## text1 132.242 103.182 169.486 38.582 0.000
## purpose1 16.110 12.641 20.532 22.461 0.000
## groupASD:text1 0.374 0.276 0.506 -6.340 0.000
## groupASD:purpose1 0.558 0.413 0.753 -3.814 0.000
## -------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.519
## scene (Intercept) 0.291
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.074
## scene 8 0.023
## -------------------------------
emm <- emmeans(model_2_15, ~ group | text * purpose)
pairs(emm, reverse = TRUE, type = "response", infer = TRUE)
## text = 0, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 2.859 0.4490 Inf 2.10 3.888 1 6.692 <.0001
##
## text = 1, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 1.068 0.1060 Inf 0.88 1.297 1 0.663 0.5071
##
## text = 0, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 1.594 0.1490 Inf 1.33 1.914 1 4.992 <.0001
##
## text = 1, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 0.595 0.0987 Inf 0.43 0.824 1 -3.128 0.0018
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log odds ratio scale
## Tests are performed on the log odds ratio scale
# Moderation of AQ-10
model_2_16 <- glmer(response ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_2_16, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 376.7704 1 < 2.2e-16 ***
## aq_score 10.7468 1 0.001045 **
## text 1054.0202 1 < 2.2e-16 ***
## purpose 383.9763 1 < 2.2e-16 ***
## aq_score:text 10.2171 1 0.001391 **
## aq_score:purpose 9.0763 1 0.002589 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_16, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 9178.920, BIC = 9238.209
## Pseudo-R² (fixed effects) = 0.630
## Pseudo-R² (total) = 0.668
##
## FIXED EFFECTS:
## ------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- -------- --------- --------- -------
## (Intercept) 0.031 0.022 0.044 -19.411 0.000
## aq_score 1.109 1.042 1.180 3.278 0.001
## text1 110.648 83.282 147.006 32.466 0.000
## purpose1 15.955 12.094 21.048 19.595 0.000
## aq_score:text1 0.907 0.854 0.963 -3.196 0.001
## aq_score:purpose1 0.913 0.860 0.969 -3.013 0.003
## ------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.548
## scene (Intercept) 0.291
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.082
## scene 8 0.023
## -------------------------------
### Collapsed across Study 1 and Study 2
# Main effects and interaction
model_2_17 <- glmer(response ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_viol, family = 'binomial')
Anova(model_2_17, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 695.8709 1 < 2e-16 ***
## text 2664.8777 1 < 2e-16 ***
## purpose 1356.4017 1 < 2e-16 ***
## text:purpose 4.9723 1 0.02576 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_17, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 20124
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 15879.524, BIC = 15926.982
## Pseudo-R² (fixed effects) = 0.628
## Pseudo-R² (total) = 0.655
##
## FIXED EFFECTS:
## --------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## -------------------- ----------- -------- -------- --------- -------
## (Intercept) 0.037 0.029 0.047 -26.379 0.000
## text1 64.160 54.783 75.142 51.622 0.000
## purpose1 18.069 15.490 21.078 36.829 0.000
## text1:purpose1 0.772 0.615 0.969 -2.230 0.026
## --------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.417
## scene (Intercept) 0.282
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 777 0.049
## scene 8 0.023
## -------------------------------
clean_data_viol %>%
ungroup() %>%
group_by(text, purpose) %>%
summarise(
success = sum(response == 1),
total = n(),
.groups = "drop"
) %>%
mutate(
percent = 100 * success / total,
ci = list(binom.confint(success, total, method = "wilson")),
lower_CI = 100 * ci[[1]]$lower,
upper_CI = 100 * ci[[1]]$upper
) %>%
dplyr::select(text, purpose, percent, lower_CI, upper_CI) %>%
mutate(
percent = format(round(percent, 2), nsmall = 2),
lower_CI = format(round(lower_CI, 2), nsmall = 2),
upper_CI = format(round(upper_CI, 2), nsmall = 2)
)
## # A tibble: 4 × 5
## text purpose percent lower_CI upper_CI
## <fct> <fct> <chr> <chr> <chr>
## 1 0 0 " 4.05" " 3.54" " 4.64"
## 2 0 1 "40.95" "39.59" "42.31"
## 3 1 0 "69.63" "68.34" "70.88"
## 4 1 1 "96.76" "96.23" "97.21"
# Moderation of group
model_2_18 <- glmer(response ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_viol, family = 'binomial')
Anova(model_2_18, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 698.007 1 < 2.2e-16 ***
## group 56.852 1 4.699e-14 ***
## text 2073.140 1 < 2.2e-16 ***
## purpose 1036.998 1 < 2.2e-16 ***
## group:text 51.771 1 6.235e-13 ***
## group:purpose 30.203 1 3.890e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_18, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 20124
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 15821.859, BIC = 15885.136
## Pseudo-R² (fixed effects) = 0.643
## Pseudo-R² (total) = 0.667
##
## FIXED EFFECTS:
## ------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- -------- --------- --------- -------
## (Intercept) 0.024 0.018 0.032 -26.420 0.000
## groupASD 2.519 1.981 3.203 7.540 0.000
## text1 94.941 78.042 115.498 45.532 0.000
## purpose1 24.194 19.929 29.372 32.202 0.000
## groupASD:text1 0.413 0.324 0.525 -7.195 0.000
## groupASD:purpose1 0.510 0.401 0.649 -5.496 0.000
## ------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.400
## scene (Intercept) 0.282
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 777 0.045
## scene 8 0.023
## -------------------------------
emm <- emmeans(model_2_18, ~ group | text * purpose)
pairs(emm, reverse = TRUE, type = "response", infer = TRUE)
## text = 0, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 2.519 0.3090 Inf 1.981 3.203 1 7.540 <.0001
##
## text = 1, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 1.040 0.0698 Inf 0.912 1.186 1 0.585 0.5584
##
## text = 0, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 1.286 0.0827 Inf 1.133 1.458 1 3.906 0.0001
##
## text = 1, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 0.531 0.0678 Inf 0.413 0.682 1 -4.962 <.0001
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log odds ratio scale
## Tests are performed on the log odds ratio scale
# Moderation of AQ-10
model_2_19 <- glmer(response ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_viol, family = 'binomial')
Anova(model_2_19, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 486.0963 1 <2e-16 ***
## aq_score 0.8615 1 0.3533
## text 1425.9740 1 <2e-16 ***
## purpose 670.1469 1 <2e-16 ***
## aq_score:text 2.7574 1 0.0968 .
## aq_score:purpose 0.4613 1 0.4970
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_19, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 20124
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 15884.983, BIC = 15948.261
## Pseudo-R² (fixed effects) = 0.630
## Pseudo-R² (total) = 0.657
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- -------- -------- --------- -------
## (Intercept) 0.037 0.028 0.050 -22.048 0.000
## aq_score 1.022 0.976 1.070 0.928 0.353
## text1 66.663 53.607 82.899 37.762 0.000
## purpose1 17.216 13.879 21.355 25.887 0.000
## aq_score:text1 0.962 0.919 1.007 -1.661 0.097
## aq_score:purpose1 0.984 0.940 1.030 -0.679 0.497
## -----------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.415
## scene (Intercept) 0.282
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 777 0.049
## scene 8 0.022
## -------------------------------
2.2 Text and Purpose Violation on Adjusted Confidence (Study 1 and
2)
### STUDY 1
# Main effects and interaction
model_2_21 <- lmer(cw_resp ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_1a)
Anova(model_2_21, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 480.200 1 < 2.2e-16 ***
## text 3074.859 1 < 2.2e-16 ***
## purpose 2287.960 1 < 2.2e-16 ***
## text:purpose 30.926 1 2.68e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_21, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 7900
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 87203.375, BIC = 87252.197
## Pseudo-R² (fixed effects) = 0.532
## Pseudo-R² (total) = 0.543
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## -------------------- --------- --------- --------- --------- ---------- -------
## (Intercept) -90.889 -99.018 -82.760 -21.913 4.736 0.000
## text1 106.325 102.567 110.083 55.451 7497.990 0.000
## purpose1 91.716 87.958 95.475 47.833 7497.990 0.000
## text1:purpose1 -15.080 -20.395 -9.765 -5.561 7497.990 0.000
## -------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 2.388
## scene (Intercept) 8.761
## Residual 60.255
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 395 0.002
## scene 5 0.021
## -------------------------------
clean_data_1a %>%
group_by(text, purpose) %>%
summarise(
mean_cw_resp = mean(cw_resp, na.rm = TRUE),
sd_cw_resp = sd(cw_resp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
mean_cw_resp = format(round(mean_cw_resp, 2), nsmall = 2),
sd_cw_resp = format(round(sd_cw_resp, 2), nsmall = 2)
)
## # A tibble: 4 × 4
## text purpose mean_cw_resp sd_cw_resp
## <fct> <fct> <chr> <chr>
## 1 0 0 "-90.89" 30.17
## 2 0 1 " 0.83" 81.46
## 3 1 0 " 15.44" 80.22
## 4 1 1 " 92.07" 28.46
# Moderation of group
model_2_22 <- lmer(cw_resp ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1a)
Anova(model_2_22, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 445.1384 1 < 2e-16 ***
## group 5.1988 1 0.02260 *
## text 2755.5617 1 < 2e-16 ***
## purpose 2096.1128 1 < 2e-16 ***
## group:text 1.0968 1 0.29497
## group:purpose 5.7157 1 0.01681 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_22, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 7900
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 87224.838, BIC = 87287.610
## Pseudo-R² (fixed effects) = 0.531
## Pseudo-R² (total) = 0.541
##
## FIXED EFFECTS:
## ----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- --------- --------- --------- --------- ---------- -------
## (Intercept) -89.780 -98.121 -81.440 -21.098 5.247 0.000
## groupASD 5.391 0.757 10.024 2.280 2840.092 0.023
## text1 100.189 96.449 103.930 52.493 7497.000 0.000
## purpose1 87.382 83.642 91.123 45.783 7497.000 0.000
## groupASD:text1 -2.845 -8.169 2.479 -1.047 7497.000 0.295
## groupASD:purpose1 -6.494 -11.818 -1.170 -2.391 7497.000 0.017
## ----------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 2.332
## scene (Intercept) 8.760
## Residual 60.356
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 395 0.001
## scene 5 0.021
## -------------------------------
emm <- emmeans(model_2_22, ~ group | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 5.39 2.36 Inf 0.757 10.024 2.280 0.0226
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 2.55 2.36 Inf -2.088 7.179 1.077 0.2816
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -1.10 2.36 Inf -5.737 3.530 -0.467 0.6406
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -3.95 2.36 Inf -8.582 0.685 -1.670 0.0949
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
# Moderation of AQ-10
model_2_23 <- lmer(cw_resp ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1a)
Anova(model_2_23, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 356.6221 1 < 2e-16 ***
## aq_score 1.2933 1 0.25545
## text 1365.8312 1 < 2e-16 ***
## purpose 1182.7807 1 < 2e-16 ***
## aq_score:text 4.7018 1 0.03013 *
## aq_score:purpose 2.4970 1 0.11406
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_23, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 7900
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 87232.927, BIC = 87295.699
## Pseudo-R² (fixed effects) = 0.531
## Pseudo-R² (total) = 0.541
##
## FIXED EFFECTS:
## ----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- --------- --------- --------- --------- ---------- -------
## (Intercept) -84.988 -93.809 -76.168 -18.884 6.564 0.000
## aq_score -0.501 -1.364 0.362 -1.137 2851.453 0.256
## text1 94.114 89.123 99.105 36.957 7497.000 0.000
## purpose1 87.581 82.589 92.572 34.392 7497.000 0.000
## aq_score:text1 1.098 0.105 2.090 2.168 7497.000 0.030
## aq_score:purpose1 -0.800 -1.792 0.192 -1.580 7497.000 0.114
## ----------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 2.164
## scene (Intercept) 8.760
## Residual 60.354
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 395 0.001
## scene 5 0.021
## -------------------------------
### STUDY 2
# Main effects and interaction
model_2_24 <- lmer(cw_resp ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_2_24, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 1081.914 1 < 2.2e-16 ***
## text 7942.452 1 < 2.2e-16 ***
## purpose 1785.186 1 < 2.2e-16 ***
## text:purpose 92.699 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_24, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 134823.314, BIC = 134875.192
## Pseudo-R² (fixed effects) = 0.559
## Pseudo-R² (total) = 0.577
##
## FIXED EFFECTS:
## --------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## -------------------- --------- --------- --------- --------- ----------- -------
## (Intercept) -88.235 -93.492 -82.977 -32.892 9.905 0.000
## text1 135.413 132.435 138.391 89.120 11832.000 0.000
## purpose1 64.199 61.221 67.177 42.251 11832.000 0.000
## text1:purpose1 -20.689 -24.900 -16.477 -9.628 11832.000 0.000
## --------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 10.365
## scene (Intercept) 6.788
## Residual 59.394
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.029
## scene 8 0.013
## -------------------------------
clean_data_2 %>%
group_by(text, purpose) %>%
summarise(
mean_cw_resp = mean(cw_resp, na.rm = TRUE),
sd_cw_resp = sd(cw_resp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
mean_cw_resp = format(round(mean_cw_resp, 2), nsmall = 2),
sd_cw_resp = format(round(sd_cw_resp, 2), nsmall = 2)
)
## # A tibble: 4 × 4
## text purpose mean_cw_resp sd_cw_resp
## <fct> <fct> <chr> <chr>
## 1 0 0 "-88.23" 37.82
## 2 0 1 "-24.04" 83.65
## 3 1 0 " 47.18" 72.59
## 4 1 1 " 90.69" 31.68
# Moderation of group
model_2_25 <- lmer(cw_resp ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_2_25, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 1004.6954 1 < 2.2e-16 ***
## group 38.7504 1 4.816e-10 ***
## text 7649.9429 1 < 2.2e-16 ***
## purpose 1257.5288 1 < 2.2e-16 ***
## group:text 48.1306 1 3.987e-12 ***
## group:purpose 0.0197 1 0.8883
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_25, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 134849.990, BIC = 134916.690
## Pseudo-R² (fixed effects) = 0.558
## Pseudo-R² (total) = 0.576
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- --------- --------- --------- --------- ----------- -------
## (Intercept) -89.604 -95.145 -84.064 -31.697 12.210 0.000
## groupASD 13.222 9.059 17.385 6.225 1551.523 0.000
## text1 132.459 129.491 135.427 87.464 11831.000 0.000
## purpose1 53.705 50.736 56.673 35.462 11831.000 0.000
## groupASD:text1 -14.937 -19.157 -10.717 -6.938 11831.000 0.000
## groupASD:purpose1 0.302 -3.918 4.522 0.140 11831.000 0.888
## -----------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 9.941
## scene (Intercept) 6.788
## Residual 59.508
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.027
## scene 8 0.012
## -------------------------------
emm <- emmeans(model_2_25, ~ group | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 13.22 2.12 Inf 9.06 17.39 6.225 <.0001
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -1.71 2.12 Inf -5.88 2.45 -0.807 0.4195
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 13.52 2.12 Inf 9.36 17.69 6.367 <.0001
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -1.41 2.12 Inf -5.58 2.75 -0.665 0.5060
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
# Moderation of AQ-10
model_2_26 <- lmer(cw_resp ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_2_26, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 763.1118 1 < 2.2e-16 ***
## aq_score 4.9808 1 0.025631 *
## text 4037.9772 1 < 2.2e-16 ***
## purpose 725.8045 1 < 2.2e-16 ***
## aq_score:text 7.8271 1 0.005147 **
## aq_score:purpose 0.5020 1 0.478601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_26, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 134913.861, BIC = 134980.561
## Pseudo-R² (fixed effects) = 0.556
## Pseudo-R² (total) = 0.574
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- --------- --------- --------- --------- ----------- -------
## (Intercept) -86.922 -93.089 -80.755 -27.624 18.710 0.000
## aq_score 1.065 0.130 2.001 2.232 1498.652 0.026
## text1 129.929 125.922 133.937 63.545 11831.000 0.000
## purpose1 55.085 51.078 59.093 26.941 11831.000 0.000
## aq_score:text1 -1.341 -2.281 -0.402 -2.798 11831.000 0.005
## aq_score:purpose1 -0.340 -1.280 0.600 -0.709 11831.000 0.479
## -----------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 10.342
## scene (Intercept) 6.787
## Residual 59.608
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.029
## scene 8 0.012
## -------------------------------
### Collapsed across Study 1 and Study 2
# Main effects and interaction
model_2_27 <- lmer(cw_resp ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_viol)
Anova(model_2_27, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 1142.93 1 < 2.2e-16 ***
## text 10549.79 1 < 2.2e-16 ***
## purpose 3859.91 1 < 2.2e-16 ***
## text:purpose 117.26 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_27, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 20124
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 222600.309, BIC = 222655.677
## Pseudo-R² (fixed effects) = 0.537
## Pseudo-R² (total) = 0.552
##
## FIXED EFFECTS:
## --------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## -------------------- --------- --------- --------- --------- ----------- -------
## (Intercept) -89.691 -94.891 -84.491 -33.807 8.502 0.000
## text1 123.994 121.628 126.360 102.712 19400.961 0.000
## purpose1 75.001 72.635 77.367 62.128 19400.961 0.000
## text1:purpose1 -18.487 -21.833 -15.141 -10.829 19400.961 0.000
## --------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 8.345
## scene (Intercept) 7.037
## Residual 60.547
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 777 0.018
## scene 8 0.013
## -------------------------------
clean_data_viol %>%
group_by(text, purpose) %>%
summarise(
mean_cw_resp = mean(cw_resp, na.rm = TRUE),
sd_cw_resp = sd(cw_resp, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
mean_cw_resp = format(round(mean_cw_resp, 2), nsmall = 2),
sd_cw_resp = format(round(sd_cw_resp, 2), nsmall = 2)
)
## # A tibble: 4 × 4
## text purpose mean_cw_resp sd_cw_resp
## <fct> <fct> <chr> <chr>
## 1 0 0 "-89.28" 35.04
## 2 0 1 "-14.28" 83.67
## 3 1 0 " 34.72" 77.24
## 4 1 1 " 91.23" 30.46
# Moderation of group
model_2_28 <- lmer(cw_resp ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_viol)
Anova(model_2_28, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 1083.9148 1 < 2.2e-16 ***
## group 39.0519 1 4.127e-10 ***
## text 9913.2311 1 < 2.2e-16 ***
## purpose 3096.6623 1 < 2.2e-16 ***
## group:text 35.2804 1 2.855e-09 ***
## group:purpose 1.9633 1 0.1612
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_28, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 20124
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 222666.710, BIC = 222737.897
## Pseudo-R² (fixed effects) = 0.536
## Pseudo-R² (total) = 0.550
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- --------- --------- --------- --------- ----------- -------
## (Intercept) -89.990 -95.347 -84.632 -32.923 9.587 0.000
## groupASD 9.970 6.843 13.097 6.249 4094.947 0.000
## text1 119.775 117.417 122.132 99.565 19397.554 0.000
## purpose1 66.943 64.585 69.301 55.648 19397.554 0.000
## groupASD:text1 -10.163 -13.516 -6.809 -5.940 19397.554 0.000
## groupASD:purpose1 -2.397 -5.751 0.956 -1.401 19397.554 0.161
## -----------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 8.091
## scene (Intercept) 7.035
## Residual 60.676
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 777 0.017
## scene 8 0.013
## -------------------------------
emm <- emmeans(model_2_28, ~ group | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 9.970 1.6 Inf 6.84 13.097 6.249 <.0001
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -0.193 1.6 Inf -3.32 2.934 -0.121 0.9037
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 7.572 1.6 Inf 4.45 10.699 4.746 <.0001
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -2.590 1.6 Inf -5.72 0.536 -1.624 0.1044
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
# Moderation of AQ-10
model_2_29 <- lmer(cw_resp ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_viol)
Anova(model_2_29, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 868.8268 1 < 2e-16 ***
## aq_score 0.2561 1 0.61279
## text 5424.1932 1 < 2e-16 ***
## purpose 1643.4968 1 < 2e-16 ***
## aq_score:text 5.6569 1 0.01739 *
## aq_score:purpose 0.3676 1 0.54430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_29, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 20124
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 222719.424, BIC = 222790.611
## Pseudo-R² (fixed effects) = 0.535
## Pseudo-R² (total) = 0.549
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- --------- --------- --------- --------- ----------- -------
## (Intercept) -85.709 -91.408 -80.010 -29.476 12.250 0.000
## aq_score 0.165 -0.475 0.805 0.506 4307.738 0.613
## text1 117.971 114.831 121.110 73.649 19400.788 0.000
## purpose1 64.937 61.797 68.076 40.540 19400.788 0.000
## aq_score:text1 -0.832 -1.517 -0.146 -2.378 19400.788 0.017
## aq_score:purpose1 0.212 -0.473 0.897 0.606 19400.788 0.544
## -----------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 8.314
## scene (Intercept) 7.039
## Residual 60.721
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 777 0.018
## scene 8 0.013
## -------------------------------
2.3 Literal and Moral Judgments on Rule Violation Judgments (Study
1)
str(clean_data_1b$moral_response)
## num [1:6340] 1.5 0.5 -2.5 1.5 1.5 -2.5 -2.5 2.5 -2.5 1.5 ...
str(clean_data_1b$letter_response)
## num [1:6340] 2.5 -2.5 -2.5 2.5 -2.5 2.5 -2.5 2.5 -2.5 -2.5 ...
### STUDY 1
table(clean_data_1b$moral_response)
##
## -2.5 -1.5 -0.5 0.5 1.5 2.5
## 1990 659 319 621 996 1755
class(clean_data_1b$moral_response)
## [1] "numeric"
table(clean_data_1b$letter_response)
##
## -2.5 -1.5 -0.5 0.5 1.5 2.5
## 2120 566 221 302 631 2500
class(clean_data_1b$letter_response)
## [1] "numeric"
# Main effects and interaction
model_2_31 <- glmer(response ~ moral_response + letter_response + (1 | scene) + (1 | subject_nr),
data = clean_data_1b, family = 'binomial')
Anova(model_2_31, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 0.4172 1 0.5183
## moral_response 804.9411 1 <2e-16 ***
## letter_response 1291.8162 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_31, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 4857.672, BIC = 4891.445
## Pseudo-R² (fixed effects) = 0.623
## Pseudo-R² (total) = 0.646
##
## FIXED EFFECTS:
## ------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## --------------------- ----------- ------- ------- -------- -------
## (Intercept) 0.941 0.783 1.131 -0.646 0.518
## moral_response 1.859 1.781 1.940 28.371 0.000
## letter_response 2.045 1.967 2.126 35.942 0.000
## ------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.425
## scene (Intercept) 0.185
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.051
## scene 5 0.010
## -------------------------------
# Moderation by group
model_2_32 <- glmer(response ~ group * (moral_response + letter_response) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b, family = 'binomial')
Anova(model_2_32, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 0.3943 1 0.530062
## group 0.0048 1 0.944937
## moral_response 493.4005 1 < 2.2e-16 ***
## letter_response 713.7591 1 < 2.2e-16 ***
## group:moral_response 8.0065 1 0.004661 **
## group:letter_response 1.9544 1 0.162109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_32, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 4855.355, BIC = 4909.392
## Pseudo-R² (fixed effects) = 0.626
## Pseudo-R² (total) = 0.647
##
## FIXED EFFECTS:
## ---------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ------------------------------ ----------- ------- ------- -------- -------
## (Intercept) 0.937 0.766 1.147 -0.628 0.530
## groupASD 1.006 0.848 1.193 0.069 0.945
## moral_response 1.968 1.854 2.089 22.213 0.000
## letter_response 2.099 1.988 2.216 26.716 0.000
## groupASD:moral_response 0.890 0.821 0.965 -2.830 0.005
## groupASD:letter_response 0.949 0.881 1.021 -1.398 0.162
## ---------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.403
## scene (Intercept) 0.184
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.047
## scene 5 0.010
## -------------------------------
emtrends(model_2_32, pairwise ~ group, var = 'letter_response')
## $emtrends
## group letter_response.trend SE df asymp.LCL asymp.UCL
## NT 0.741 0.0278 Inf 0.687 0.796
## ASD 0.689 0.0272 Inf 0.635 0.742
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df z.ratio p.value
## NT - ASD 0.0529 0.0378 Inf 1.398 0.1621
# Moderation by AQ-10
model_2_33 <- glmer(response ~ aq_score * (moral_response + letter_response) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b, family = 'binomial')
Anova(model_2_33, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 0.1585 1 0.69052
## aq_score 0.0413 1 0.83895
## moral_response 220.1578 1 < 2e-16 ***
## letter_response 389.7962 1 < 2e-16 ***
## aq_score:moral_response 3.6272 1 0.05684 .
## aq_score:letter_response 1.2815 1 0.25761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_33, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 4859.739, BIC = 4913.776
## Pseudo-R² (fixed effects) = 0.624
## Pseudo-R² (total) = 0.647
##
## FIXED EFFECTS:
## ---------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ------------------------------ ----------- ------- ------- -------- -------
## (Intercept) 0.955 0.759 1.200 -0.398 0.691
## aq_score 0.997 0.965 1.029 -0.203 0.839
## moral_response 1.750 1.626 1.885 14.838 0.000
## letter_response 1.981 1.851 2.120 19.743 0.000
## aq_score:moral_response 1.015 1.000 1.030 1.905 0.057
## aq_score:letter_response 1.008 0.994 1.022 1.132 0.258
## ---------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.426
## scene (Intercept) 0.186
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.052
## scene 5 0.010
## -------------------------------
2.4 Literal and Moral Judgments on Adjusted Confidence (Study
1)
### STUDY 1
# Main effects and interaction
model_2_41 <- lmer(cw_resp ~ moral_response + letter_response + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_2_41, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0036 1 0.9523
## moral_response 1994.7554 1 <2e-16 ***
## letter_response 3601.4550 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_41, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 69372.559, BIC = 69413.087
## Pseudo-R² (fixed effects) = 0.583
## Pseudo-R² (total) = 0.598
##
## FIXED EFFECTS:
## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## --------------------- -------- -------- -------- -------- ---------- -------
## (Intercept) 0.144 -4.590 4.879 0.060 4.438 0.955
## moral_response 16.894 16.153 17.636 44.663 6308.919 0.000
## letter_response 20.920 20.237 21.603 60.012 6163.426 0.000
## ----------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 9.636
## scene (Intercept) 5.015
## Residual 56.789
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.028
## scene 5 0.008
## -------------------------------
# Moderation by group
model_2_42 <- lmer(cw_resp ~ group * (moral_response + letter_response) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_2_42, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0007 1 0.979090
## group 0.0389 1 0.843647
## moral_response 1210.2263 1 < 2.2e-16 ***
## letter_response 1831.3221 1 < 2.2e-16 ***
## group:moral_response 10.4287 1 0.001241 **
## group:letter_response 1.5386 1 0.214823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_42, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 69362.907, BIC = 69423.699
## Pseudo-R² (fixed effects) = 0.583
## Pseudo-R² (total) = 0.597
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ------------------------------ -------- -------- -------- -------- ---------- -------
## (Intercept) -0.067 -5.082 4.948 -0.026 5.598 0.980
## groupASD 0.351 -3.134 3.836 0.197 307.053 0.844
## moral_response 18.027 17.011 19.043 34.788 6233.442 0.000
## letter_response 20.503 19.564 21.442 42.794 6138.361 0.000
## groupASD:moral_response -2.431 -3.907 -0.956 -3.229 6312.919 0.001
## groupASD:letter_response 0.865 -0.502 2.231 1.240 6160.543 0.215
## -------------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 9.375
## scene (Intercept) 5.012
## Residual 56.783
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.026
## scene 5 0.008
## -------------------------------
# Moderation by AQ-10
model_2_43 <- lmer(cw_resp ~ aq_score * (moral_response + letter_response) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_2_43, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 0.0101 1 0.9200
## aq_score 0.0083 1 0.9275
## moral_response 600.7782 1 <2e-16 ***
## letter_response 1005.0349 1 <2e-16 ***
## aq_score:moral_response 0.0005 1 0.9818
## aq_score:letter_response 1.9505 1 0.1625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_43, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 69381.296, BIC = 69442.088
## Pseudo-R² (fixed effects) = 0.583
## Pseudo-R² (total) = 0.598
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ------------------------------ -------- -------- -------- -------- ---------- -------
## (Intercept) 0.281 -5.205 5.767 0.100 7.953 0.923
## aq_score -0.030 -0.675 0.615 -0.091 309.473 0.928
## moral_response 16.889 15.539 18.240 24.511 6282.959 0.000
## letter_response 20.178 18.930 21.425 31.702 6137.357 0.000
## aq_score:moral_response 0.003 -0.263 0.269 0.023 6261.272 0.982
## aq_score:letter_response 0.174 -0.070 0.418 1.397 6116.676 0.163
## -------------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 9.676
## scene (Intercept) 5.020
## Residual 56.788
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.028
## scene 5 0.008
## -------------------------------
2.5 Literal Judgments and Affective Inference on Rule Violation
Judgments (Study 2)
str(clean_data_2$response_literal_meaning)
## num [1:12224] 1 1 1 0 1 0 1 0 1 1 ...
str(clean_data_2$response_upset_rating)
## num [1:12224] 1.5 1.5 1.5 -1.5 1.5 1.5 -0.5 -1.5 1.5 0.5 ...
### STUDY 2
# Main effect
model_2_51 <- glmer(response ~ response_literal_meaning + response_upset_rating + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_2_51, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 785.29 1 < 2.2e-16 ***
## response_literal_meaning 2992.68 1 < 2.2e-16 ***
## response_upset_rating 1246.33 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_51, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 6431.884, BIC = 6468.939
## Pseudo-R² (fixed effects) = 0.728
## Pseudo-R² (total) = 0.752
##
## FIXED EFFECTS:
## ------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ------------------------------ ----------- -------- -------- --------- -------
## (Intercept) 0.097 0.082 0.114 -28.023 0.000
## response_literal_meaning 71.639 61.473 83.486 54.705 0.000
## response_upset_rating 3.006 2.828 3.196 35.303 0.000
## ------------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.554
## scene (Intercept) 0.154
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.085
## scene 8 0.007
## -------------------------------
# Moderation of group
model_2_52 <- glmer(response ~ group * (response_literal_meaning + response_upset_rating) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_2_52, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 603.762 1 < 2.2e-16 ***
## group 37.710 1 8.208e-10 ***
## response_literal_meaning 1598.920 1 < 2.2e-16 ***
## response_upset_rating 630.186 1 < 2.2e-16 ***
## group:response_literal_meaning 53.487 1 2.602e-13 ***
## group:response_upset_rating 6.166 1 0.01302 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_52, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 6380.077, BIC = 6439.367
## Pseudo-R² (fixed effects) = 0.737
## Pseudo-R² (total) = 0.761
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5%
## --------------------------------------- ----------- --------- ---------
## (Intercept) 0.063 0.051 0.079
## groupASD 2.172 1.695 2.781
## response_literal_meaning 131.401 103.456 166.895
## response_upset_rating 3.353 3.050 3.685
## groupASD:response_literal_meaning 0.333 0.248 0.447
## groupASD:response_upset_rating 0.856 0.757 0.968
## -----------------------------------------------------------------------
##
## ---------------------------------------------------------
## z val. p
## --------------------------------------- --------- -------
## (Intercept) -24.572 0.000
## groupASD 6.141 0.000
## response_literal_meaning 39.986 0.000
## response_upset_rating 25.104 0.000
## groupASD:response_literal_meaning -7.314 0.000
## groupASD:response_upset_rating -2.483 0.013
## ---------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.557
## scene (Intercept) 0.156
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.086
## scene 8 0.007
## -------------------------------
# Moderation of AQ-10
model_2_53 <- glmer(response ~ aq_score * (response_literal_meaning + response_upset_rating) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_2_53, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 384.3531 1 < 2.2e-16 ***
## aq_score 4.3410 1 0.037206 *
## response_literal_meaning 1047.1317 1 < 2.2e-16 ***
## response_upset_rating 434.9097 1 < 2.2e-16 ***
## aq_score:response_literal_meaning 8.8176 1 0.002983 **
## aq_score:response_upset_rating 3.8803 1 0.048857 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_53, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 6428.578, BIC = 6487.867
## Pseudo-R² (fixed effects) = 0.730
## Pseudo-R² (total) = 0.754
##
## FIXED EFFECTS:
## --------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val.
## --------------------------------------- ----------- -------- --------- ---------
## (Intercept) 0.079 0.061 0.102 -19.605
## aq_score 1.055 1.003 1.110 2.083
## response_literal_meaning 100.845 76.260 133.356 32.359
## response_upset_rating 3.297 2.947 3.688 20.854
## aq_score:response_literal_meaning 0.914 0.861 0.970 -2.969
## aq_score:response_upset_rating 0.976 0.952 1.000 -1.970
## --------------------------------------------------------------------------------
##
## -----------------------------------------------
## p
## --------------------------------------- -------
## (Intercept) 0.000
## aq_score 0.037
## response_literal_meaning 0.000
## response_upset_rating 0.000
## aq_score:response_literal_meaning 0.003
## aq_score:response_upset_rating 0.049
## -----------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.557
## scene (Intercept) 0.153
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.086
## scene 8 0.006
## -------------------------------
2.6 Literal Judgments and Affective Inference on Adjusted Confidence
(Study 2)
### STUDY 2
# Main effect
model_2_61 <- lmer(cw_resp ~ response_literal_meaning + response_upset_rating + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_2_61, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 2598.2 1 < 2.2e-16 ***
## response_literal_meaning 13511.5 1 < 2.2e-16 ***
## response_upset_rating 3247.1 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_61, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 129147.463, BIC = 129191.930
## Pseudo-R² (fixed effects) = 0.726
## Pseudo-R² (total) = 0.734
##
## FIXED EFFECTS:
## ----------------------------------------------------------------------
## Est. 2.5% 97.5% t val.
## ------------------------------ --------- --------- --------- ---------
## (Intercept) -61.271 -63.627 -58.915 -50.973
## response_literal_meaning 118.026 116.036 120.016 116.239
## response_upset_rating 22.563 21.787 23.339 56.984
## ----------------------------------------------------------------------
##
## --------------------------------------------------
## d.f. p
## ------------------------------ ----------- -------
## (Intercept) 14.568 0.000
## response_literal_meaning 12102.346 0.000
## response_upset_rating 12182.775 0.000
## --------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 7.850
## scene (Intercept) 2.559
## Residual 47.130
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.027
## scene 8 0.003
## -------------------------------
# Moderation by group
model_2_62 <- lmer(cw_resp ~ group * (response_literal_meaning + response_upset_rating) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_2_62, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 2151.761 1 < 2.2e-16 ***
## group 42.598 1 6.723e-11 ***
## response_literal_meaning 8373.255 1 < 2.2e-16 ***
## response_upset_rating 1615.122 1 < 2.2e-16 ***
## group:response_literal_meaning 76.571 1 < 2.2e-16 ***
## group:response_upset_rating 11.133 1 0.0008481 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_62, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 129067.696, BIC = 129134.396
## Pseudo-R² (fixed effects) = 0.728
## Pseudo-R² (total) = 0.736
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val.
## --------------------------------------- --------- --------- --------- ---------
## (Intercept) -66.087 -68.879 -63.295 -46.387
## groupASD 10.353 7.244 13.463 6.527
## response_literal_meaning 126.305 123.599 129.010 91.505
## response_upset_rating 21.497 20.449 22.546 40.189
## groupASD:response_literal_meaning -17.751 -21.727 -13.775 -8.751
## groupASD:response_upset_rating 2.639 1.089 4.188 3.337
## -------------------------------------------------------------------------------
##
## -----------------------------------------------------------
## d.f. p
## --------------------------------------- ----------- -------
## (Intercept) 28.085 0.000
## groupASD 1199.063 0.000
## response_literal_meaning 12066.969 0.000
## response_upset_rating 12170.383 0.000
## groupASD:response_literal_meaning 12098.118 0.000
## groupASD:response_upset_rating 12192.804 0.001
## -----------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 7.959
## scene (Intercept) 2.573
## Residual 46.971
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.028
## scene 8 0.003
## -------------------------------
# Moderation by AQ-10
model_2_63 <- lmer(cw_resp ~ aq_score * (response_literal_meaning + response_upset_rating) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_2_63, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 1368.2939 1 < 2.2e-16 ***
## aq_score 4.5743 1 0.0324550 *
## response_literal_meaning 4419.0200 1 < 2.2e-16 ***
## response_upset_rating 953.6077 1 < 2.2e-16 ***
## aq_score:response_literal_meaning 12.4876 1 0.0004097 ***
## aq_score:response_upset_rating 0.4428 1 0.5057548
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_2_63, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 129142.737, BIC = 129209.437
## Pseudo-R² (fixed effects) = 0.727
## Pseudo-R² (total) = 0.735
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val.
## --------------------------------------- --------- --------- --------- ---------
## (Intercept) -63.945 -67.333 -60.556 -36.990
## aq_score 0.728 0.061 1.396 2.139
## response_literal_meaning 123.509 119.868 127.151 66.476
## response_upset_rating 22.127 20.723 23.532 30.881
## aq_score:response_literal_meaning -1.494 -2.323 -0.666 -3.534
## aq_score:response_upset_rating 0.109 -0.212 0.431 0.665
## -------------------------------------------------------------------------------
##
## -----------------------------------------------------------
## d.f. p
## --------------------------------------- ----------- -------
## (Intercept) 61.389 0.000
## aq_score 1073.497 0.033
## response_literal_meaning 12044.702 0.000
## response_upset_rating 12162.090 0.000
## aq_score:response_literal_meaning 12010.804 0.000
## aq_score:response_upset_rating 12103.218 0.506
## -----------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 7.877
## scene (Intercept) 2.549
## Residual 47.107
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.027
## scene 8 0.003
## -------------------------------
3. Do Literal and Moral Judgments Differentially Track Text and
Purpose Violations? (Study 1 and 2)
3.1 Text and Purpose Violation on Literal Judgments (Study 1 and
2)
### STUDY 1
# Main effects and interaction
model_3_11 <- lmer(letter_response ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_3_11, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: letter_response
## Chisq Df Pr(>Chisq)
## (Intercept) 994.499 1 < 2.2e-16 ***
## text 8104.979 1 < 2.2e-16 ***
## purpose 485.840 1 < 2.2e-16 ***
## text:purpose 17.653 1 2.651e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_11, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: letter_response
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 20320.010, BIC = 20367.292
## Pseudo-R² (fixed effects) = 0.706
## Pseudo-R² (total) = 0.718
##
## FIXED EFFECTS:
## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## -------------------- -------- -------- -------- --------- ---------- -------
## (Intercept) -2.122 -2.254 -1.990 -31.536 5.882 0.000
## text1 3.785 3.703 3.868 90.028 6016.000 0.000
## purpose1 0.927 0.844 1.009 22.042 6016.000 0.000
## text1:purpose1 -0.250 -0.366 -0.133 -4.202 6016.000 0.000
## ----------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.204
## scene (Intercept) 0.133
## Residual 1.184
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.028
## scene 5 0.012
## -------------------------------
clean_data_1b %>%
group_by(text, purpose) %>%
summarise(
mean_letter = mean(letter_response, na.rm = TRUE),
sd_letter = sd(letter_response, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
mean_letter = format(round(mean_letter, 2), nsmall = 2),
sd_letter = format(round(sd_letter, 2), nsmall = 2)
)
## # A tibble: 4 × 4
## text purpose mean_letter sd_letter
## <fct> <fct> <chr> <chr>
## 1 0 0 "-2.12" 1.05
## 2 0 1 "-1.20" 1.66
## 3 1 0 " 1.66" 1.30
## 4 1 1 " 2.34" 0.53
# Moderation by group
model_3_12 <- lmer(letter_response ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_3_12, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: letter_response
## Chisq Df Pr(>Chisq)
## (Intercept) 920.8451 1 < 2.2e-16 ***
## group 12.5636 1 0.0003933 ***
## text 8524.5845 1 < 2.2e-16 ***
## purpose 392.5206 1 < 2.2e-16 ***
## group:text 28.3743 1 9.998e-08 ***
## group:purpose 0.3267 1 0.5676318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_12, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: letter_response
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 20321.103, BIC = 20381.895
## Pseudo-R² (fixed effects) = 0.707
## Pseudo-R² (total) = 0.719
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- -------- -------- -------- --------- ---------- -------
## (Intercept) -2.156 -2.295 -2.017 -30.345 7.308 0.000
## groupASD 0.200 0.089 0.310 3.545 1474.835 0.000
## text1 3.813 3.732 3.894 92.329 6015.000 0.000
## purpose1 0.818 0.737 0.899 19.812 6015.000 0.000
## groupASD:text1 -0.317 -0.433 -0.200 -5.327 6015.000 0.000
## groupASD:purpose1 -0.034 -0.151 0.083 -0.572 6015.000 0.568
## -------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.205
## scene (Intercept) 0.133
## Residual 1.183
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.029
## scene 5 0.012
## -------------------------------
emm <- emmeans(model_3_12, ~ group | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 0.200 0.0564 Inf 0.0894 0.31042 3.545 0.0004
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -0.117 0.0564 Inf -0.2273 -0.00626 -2.071 0.0384
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 0.166 0.0564 Inf 0.0554 0.27644 2.942 0.0033
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -0.151 0.0564 Inf -0.2613 -0.04024 -2.674 0.0075
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
# Moderation by AQ-10
model_3_13 <- lmer(letter_response ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_3_13, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: letter_response
## Chisq Df Pr(>Chisq)
## (Intercept) 652.7635 1 <2e-16 ***
## aq_score 0.6960 1 0.4041
## text 4230.8335 1 <2e-16 ***
## purpose 230.6952 1 <2e-16 ***
## aq_score:text 1.4606 1 0.2268
## aq_score:purpose 0.7217 1 0.3956
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_13, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: letter_response
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 20357.226, BIC = 20418.017
## Pseudo-R² (fixed effects) = 0.706
## Pseudo-R² (total) = 0.718
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- -------- -------- -------- --------- ---------- -------
## (Intercept) -2.023 -2.178 -1.868 -25.549 11.253 0.000
## aq_score -0.009 -0.029 0.012 -0.834 1483.389 0.404
## text1 3.604 3.495 3.713 65.045 6015.000 0.000
## purpose1 0.842 0.733 0.950 15.189 6015.000 0.000
## aq_score:text1 0.013 -0.008 0.035 1.209 6015.000 0.227
## aq_score:purpose1 -0.009 -0.031 0.012 -0.850 6015.000 0.396
## -------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.204
## scene (Intercept) 0.133
## Residual 1.185
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.028
## scene 5 0.012
## -------------------------------
### STUDY 2
# Main effects and interaction
model_3_14 <- glmer(response_literal_meaning ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_3_14, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_literal_meaning
## Chisq Df Pr(>Chisq)
## (Intercept) 374.6022 1 <2e-16 ***
## text 2480.1146 1 <2e-16 ***
## purpose 360.8950 1 <2e-16 ***
## text:purpose 0.0653 1 0.7983
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_14, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_literal_meaning
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 7423.074, BIC = 7467.541
## Pseudo-R² (fixed effects) = 0.663
## Pseudo-R² (total) = 0.707
##
## FIXED EFFECTS:
## ----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## -------------------- ----------- --------- --------- --------- -------
## (Intercept) 0.059 0.044 0.078 -19.355 0.000
## text1 178.154 145.283 218.463 49.801 0.000
## purpose1 5.129 4.333 6.071 18.997 0.000
## text1:purpose1 1.043 0.756 1.437 0.256 0.798
## ----------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.614
## scene (Intercept) 0.341
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.100
## scene 8 0.031
## -------------------------------
clean_data_2 %>%
ungroup() %>%
group_by(text, purpose) %>%
summarise(
success = sum(response_literal_meaning == 1),
total = n(),
.groups = "drop"
) %>%
mutate(
percent = 100 * success / total,
ci = list(binom.confint(success, total, method = "wilson")),
lower_CI = 100 * ci[[1]]$lower,
upper_CI = 100 * ci[[1]]$upper
) %>%
dplyr::select(text, purpose, percent, lower_CI, upper_CI) %>%
mutate(
percent = format(round(percent, 2), nsmall = 2),
lower_CI = format(round(lower_CI, 2), nsmall = 2),
upper_CI = format(round(upper_CI, 2), nsmall = 2)
)
## # A tibble: 4 × 5
## text purpose percent lower_CI upper_CI
## <fct> <fct> <chr> <chr> <chr>
## 1 0 0 " 6.81" " 5.97" " 7.75"
## 2 0 1 "25.20" "23.69" "26.77"
## 3 1 0 "89.76" "88.63" "90.78"
## 4 1 1 "97.84" "97.26" "98.30"
# Moderation by group
model_3_15 <- glmer(response_literal_meaning ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_3_15, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_literal_meaning
## Chisq Df Pr(>Chisq)
## (Intercept) 375.7657 1 < 2.2e-16 ***
## group 37.0984 1 1.123e-09 ***
## text 1824.0077 1 < 2.2e-16 ***
## purpose 237.4191 1 < 2.2e-16 ***
## group:text 57.4970 1 3.385e-14 ***
## group:purpose 6.5207 1 0.01066 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_15, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_literal_meaning
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 7351.515, BIC = 7410.804
## Pseudo-R² (fixed effects) = 0.674
## Pseudo-R² (total) = 0.716
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- --------- --------- --------- -------
## (Intercept) 0.034 0.024 0.048 -19.385 0.000
## groupASD 2.555 1.890 3.456 6.091 0.000
## text1 365.342 278.672 478.968 42.708 0.000
## purpose1 6.697 5.258 8.530 15.408 0.000
## groupASD:text1 0.284 0.205 0.394 -7.583 0.000
## groupASD:purpose1 0.676 0.501 0.913 -2.554 0.011
## -------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.606
## scene (Intercept) 0.344
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.097
## scene 8 0.031
## -------------------------------
emm <- emmeans(model_3_15, ~ group | text * purpose)
pairs(emm, reverse = TRUE, type = "response", infer = TRUE)
## text = 0, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 2.555 0.3940 Inf 1.890 3.46 1 6.091 <.0001
##
## text = 1, purpose = 0:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 0.727 0.0953 Inf 0.562 0.94 1 -2.435 0.0149
##
## text = 0, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 1.727 0.1830 Inf 1.404 2.13 1 5.166 <.0001
##
## text = 1, purpose = 1:
## contrast odds.ratio SE df asymp.LCL asymp.UCL null z.ratio p.value
## ASD / NT 0.491 0.0886 Inf 0.345 0.70 1 -3.940 0.0001
##
## Confidence level used: 0.95
## Intervals are back-transformed from the log odds ratio scale
## Tests are performed on the log odds ratio scale
# Moderation by AQ-10
model_3_16 <- glmer(response_literal_meaning ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = 'binomial')
Anova(model_3_16, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_literal_meaning
## Chisq Df Pr(>Chisq)
## (Intercept) 280.3080 1 < 2.2e-16 ***
## aq_score 7.1853 1 0.0073503 **
## text 1278.5210 1 < 2.2e-16 ***
## purpose 182.6572 1 < 2.2e-16 ***
## aq_score:text 10.9735 1 0.0009243 ***
## aq_score:purpose 5.4608 1 0.0194482 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_16, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_literal_meaning
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 7415.875, BIC = 7475.164
## Pseudo-R² (fixed effects) = 0.665
## Pseudo-R² (total) = 0.709
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ----------------------- ----------- --------- --------- --------- -------
## (Intercept) 0.042 0.029 0.061 -16.742 0.000
## aq_score 1.088 1.023 1.158 2.681 0.007
## text1 274.129 201.521 372.897 35.756 0.000
## purpose1 6.865 5.192 9.077 13.515 0.000
## aq_score:text1 0.897 0.840 0.956 -3.313 0.001
## aq_score:purpose1 0.930 0.875 0.988 -2.337 0.019
## -------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.616
## scene (Intercept) 0.342
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.100
## scene 8 0.031
## -------------------------------
3.2 Text and Purpose Violation on Moral Judgments (Study 1)
# Main effects and interaction
model_3_21 <- lmer(moral_response ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_3_21, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: moral_response
## Chisq Df Pr(>Chisq)
## (Intercept) 303.45 1 < 2.2e-16 ***
## text 749.93 1 < 2.2e-16 ***
## purpose 7922.55 1 < 2.2e-16 ***
## text:purpose 111.41 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_21, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: moral_response
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 19929.659, BIC = 19976.941
## Pseudo-R² (fixed effects) = 0.651
## Pseudo-R² (total) = 0.709
##
## FIXED EFFECTS:
## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## -------------------- -------- -------- -------- --------- ---------- -------
## (Intercept) -2.160 -2.403 -1.917 -17.420 4.686 0.000
## text1 1.091 1.013 1.170 27.385 6016.000 0.000
## purpose1 3.548 3.470 3.626 89.009 6016.000 0.000
## text1:purpose1 -0.595 -0.705 -0.484 -10.555 6016.000 0.000
## ----------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.426
## scene (Intercept) 0.265
## Residual 1.122
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.120
## scene 5 0.046
## -------------------------------
clean_data_1b %>%
group_by(text, purpose) %>%
summarise(
mean_moral_response = mean(moral_response, na.rm = TRUE),
sd_moral_response = sd(moral_response, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
mean_moral_response = format(round(mean_moral_response, 2), nsmall = 2),
sd_moral_response = format(round(sd_moral_response, 2), nsmall = 2)
)
## # A tibble: 4 × 4
## text purpose mean_moral_response sd_moral_response
## <fct> <fct> <chr> <chr>
## 1 0 0 "-2.16" 0.92
## 2 0 1 " 1.39" 1.30
## 3 1 0 "-1.07" 1.53
## 4 1 1 " 1.88" 1.05
# Moderation by group
model_3_22 <- lmer(moral_response ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_3_22, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: moral_response
## Chisq Df Pr(>Chisq)
## (Intercept) 271.5608 1 < 2.2e-16 ***
## group 7.5537 1 0.005989 **
## text 486.2911 1 < 2.2e-16 ***
## purpose 7518.0953 1 < 2.2e-16 ***
## group:text 7.4858 1 0.006219 **
## group:purpose 36.9093 1 1.238e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_22, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: moral_response
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 20006.430, BIC = 20067.221
## Pseudo-R² (fixed effects) = 0.648
## Pseudo-R² (total) = 0.706
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- -------- -------- -------- --------- ---------- -------
## (Intercept) -2.102 -2.352 -1.852 -16.479 5.248 0.000
## groupASD 0.188 0.054 0.323 2.748 717.914 0.006
## text1 0.869 0.792 0.946 22.052 6015.000 0.000
## purpose1 3.416 3.339 3.494 86.707 6015.000 0.000
## groupASD:text1 -0.155 -0.266 -0.044 -2.736 6015.000 0.006
## groupASD:purpose1 -0.345 -0.456 -0.233 -6.075 6015.000 0.000
## -------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.425
## scene (Intercept) 0.265
## Residual 1.128
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.119
## scene 5 0.046
## -------------------------------
emm <- emmeans(model_3_22, ~ group | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 0.1884 0.0686 Inf 0.054 0.3228 2.748 0.0060
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 0.0332 0.0686 Inf -0.101 0.1676 0.485 0.6278
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -0.1562 0.0686 Inf -0.291 -0.0218 -2.278 0.0227
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -0.3113 0.0686 Inf -0.446 -0.1770 -4.542 <.0001
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
# Moderation by AQ-10
model_3_23 <- lmer(moral_response ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_1b)
Anova(model_3_23, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: moral_response
## Chisq Df Pr(>Chisq)
## (Intercept) 225.9284 1 <2e-16 ***
## aq_score 0.0337 1 0.8544
## text 244.7435 1 <2e-16 ***
## purpose 3872.8022 1 <2e-16 ***
## aq_score:text 0.5824 1 0.4454
## aq_score:purpose 0.9657 1 0.3258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_3_23, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 6340
## Dependent Variable: moral_response
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 20060.073, BIC = 20120.864
## Pseudo-R² (fixed effects) = 0.646
## Pseudo-R² (total) = 0.704
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- -------- -------- -------- --------- ---------- -------
## (Intercept) -2.021 -2.285 -1.758 -15.031 6.483 0.000
## aq_score 0.002 -0.022 0.027 0.184 720.095 0.854
## text1 0.828 0.724 0.932 15.644 6015.001 0.000
## purpose1 3.294 3.190 3.398 62.232 6015.001 0.000
## aq_score:text1 -0.008 -0.028 0.012 -0.763 6015.001 0.445
## aq_score:purpose1 -0.010 -0.031 0.010 -0.983 6015.001 0.326
## -------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.426
## scene (Intercept) 0.265
## Residual 1.132
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 317 0.118
## scene 5 0.046
## -------------------------------
4. The Role of Morality and Mentalizing (Study 1 and 2)
4.1 Does Affective Inference Differentially Track Text and Purpose
Violations? (Study 2)
str(clean_data_2$purpose_display)
## Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
# Main effects and interactions
model_4_11 <- lmer(response_upset_rating ~ text * purpose + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_11, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_upset_rating
## Chisq Df Pr(>Chisq)
## (Intercept) 884.92 1 < 2.2e-16 ***
## text 3254.96 1 < 2.2e-16 ***
## purpose 7433.69 1 < 2.2e-16 ***
## text:purpose 216.05 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_11, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_upset_rating
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 30599.310, BIC = 30651.188
## Pseudo-R² (fixed effects) = 0.548
## Pseudo-R² (total) = 0.583
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## -------------------- -------- -------- -------- --------- ----------- -------
## (Intercept) -1.281 -1.366 -1.197 -29.748 9.831 0.000
## text1 1.210 1.168 1.251 57.052 11832.000 0.000
## purpose1 1.828 1.787 1.870 86.219 11832.000 0.000
## text1:purpose1 -0.441 -0.500 -0.382 -14.699 11832.000 0.000
## -----------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.215
## scene (Intercept) 0.110
## Residual 0.829
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.062
## scene 8 0.016
## -------------------------------
clean_data_2 %>%
group_by(text, purpose) %>%
summarise(
mean_response_upset_rating = mean(response_upset_rating, na.rm = TRUE),
sd_response_upset_rating = sd(response_upset_rating, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
mean_response_upset_rating = format(round(mean_response_upset_rating, 2), nsmall = 2),
sd_response_upset_rating = format(round(sd_response_upset_rating, 2), nsmall = 2)
)
## # A tibble: 4 × 4
## text purpose mean_response_upset_rating sd_response_upset_rating
## <fct> <fct> <chr> <chr>
## 1 0 0 "-1.28" 0.68
## 2 0 1 " 0.55" 1.05
## 3 1 0 "-0.07" 1.08
## 4 1 1 " 1.32" 0.50
# Moderation by group
model_4_12 <- lmer(response_upset_rating ~ group * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_12, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_upset_rating
## Chisq Df Pr(>Chisq)
## (Intercept) 768.507 1 < 2.2e-16 ***
## group 30.173 1 3.951e-08 ***
## text 1955.290 1 < 2.2e-16 ***
## purpose 6923.594 1 < 2.2e-16 ***
## group:text 12.579 1 0.0003901 ***
## group:purpose 107.533 1 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_12, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_upset_rating
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 30698.594, BIC = 30765.294
## Pseudo-R² (fixed effects) = 0.546
## Pseudo-R² (total) = 0.580
##
## FIXED EFFECTS:
## --------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- -------- -------- -------- --------- ----------- -------
## (Intercept) -1.263 -1.353 -1.174 -27.722 12.304 0.000
## groupASD 0.186 0.120 0.252 5.493 1024.497 0.000
## text1 0.937 0.895 0.978 44.219 11831.000 0.000
## purpose1 1.762 1.721 1.804 83.208 11831.000 0.000
## groupASD:text1 0.107 0.048 0.166 3.547 11831.000 0.000
## groupASD:purpose1 -0.312 -0.371 -0.253 -10.370 11831.000 0.000
## --------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.211
## scene (Intercept) 0.110
## Residual 0.832
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.060
## scene 8 0.016
## -------------------------------
emm <- emmeans(model_4_12, ~ group | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 0.1861 0.0339 Inf 0.1197 0.2525 5.493 <.0001
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT 0.2929 0.0339 Inf 0.2265 0.3592 8.646 <.0001
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -0.1262 0.0339 Inf -0.1926 -0.0598 -3.725 0.0002
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL z.ratio p.value
## ASD - NT -0.0194 0.0339 Inf -0.0858 0.0470 -0.572 0.5671
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
# Moderation by AQ-10
model_4_13 <- lmer(response_upset_rating ~ aq_score * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_13, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_upset_rating
## Chisq Df Pr(>Chisq)
## (Intercept) 616.9789 1 < 2.2e-16 ***
## aq_score 9.4253 1 0.002140 **
## text 1378.5268 1 < 2.2e-16 ***
## purpose 3232.8956 1 < 2.2e-16 ***
## aq_score:text 9.5835 1 0.001963 **
## aq_score:purpose 0.8762 1 0.349232
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_13, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_upset_rating
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 30823.842, BIC = 30890.542
## Pseudo-R² (fixed effects) = 0.541
## Pseudo-R² (total) = 0.576
##
## FIXED EFFECTS:
## --------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f. p
## ----------------------- -------- -------- -------- --------- ----------- -------
## (Intercept) -1.256 -1.355 -1.157 -24.839 18.592 0.000
## aq_score 0.023 0.008 0.038 3.070 1016.988 0.002
## text1 1.065 1.009 1.121 37.129 11831.000 0.000
## purpose1 1.631 1.574 1.687 56.859 11831.000 0.000
## aq_score:text1 -0.021 -0.034 -0.008 -3.096 11831.000 0.002
## aq_score:purpose1 -0.006 -0.019 0.007 -0.936 11831.000 0.349
## --------------------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.214
## scene (Intercept) 0.110
## Residual 0.836
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.060
## scene 8 0.016
## -------------------------------
4.2 Goal Disclosure on Affective Inference (Study 2)
##### Moderation by Goal (on Affective response)
model_4_21 <- lmer(response_upset_rating ~ purpose_display * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_21, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_upset_rating
## Chisq Df Pr(>Chisq)
## (Intercept) 707.849 1 < 2.2e-16 ***
## purpose_display 0.707 1 0.4004536
## text 2375.672 1 < 2.2e-16 ***
## purpose 5305.046 1 < 2.2e-16 ***
## purpose_display:text 12.073 1 0.0005115 ***
## purpose_display:purpose 11.333 1 0.0007616 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_21, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_upset_rating
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 30803.916, BIC = 30870.617
## Pseudo-R² (fixed effects) = 0.541
## Pseudo-R² (total) = 0.577
##
## FIXED EFFECTS:
## --------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. d.f.
## ------------------------------- -------- -------- -------- --------- -----------
## (Intercept) -1.182 -1.269 -1.095 -26.605 11.092
## purpose_display1 0.022 -0.029 0.073 0.841 11830.416
## text1 1.042 1.000 1.084 48.741 11830.000
## purpose1 1.557 1.515 1.599 72.836 11830.000
## purpose_display1:text1 -0.105 -0.164 -0.046 -3.475 11830.000
## purpose_display1:purpose1 0.102 0.043 0.161 3.366 11830.000
## --------------------------------------------------------------------------------
##
## ---------------------------------------
## p
## ------------------------------- -------
## (Intercept) 0.000
## purpose_display1 0.400
## text1 0.000
## purpose1 0.000
## purpose_display1:text1 0.001
## purpose_display1:purpose1 0.001
## ---------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.215
## scene (Intercept) 0.110
## Residual 0.836
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.061
## scene 8 0.016
## -------------------------------
emm <- emmeans(model_4_21, ~ purpose_display | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL
## purpose_display1 - purpose_display0 0.0220 0.0262 Inf -0.0293 0.0733
## z.ratio p.value
## 0.841 0.4005
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL asymp.UCL
## purpose_display1 - purpose_display0 -0.0830 0.0262 Inf -0.1343 -0.0317
## z.ratio p.value
## -3.170 0.0015
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL
## purpose_display1 - purpose_display0 0.1238 0.0262 Inf 0.0725 0.1751
## z.ratio p.value
## 4.727 <.0001
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL asymp.UCL
## purpose_display1 - purpose_display0 0.0187 0.0262 Inf -0.0326 0.0701
## z.ratio p.value
## 0.716 0.4741
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
##### Group by Goal (on Affective response)
model_4_22 <- lmer(response_upset_rating ~ group * purpose_display * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_22, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response_upset_rating
## Chisq Df Pr(>Chisq)
## (Intercept) 671.1753 1 < 2.2e-16 ***
## group 18.4863 1 1.711e-05 ***
## purpose_display 0.2890 1 0.590888
## text 1067.9300 1 < 2.2e-16 ***
## purpose 3299.0638 1 < 2.2e-16 ***
## group:purpose_display 0.0080 1 0.928629
## group:text 9.2164 1 0.002399 **
## group:purpose 59.2079 1 1.419e-14 ***
## purpose_display:text 3.8369 1 0.050137 .
## purpose_display:purpose 4.2050 1 0.040304 *
## group:purpose_display:text 0.5530 1 0.457097
## group:purpose_display:purpose 0.2532 1 0.614838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_22, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response_upset_rating
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 30713.956, BIC = 30825.124
## Pseudo-R² (fixed effects) = 0.546
## Pseudo-R² (total) = 0.581
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val.
## ---------------------------------------- -------- -------- -------- ---------
## (Intercept) -1.273 -1.369 -1.177 -25.907
## groupASD 0.184 0.100 0.267 4.300
## purpose_display1 0.020 -0.052 0.092 0.538
## text1 0.978 0.919 1.037 32.679
## purpose1 1.719 1.660 1.778 57.437
## groupASD:purpose_display1 0.005 -0.098 0.107 0.090
## groupASD:text1 0.129 0.046 0.213 3.036
## groupASD:purpose1 -0.327 -0.411 -0.244 -7.695
## purpose_display1:text1 -0.083 -0.166 0.000 -1.959
## purpose_display1:purpose1 0.087 0.004 0.170 2.051
## groupASD:purpose_display1:text1 -0.045 -0.163 0.073 -0.744
## groupASD:purpose_display1:purpose1 0.030 -0.088 0.148 0.503
## -----------------------------------------------------------------------------
##
## ------------------------------------------------------------
## d.f. p
## ---------------------------------------- ----------- -------
## (Intercept) 16.584 0.000
## groupASD 2423.288 0.000
## purpose_display1 11825.442 0.591
## text1 11825.000 0.000
## purpose1 11825.000 0.000
## groupASD:purpose_display1 11825.764 0.929
## groupASD:text1 11825.000 0.002
## groupASD:purpose1 11825.000 0.000
## purpose_display1:text1 11825.000 0.050
## purpose_display1:purpose1 11825.000 0.040
## groupASD:purpose_display1:text1 11825.000 0.457
## groupASD:purpose_display1:purpose1 11825.000 0.615
## ------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.211
## scene (Intercept) 0.110
## Residual 0.832
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.060
## scene 8 0.016
## -------------------------------
emm <- emmeans(model_4_22, ~ group * purpose_display | text * purpose)
pairs(emm, reverse = TRUE, infer = TRUE)
## text = 0, purpose = 0:
## contrast estimate SE df asymp.LCL
## ASD purpose_display0 - NT purpose_display0 0.183727 0.0427 Inf 0.0739
## NT purpose_display1 - NT purpose_display0 0.019707 0.0367 Inf -0.0745
## NT purpose_display1 - ASD purpose_display0 -0.164019 0.0427 Inf -0.2738
## ASD purpose_display1 - NT purpose_display0 0.208104 0.0427 Inf 0.0983
## ASD purpose_display1 - ASD purpose_display0 0.024377 0.0371 Inf -0.0708
## ASD purpose_display1 - NT purpose_display1 0.188396 0.0427 Inf 0.0786
## asymp.UCL z.ratio p.value
## 0.29351 4.300 0.0001
## 0.11389 0.538 0.9499
## -0.05425 -3.839 0.0007
## 0.31787 4.870 <.0001
## 0.11957 0.658 0.9128
## 0.29817 4.409 0.0001
##
## text = 1, purpose = 0:
## contrast estimate SE df asymp.LCL
## ASD purpose_display0 - NT purpose_display0 0.312891 0.0427 Inf 0.2031
## NT purpose_display1 - NT purpose_display0 -0.063194 0.0367 Inf -0.1574
## NT purpose_display1 - ASD purpose_display0 -0.376084 0.0427 Inf -0.4859
## ASD purpose_display1 - NT purpose_display0 0.209622 0.0427 Inf 0.0999
## ASD purpose_display1 - ASD purpose_display0 -0.103269 0.0371 Inf -0.1985
## ASD purpose_display1 - NT purpose_display1 0.272816 0.0427 Inf 0.1630
## asymp.UCL z.ratio p.value
## 0.42267 7.322 <.0001
## 0.03099 -1.724 0.3112
## -0.26631 -8.802 <.0001
## 0.31939 4.906 <.0001
## -0.00808 -2.787 0.0273
## 0.38259 6.384 <.0001
##
## text = 0, purpose = 1:
## contrast estimate SE df asymp.LCL
## ASD purpose_display0 - NT purpose_display0 -0.143651 0.0427 Inf -0.2534
## NT purpose_display1 - NT purpose_display0 0.106495 0.0367 Inf 0.0123
## NT purpose_display1 - ASD purpose_display0 0.250146 0.0427 Inf 0.1404
## ASD purpose_display1 - NT purpose_display0 -0.002210 0.0427 Inf -0.1120
## ASD purpose_display1 - ASD purpose_display0 0.141440 0.0371 Inf 0.0462
## ASD purpose_display1 - NT purpose_display1 -0.108705 0.0427 Inf -0.2185
## asymp.UCL z.ratio p.value
## -0.03387 -3.362 0.0043
## 0.20068 2.905 0.0193
## 0.35992 5.854 <.0001
## 0.10756 -0.052 1.0000
## 0.23663 3.817 0.0008
## 0.00107 -2.544 0.0534
##
## text = 1, purpose = 1:
## contrast estimate SE df asymp.LCL
## ASD purpose_display0 - NT purpose_display0 -0.014487 0.0427 Inf -0.1243
## NT purpose_display1 - NT purpose_display0 0.023593 0.0367 Inf -0.0706
## NT purpose_display1 - ASD purpose_display0 0.038081 0.0427 Inf -0.0717
## ASD purpose_display1 - NT purpose_display0 -0.000692 0.0427 Inf -0.1105
## ASD purpose_display1 - ASD purpose_display0 0.013795 0.0371 Inf -0.0814
## ASD purpose_display1 - NT purpose_display1 -0.024286 0.0427 Inf -0.1341
## asymp.UCL z.ratio p.value
## 0.09529 -0.339 0.9866
## 0.11778 0.644 0.9178
## 0.14785 0.891 0.8094
## 0.10908 -0.016 1.0000
## 0.10899 0.372 0.9824
## 0.08549 -0.568 0.9415
##
## Degrees-of-freedom method: asymptotic
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 4 estimates
## P value adjustment: tukey method for comparing a family of 4 estimates
4.3 Goal Disclosure on Rule Violation Judgments (Study 2)
##### Moderation by Goal (on Rule violation)
model_4_31 <- glmer(response ~ purpose_display * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = binomial)
Anova(model_4_31, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 453.4946 1 <2e-16 ***
## purpose_display 0.0445 1 0.8330
## text 1658.9162 1 <2e-16 ***
## purpose 560.9551 1 <2e-16 ***
## purpose_display:text 0.0438 1 0.8343
## purpose_display:purpose 0.1243 1 0.7244
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_31, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 9190.553, BIC = 9249.842
## Pseudo-R² (fixed effects) = 0.626
## Pseudo-R² (total) = 0.665
##
## FIXED EFFECTS:
## -------------------------------------------------------------------------------
## exp(Est.) 2.5% 97.5% z val. p
## ------------------------------- ----------- -------- -------- --------- -------
## (Intercept) 0.045 0.034 0.060 -21.295 0.000
## purpose_display1 1.029 0.786 1.347 0.211 0.833
## text1 77.134 62.579 95.075 40.730 0.000
## purpose1 11.519 9.410 14.102 23.684 0.000
## purpose_display1:text1 0.970 0.730 1.289 -0.209 0.834
## purpose_display1:purpose1 0.951 0.717 1.260 -0.353 0.724
## -------------------------------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.549
## scene (Intercept) 0.291
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.082
## scene 8 0.023
## -------------------------------
emmeans(model_4_31, pairwise ~ purpose_display | text * purpose)
## $emmeans
## text = 0, purpose = 0:
## purpose_display emmean SE df asymp.LCL asymp.UCL
## 0 -3.101 0.146 Inf -3.387 -2.816
## 1 -3.072 0.145 Inf -3.356 -2.788
##
## text = 1, purpose = 0:
## purpose_display emmean SE df asymp.LCL asymp.UCL
## 0 1.244 0.122 Inf 1.005 1.484
## 1 1.243 0.122 Inf 1.004 1.482
##
## text = 0, purpose = 1:
## purpose_display emmean SE df asymp.LCL asymp.UCL
## 0 -0.657 0.119 Inf -0.892 -0.423
## 1 -0.679 0.120 Inf -0.913 -0.445
##
## text = 1, purpose = 1:
## purpose_display emmean SE df asymp.LCL asymp.UCL
## 0 3.688 0.151 Inf 3.392 3.985
## 1 3.636 0.150 Inf 3.342 3.930
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## text = 0, purpose = 0:
## contrast estimate SE df z.ratio p.value
## purpose_display0 - purpose_display1 -0.02897 0.1370 Inf -0.211 0.8330
##
## text = 1, purpose = 0:
## contrast estimate SE df z.ratio p.value
## purpose_display0 - purpose_display1 0.00138 0.0834 Inf 0.017 0.9868
##
## text = 0, purpose = 1:
## contrast estimate SE df z.ratio p.value
## purpose_display0 - purpose_display1 0.02180 0.0762 Inf 0.286 0.7747
##
## text = 1, purpose = 1:
## contrast estimate SE df z.ratio p.value
## purpose_display0 - purpose_display1 0.05215 0.1480 Inf 0.353 0.7241
##
## Results are given on the log odds ratio (not the response) scale.
##### Group by Goal (on Rule violation)
model_4_32 <- glmer(response ~ group * purpose_display * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2, family = binomial)
Anova(model_4_32, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: response
## Chisq Df Pr(>Chisq)
## (Intercept) 327.4004 1 < 2.2e-16 ***
## group 15.6586 1 7.587e-05 ***
## purpose_display 1.2105 1 0.2712296
## text 793.7224 1 < 2.2e-16 ***
## purpose 257.8257 1 < 2.2e-16 ***
## group:purpose_display 2.2162 1 0.1365649
## group:text 12.3882 1 0.0004321 ***
## group:purpose 3.2680 1 0.0706430 .
## purpose_display:text 1.2230 1 0.2687822
## purpose_display:purpose 0.8310 1 0.3619820
## group:purpose_display:text 2.2371 1 0.1347319
## group:purpose_display:purpose 1.7531 1 0.1854935
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_32, digits = 3, confint = TRUE, exp = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: response
## Type: Mixed effects generalized linear regression
## Error Distribution: binomial
## Link function: logit
##
## MODEL FIT:
## AIC = 9140.553, BIC = 9244.309
## Pseudo-R² (fixed effects) = 0.641
## Pseudo-R² (total) = 0.676
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------
## exp(Est.) 2.5% 97.5%
## ---------------------------------------- ----------- -------- ---------
## (Intercept) 0.028 0.019 0.042
## groupASD 2.302 1.523 3.478
## purpose_display1 0.765 0.475 1.233
## text1 116.012 83.345 161.484
## purpose1 14.483 10.451 20.070
## groupASD:purpose_display1 1.554 0.870 2.775
## groupASD:text1 0.470 0.308 0.715
## groupASD:purpose1 0.681 0.449 1.033
## purpose_display1:text1 1.316 0.809 2.143
## purpose_display1:purpose1 1.254 0.771 2.039
## groupASD:purpose_display1:text1 0.630 0.343 1.154
## groupASD:purpose_display1:purpose1 0.666 0.364 1.216
## -----------------------------------------------------------------------
##
## ----------------------------------------------------------
## z val. p
## ---------------------------------------- --------- -------
## (Intercept) -18.094 0.000
## groupASD 3.957 0.000
## purpose_display1 -1.100 0.271
## text1 28.173 0.000
## purpose1 16.057 0.000
## groupASD:purpose_display1 1.489 0.137
## groupASD:text1 -3.520 0.000
## groupASD:purpose1 -1.808 0.071
## purpose_display1:text1 1.106 0.269
## purpose_display1:purpose1 0.912 0.362
## groupASD:purpose_display1:text1 -1.496 0.135
## groupASD:purpose_display1:purpose1 -1.324 0.185
## ----------------------------------------------------------
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 0.519
## scene (Intercept) 0.292
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.074
## scene 8 0.023
## -------------------------------
emmeans(model_4_32, pairwise ~ group * purpose_display | text * purpose)
## $emmeans
## text = 0, purpose = 0:
## group purpose_display emmean SE df asymp.LCL asymp.UCL
## NT 0 -3.560 0.197 Inf -3.945 -3.174
## ASD 0 -2.726 0.166 Inf -3.051 -2.402
## NT 1 -3.827 0.213 Inf -4.244 -3.411
## ASD 1 -2.553 0.160 Inf -2.867 -2.240
##
## text = 1, purpose = 0:
## group purpose_display emmean SE df asymp.LCL asymp.UCL
## NT 0 1.194 0.138 Inf 0.924 1.464
## ASD 0 1.272 0.139 Inf 1.000 1.543
## NT 1 1.201 0.138 Inf 0.930 1.472
## ASD 1 1.257 0.138 Inf 0.987 1.527
##
## text = 0, purpose = 1:
## group purpose_display emmean SE df asymp.LCL asymp.UCL
## NT 0 -0.887 0.135 Inf -1.152 -0.622
## ASD 0 -0.437 0.133 Inf -0.697 -0.177
## NT 1 -0.928 0.136 Inf -1.194 -0.662
## ASD 1 -0.445 0.132 Inf -0.705 -0.185
##
## text = 1, purpose = 1:
## group purpose_display emmean SE df asymp.LCL asymp.UCL
## NT 0 3.867 0.201 Inf 3.472 4.261
## ASD 0 3.561 0.178 Inf 3.212 3.910
## NT 1 4.100 0.217 Inf 3.676 4.525
## ASD 1 3.365 0.171 Inf 3.029 3.701
##
## Results are given on the logit (not the response) scale.
## Confidence level used: 0.95
##
## $contrasts
## text = 0, purpose = 0:
## contrast estimate SE df z.ratio p.value
## NT purpose_display0 - ASD purpose_display0 -0.83360 0.211 Inf -3.957 0.0004
## NT purpose_display0 - NT purpose_display1 0.26773 0.243 Inf 1.100 0.6894
## NT purpose_display0 - ASD purpose_display1 -1.00641 0.207 Inf -4.873 <.0001
## ASD purpose_display0 - NT purpose_display1 1.10133 0.226 Inf 4.883 <.0001
## ASD purpose_display0 - ASD purpose_display1 -0.17281 0.168 Inf -1.027 0.7338
## NT purpose_display1 - ASD purpose_display1 -1.27414 0.222 Inf -5.746 <.0001
##
## text = 1, purpose = 0:
## contrast estimate SE df z.ratio p.value
## NT purpose_display0 - ASD purpose_display0 -0.07770 0.130 Inf -0.599 0.9324
## NT purpose_display0 - NT purpose_display1 -0.00723 0.118 Inf -0.061 0.9999
## NT purpose_display0 - ASD purpose_display1 -0.06267 0.129 Inf -0.487 0.9620
## ASD purpose_display0 - NT purpose_display1 0.07046 0.130 Inf 0.542 0.9488
## ASD purpose_display0 - ASD purpose_display1 0.01502 0.118 Inf 0.128 0.9993
## NT purpose_display1 - ASD purpose_display1 -0.05544 0.129 Inf -0.429 0.9735
##
## text = 0, purpose = 1:
## contrast estimate SE df z.ratio p.value
## NT purpose_display0 - ASD purpose_display0 -0.44973 0.120 Inf -3.733 0.0011
## NT purpose_display0 - NT purpose_display1 0.04151 0.112 Inf 0.371 0.9825
## NT purpose_display0 - ASD purpose_display1 -0.44176 0.120 Inf -3.670 0.0014
## ASD purpose_display0 - NT purpose_display1 0.49124 0.121 Inf 4.053 0.0003
## ASD purpose_display0 - ASD purpose_display1 0.00797 0.105 Inf 0.076 0.9998
## NT purpose_display1 - ASD purpose_display1 -0.48327 0.121 Inf -3.991 0.0004
##
## text = 1, purpose = 1:
## contrast estimate SE df z.ratio p.value
## NT purpose_display0 - ASD purpose_display0 0.30617 0.224 Inf 1.364 0.5218
## NT purpose_display0 - NT purpose_display1 -0.23346 0.250 Inf -0.933 0.7872
## NT purpose_display0 - ASD purpose_display1 0.50198 0.219 Inf 2.292 0.0998
## ASD purpose_display0 - NT purpose_display1 -0.53963 0.238 Inf -2.266 0.1061
## ASD purpose_display0 - ASD purpose_display1 0.19580 0.189 Inf 1.034 0.7297
## NT purpose_display1 - ASD purpose_display1 0.73543 0.233 Inf 3.154 0.0088
##
## Results are given on the log odds ratio (not the response) scale.
## P value adjustment: tukey method for comparing a family of 4 estimates
4.4 Goal Disclosure on Adjusted Confidence (Study 2)
# Main effect
model_4_41 <- lmer(cw_resp ~ purpose_display * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_41, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 887.8464 1 <2e-16 ***
## purpose_display 0.0106 1 0.9179
## text 6748.5687 1 <2e-16 ***
## purpose 1233.9422 1 <2e-16 ***
## purpose_display:text 0.0539 1 0.8164
## purpose_display:purpose 0.0613 1 0.8044
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_41, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 134914.132, BIC = 134980.833
## Pseudo-R² (fixed effects) = 0.556
## Pseudo-R² (total) = 0.574
##
## FIXED EFFECTS:
## -----------------------------------------------------------------------
## Est. 2.5% 97.5% t val.
## ------------------------------- --------- --------- --------- ---------
## (Intercept) -83.159 -88.629 -77.689 -29.797
## purpose_display1 0.193 -3.470 3.855 0.103
## text1 125.319 122.329 128.309 82.150
## purpose1 53.587 50.597 56.577 35.128
## purpose_display1:text1 -0.501 -4.729 3.727 -0.232
## purpose_display1:purpose1 0.534 -3.694 4.763 0.248
## -----------------------------------------------------------------------
##
## ---------------------------------------------------
## d.f. p
## ------------------------------- ----------- -------
## (Intercept) 11.579 0.000
## purpose_display1 11830.545 0.918
## text1 11829.999 0.000
## purpose1 11829.999 0.000
## purpose_display1:text1 11829.999 0.816
## purpose_display1:purpose1 11829.999 0.804
## ---------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 10.322
## scene (Intercept) 6.791
## Residual 59.631
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.029
## scene 8 0.012
## -------------------------------
# Moderation by group
model_4_42 <- lmer(cw_resp ~ group * purpose_display * (text + purpose) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_42, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 813.4666 1 < 2.2e-16 ***
## group 17.0105 1 3.717e-05 ***
## purpose_display 0.2655 1 0.6064
## text 3802.7547 1 < 2.2e-16 ***
## purpose 602.3467 1 < 2.2e-16 ***
## group:purpose_display 0.7002 1 0.4027
## group:text 20.2563 1 6.773e-06 ***
## group:purpose 0.4510 1 0.5019
## purpose_display:text 0.0559 1 0.8131
## purpose_display:purpose 0.5561 1 0.4559
## group:purpose_display:text 0.3263 1 0.5678
## group:purpose_display:purpose 0.6551 1 0.4183
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_42, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 134839.229, BIC = 134950.396
## Pseudo-R² (fixed effects) = 0.558
## Pseudo-R² (total) = 0.576
##
## FIXED EFFECTS:
## --------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val.
## ---------------------------------------- --------- --------- --------- ---------
## (Intercept) -88.928 -95.039 -82.817 -28.521
## groupASD 11.661 6.119 17.202 4.124
## purpose_display1 -1.352 -6.496 3.791 -0.515
## text1 132.101 127.902 136.300 61.666
## purpose1 52.575 48.377 56.774 24.543
## groupASD:purpose_display1 3.122 -4.191 10.436 0.837
## groupASD:text1 -13.707 -19.676 -7.738 -4.501
## groupASD:purpose1 2.045 -3.924 8.014 0.672
## purpose_display1:text1 0.716 -5.221 6.654 0.236
## purpose_display1:purpose1 2.259 -3.679 8.197 0.746
## groupASD:purpose_display1:text1 -2.460 -10.902 5.981 -0.571
## groupASD:purpose_display1:purpose1 -3.486 -11.927 4.956 -0.809
## --------------------------------------------------------------------------------
##
## ------------------------------------------------------------
## d.f. p
## ---------------------------------------- ----------- -------
## (Intercept) 18.016 0.000
## groupASD 4120.024 0.000
## purpose_display1 11825.582 0.606
## text1 11824.999 0.000
## purpose1 11824.999 0.000
## groupASD:purpose_display1 11826.004 0.403
## groupASD:text1 11824.999 0.000
## groupASD:purpose1 11824.999 0.502
## purpose_display1:text1 11824.999 0.813
## purpose_display1:purpose1 11824.999 0.456
## groupASD:purpose_display1:text1 11824.999 0.568
## groupASD:purpose_display1:purpose1 11824.999 0.418
## ------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 9.939
## scene (Intercept) 6.793
## Residual 59.520
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.027
## scene 8 0.013
## -------------------------------
# Moderation by AQ-10
model_4_43 <- lmer(cw_resp ~ aq_score * (purpose_display * case) + (1 | scene) + (1 | subject_nr),
data = clean_data_2)
Anova(model_4_43, type = 3) #
## Analysis of Deviance Table (Type III Wald chisquare tests)
##
## Response: cw_resp
## Chisq Df Pr(>Chisq)
## (Intercept) 566.9780 1 <2e-16 ***
## aq_score 2.5986 1 0.1070
## purpose_display 0.6315 1 0.4268
## case 2385.5291 3 <2e-16 ***
## purpose_display:case 1.4380 3 0.6966
## aq_score:purpose_display 0.7774 1 0.3779
## aq_score:case 4.7907 3 0.1878
## aq_score:purpose_display:case 2.6242 3 0.4533
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summ(model_4_43, digits = 3, confint = TRUE)
## MODEL INFO:
## Observations: 12224
## Dependent Variable: cw_resp
## Type: Mixed effects linear regression
##
## MODEL FIT:
## AIC = 134808.162, BIC = 134948.974
## Pseudo-R² (fixed effects) = 0.559
## Pseudo-R² (total) = 0.578
##
## FIXED EFFECTS:
## ------------------------------------------------------------------------
## Est. 2.5% 97.5%
## ----------------------------------------- --------- ---------- ---------
## (Intercept) -92.508 -100.122 -84.893
## aq_score 1.154 -0.249 2.557
## purpose_display1 3.239 -4.749 11.226
## caseviol 185.392 177.408 193.377
## caseover 135.996 128.012 143.981
## caseunder 64.549 56.564 72.533
## purpose_display1:caseviol -0.756 -12.048 10.535
## purpose_display1:caseover 3.733 -7.559 15.025
## purpose_display1:caseunder -3.060 -14.351 8.232
## aq_score:purpose_display1 -0.843 -2.716 1.031
## aq_score:caseviol -1.790 -3.663 0.082
## aq_score:caseover -0.094 -1.966 1.779
## aq_score:caseunder -0.172 -2.045 1.700
## aq_score:purpose_display1:caseviol 0.218 -2.430 2.866
## aq_score:purpose_display1:caseover -1.165 -3.813 1.484
## aq_score:purpose_display1:caseunder 0.996 -1.652 3.644
## ------------------------------------------------------------------------
##
## -----------------------------------------------------------------------
## t val. d.f. p
## ----------------------------------------- --------- ----------- -------
## (Intercept) -23.811 43.199 0.000
## aq_score 1.612 5583.374 0.107
## purpose_display1 0.795 11821.752 0.427
## caseviol 45.508 11821.000 0.000
## caseover 33.383 11821.000 0.000
## caseunder 15.845 11821.000 0.000
## purpose_display1:caseviol -0.131 11821.000 0.896
## purpose_display1:caseover 0.648 11821.000 0.517
## purpose_display1:caseunder -0.531 11821.000 0.595
## aq_score:purpose_display1 -0.882 11821.881 0.378
## aq_score:caseviol -1.874 11821.000 0.061
## aq_score:caseover -0.098 11821.000 0.922
## aq_score:caseunder -0.180 11821.000 0.857
## aq_score:purpose_display1:caseviol 0.161 11821.000 0.872
## aq_score:purpose_display1:caseover -0.862 11821.000 0.389
## aq_score:purpose_display1:caseunder 0.737 11821.000 0.461
## -----------------------------------------------------------------------
##
## p values calculated using Satterthwaite d.f.
##
## RANDOM EFFECTS:
## --------------------------------------
## Group Parameter Std. Dev.
## ------------ ------------- -----------
## subject_nr (Intercept) 10.382
## scene (Intercept) 6.799
## Residual 59.382
## --------------------------------------
##
## Grouping variables:
## -------------------------------
## Group # groups ICC
## ------------ ---------- -------
## subject_nr 382 0.029
## scene 8 0.013
## -------------------------------
5. Do Autistic Adults Explicitly Endorse a Different Interpretive
Policy? (Study 1 and 2)
##### Effect of group on on "Interpretive policy: choice" (Study 1, both parts, and Study 2)
##### and "Interpretive policy: bipolar rating"
clean_1_expl <- clean_data_1a %>%
dplyr::group_by(subject_nr) %>%
dplyr::slice_sample(n = 1) %>%
dplyr::ungroup()
clean_1_expl <- clean_1_expl[, c("subject_nr", "group", "aq_score", "theory", "theory_bipolar")]
##### Choice (Study 1): theory
str(clean_1_expl$theory)
## chr [1:395] "Spirit" "Spirit" "Letter" "Spirit" "Spirit" "Spirit" "Letter" ...
clean_1_expl$theory <- as.factor(clean_1_expl$theory)
str(clean_1_expl$theory)
## Factor w/ 2 levels "Letter","Spirit": 2 2 1 2 2 2 1 2 1 1 ...
model_expl1 <- glm(theory ~ group,
clean_1_expl, family = binomial)
Anova(model_expl1, type = 3) # No effect
## Analysis of Deviance Table (Type III tests)
##
## Response: theory
## LR Chisq Df Pr(>Chisq)
## group 1.6141 1 0.2039
clean_1_expl %>%
count(group, theory) %>%
group_by(group) %>%
mutate(percent = n / sum(n) * 100) %>%
ungroup() # Descriptively, there are differences
## # A tibble: 4 × 4
## group theory n percent
## <fct> <fct> <int> <dbl>
## 1 NT Letter 102 51
## 2 NT Spirit 98 49
## 3 ASD Letter 87 44.6
## 4 ASD Spirit 108 55.4
# Let`s use contingency tables then
table(clean_1_expl$group, clean_1_expl$theory)
##
## Letter Spirit
## NT 102 98
## ASD 87 108
chisq.test(table(clean_1_expl$group, clean_1_expl$theory))
##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(clean_1_expl$group, clean_1_expl$theory)
## X-squared = 1.3672, df = 1, p-value = 0.2423
##### Choice (Study 2): ???
##### Rating (Study 1): theory_bipolar
str(clean_1_expl$theory_bipolar)
## int [1:395] 5 4 2 5 4 5 3 5 1 2 ...
clean_1_expl$theory_bipolar <- as.numeric(clean_1_expl$theory_bipolar)
str(clean_1_expl$theory_bipolar)
## num [1:395] 5 4 2 5 4 5 3 5 1 2 ...
range(clean_data_1a$theory_bipolar)
## [1] 0 6
model_expl2 <- lm(theory_bipolar ~ group,
clean_1_expl)
Anova(model_expl2, type = 3) # No effect
## Anova Table (Type III tests)
##
## Response: theory_bipolar
## Sum Sq Df F value Pr(>F)
## (Intercept) 1556.82 1 608.0755 <2e-16 ***
## group 4.14 1 1.6186 0.204
## Residuals 1006.17 393
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
clean_1_expl %>%
group_by(group) %>%
summarise(
mean_theory_bipolar = mean(theory_bipolar, na.rm = TRUE),
sd_theory_bipolar = sd(theory_bipolar, na.rm = TRUE),
n = n()
)
## # A tibble: 2 × 4
## group mean_theory_bipolar sd_theory_bipolar n
## <fct> <dbl> <dbl> <int>
## 1 NT 2.79 1.58 200
## 2 ASD 2.99 1.62 195
##### Rating (Study 2): ???