library(here) # for engaging with working environment
library(rio) # for importing excel files
library(psych) # for scoring multiple choice items
library(R.utils) # just for the insert() function
library(conflicted)
library(tidyverse) # for data cleaning and manipulation
conflict_prefer("here", "here")
conflict_prefer("filter", "dplyr")
conflict_prefer("count", "dplyr")
conflict_prefer("mutate", "dplyr")
conflict_prefer("summarise", "dplyr")The excel files contain raw data exported from Qualtrics. The first row of this file contains variable names, but rows 2 and 3 contain meta data. To account, we first load in all data as a “names” data frame and then data starting at row 4, for the raw data.
# read in form A (Prolific)
formA_names = read_csv(
here("data/Form+A_Prolific_April+2,+2021_15.27.csv"))
formA = read_csv(
here("data/Form+A_Prolific_April+2,+2021_15.27.csv"),
col_names = F, skip = 3)
names(formA) = names(formA_names)
rm(formA_names)
# read in form A (MTurk)
formA_names = read_csv(
here("data/Form A_April 2, 2021_15.41.numeric.csv"))
formA2 = read_csv(
here("data/Form A_April 2, 2021_15.41.numeric.csv"),
col_names = F, skip = 3)
names(formA2) = names(formA_names)
rm(formA_names)
formA = full_join(formA, formA2)
rm(formA2)
# read in form B (Prolific)
formB_names = read_csv(
here("data/Form+B_Prolific_April+2,+2021_15.27.csv"))
formB = read_csv(
here("data/Form+B_Prolific_April+2,+2021_15.27.csv"),
col_names = F, skip = 3)
names(formB) = names(formB_names)
rm(formB_names)
# read in form B (MTurk)
formB_names = read_csv(
here("data/Form B_April 2, 2021_15.44.numeric.csv"))
formB2 = read_csv(
here("data/Form B_April 2, 2021_15.44.numeric.csv"),
col_names = F, skip = 3)
names(formB2) = names(formB_names)
rm(formB_names)
formB = full_join(formB, formB2)
rm(formB2)
# read in form C (Prolific)
formC_names = read_csv(
here("data/Form+C_April+16,+2021_17.21.csv"))
formC = read_csv(
here("data/Form+C_April+16,+2021_17.21.csv"),
col_names = F, skip = 3)
names(formC) = names(formC_names)
rm(formC_names)In forms A and B, variable names composed solely of numbers (e.g., 20001) refer to the specific variable number as included in the dictionary. These must be prefaced by q_ for readability.
names(formA) = str_replace(names(formA), # look in this vector of strings
"(^)([0-9]*$)", #match two parts. part 1: beginning of string, second part: digits 0 or more times and then the string ends
"q_\\2") # replace with q_ followed by the second part of the match
names(formB) = str_replace(names(formB),
"(^)([0-9]*$)",
"q_\\2")
names(formC) = str_replace(names(formC),
"^Q",
"q_")
# an additional error in form C -- some variable names were coded with only the last 4 digits (i.e., missing the first "2")
names(formC) = str_replace(names(formC),
"(q_)(\\d{4}$)", "\\12\\2")Remove participants without valid Prolific or MTurk IDs.
formA = formA %>%
dplyr::filter(!is.na(mTurkCode) | str_length(PROLIFIC_PID) > 3)
formB = formB %>%
dplyr::filter(!is.na(mTurkCode) | str_length(PROLIFIC_PID) > 3)
formC = formC %>%
dplyr::filter(str_length(PROLIFIC_PID) > 3)keyA = read.csv(here("keys/key_A.csv"))
keyB = read.csv(here("keys/key_B.csv"))
keyC = import(here("keys/key_C.xlsx"), sheet = 1)Variable names composed solely of numbers (e.g., 1) refer to the response choice number as presented to participants. These must be prefaced by R_ for readability.
names(keyA) = str_replace(names(keyA),
"(^X)([0-9]{1}$)",
"R_\\2")
names(keyB) = str_replace(names(keyB),
"(^X)([0-9]{1}$)",
"R_\\2")
names(keyC) = str_replace(names(keyC),
"(^)([0-9]{1}$)",
"R_\\2")
keyC = dplyr::rename(keyC, X = .)The Key column indicates the position of the correct response. We create a key vector that contains the value of the correct response, not the position.
create_key = function(keydf){
# select only response variables and concatonate by row into vectors
keydf$response_vector = keydf %>%
select(starts_with("R")) %>%
pmap(~c(..1, ..2, ..3, ..4, ..5, ..6))
# map across response vectors and key positions to pull out correct answer
keydf = keydf %>%
mutate(answer = map2_chr(response_vector, Key,
~.x[.y]))
return(keydf)
}
keyA = create_key(keyA) %>%
mutate(form = "A")
keyB = create_key(keyB) %>%
mutate(form = "B")
keyC = create_key(keyC) %>%
group_by(Stimulus) %>%
dplyr::mutate(form = ifelse(dplyr::row_number() == 1, "C", "D")) %>%
ungroup()Merge keys together.
masterKey = keyA %>%
full_join(keyB) %>%
full_join(keyC) %>%
select(X:Key, answer, form) %>%
dplyr::rename(item = X)First, we merge all raw datasets.
all_data = formA %>%
full_join(formB) %>%
full_join(formC)alldata_scored = score.multiple.choice(
key = masterKey$Key,
data = all_data[, masterKey$item],
score = FALSE)Remove items which were mistakes (see key_C, sheet entitled linking C to A and B.)
remove_items = c(
"q_21148",
"q_22573",
"q_22496",
"q_20052",
"q_21409",
"q_22068",
"q_22169",
"q_21911",
"q_21185",
"q_21288",
"q_20790",
"q_20033",
"q_24285",
"q_23402",
"q_24824",
"q_23044",
"q_25399",
"q_23847",
"q_23990",
"q_23485",
"q_24709",
"q_23602",
"q_24676",
"q_25281",
"q_23456",
"q_20479",
"q_23832")
remove_which = which(masterKey$item %in% remove_items)
alldata_scored = alldata_scored[, -remove_which]
masterKey = dplyr::filter(masterKey, !(item %in% remove_items))
all_data = select(all_data, -all_of(remove_items))demo = all_data %>%
mutate(comb_id = case_when(
!is.na(prolificID) ~ prolificID,
!is.na(mTurkCode) ~ as.character(mTurkCode),
TRUE ~ NA_character_)) %>%
mutate(source = case_when(
!is.na(prolificID) ~ "Prolific",
!is.na(mTurkCode) ~ "MTurk",
TRUE ~ NA_character_)) %>%
dplyr::mutate(row = row_number() + 1000) %>%
group_by(comb_id) %>%
dplyr::mutate(row = min(row)) %>%
dplyr::mutate(PID = paste0("R", row)) %>%
ungroup() %>%
dplyr::rename(duration = `Duration (in seconds)`) %>%
dplyr::mutate(
english_cat = case_when(
english == 1 ~ "Very well (fluent/native)",
english == 2 ~ "Well",
english == 3 ~ "Not well",
english == 4 ~ "Not at all (Need translation)",
TRUE ~ NA_character_),
growup_cat = case_when(
growup == 1 ~ "United States of America",
growup == 2 ~ "Outside of the US"),
live_cat = case_when(
live == 1 ~ "Yes",
live == 2 ~ "No"),
state = recode(states,
!!!c(state.name[1:50], "District of Columbia")
),
sex = case_when(
sex == 1 ~ "Male",
sex == 2 ~ "Female",
sex == 3 ~ "Other",
sex == 4 ~ "Prefer not to say"),
edu = case_when(
edu == 0 ~ "Less than 12 years",
edu == 1 ~ "High school grad/GED",
edu == 2 ~ "Currently in college/university",
edu == 3 ~ "Some college/university, but did not graduate",
edu == 4 ~ "Associate degree (2 yr)",
edu == 5 ~ "College/university degree (4 yr)",
edu == 6 ~ "Currently in grad/professional school",
edu == 7 ~ "Grad/professional degree"),
ethnic = case_when(
ethnic == 1 ~ "American Indian/Alaskan Native",
ethnic == 2 ~ "Asian",
ethnic == 3 ~ "Black",
ethnic == 4 ~ "Hispanic",
ethnic == 5 ~ "White",
ethnic == 6 ~ "Mixed (two or more)",
ethnic == 7 ~ "Other"),
hhinc = case_when(
hhinc == 1 ~ "< 20K",
hhinc == 2 ~ "20K-40K",
hhinc == 3 ~ "40K-60K",
hhinc == 4 ~ "60K-80K",
hhinc == 5 ~ "80K-100K",
hhinc == 6 ~ "100K-120K",
hhinc == 7 ~ "120K-150K",
hhinc == 8 ~ "150K-200K",
hhinc == 9 ~ "200K-250K",
hhinc == 10 ~ "250K-350K",
hhinc == 11 ~ "350K-500K",
hhinc == 12 ~ "> 500K",
hhinc == 13 ~ "Prefer not to say"))Participants are excluded from analyses if they * complete the survey in less than 3 minutes (180 seconds), * don’t speak fluent English, * grew up outside the US, or * are currently living outside the US.
demo = demo %>%
mutate(included = case_when(
duration < 180 ~ "No",
english > 2 ~ "No",
growup == 2 ~ "No",
live == 2 ~ "No",
TRUE ~ "Yes"
))163 rows are excluded on this basis.
demo = demo %>%
select(PID, source, StartDate, duration, english_cat, growup_cat, live_cat, state, age, sex, edu, ethnic, hhinc, included)
names(demo) = str_remove(names(demo), "_cat")
demo$prop_correct <- alldata_scored %>% rowMeans(na.rm = T)alldata_scored_df = as.data.frame(alldata_scored)
data_scored = cbind(demo, alldata_scored)
write_csv(data_scored,
file = here("data/TDA_data_scored.csv"))
# This output file has been posted on Dataverse at
# https://doi.org/10.7910/DVN/5T80PFWe also save a shortened version of the master key object.
masterKey %>%
select(item, answer, form) %>%
dplyr::rename(adjective = answer) %>%
write_csv(file = here("data/masterkey.csv"))
# This output file has been posted on Dataverse at
# https://doi.org/10.7910/DVN/5T80PF