Contact
Participants
1397
post-template-default,single,single-post,postid-1397,single-format-standard,bridge-core-2.4.3,ajax_fade,page_not_loaded,,qode_grid_1300,side_area_uncovered_from_content,overlapping_content,qode-content-sidebar-responsive,qode-theme-ver-22.8,qode-theme-bridge,disabled_footer_top,wpb-js-composer js-comp-ver-6.3.0,vc_responsive,elementor-default,elementor-kit-3194

Participants

Participants

Particpants

Of the 126 people who participated,
six were excluded due to inconsistent
responses, leaving a final sample of N = 120
participants with 62 women (mean age 33 years,
range 18 to 65, SD 11 years). We recruited via
Amazon Mechanical Turk. Compensation was
$2 for an average duration of 22 min, range 7 to
56 min

# Load the demographics data
# Change this path to where your data lies
setwd("../4-Data") # Change to your data directory
d  <- fread("demographics.csv", sep=";", select=c("i","staDat","endDat","age","fem"))

# Rejected participants
rejected <- fread("../4-Data/raw_data/rejected_workerIDs.csv", header = F)
nrow(rejected) # n = 6
d[, summary(age)] # Age
d[, table(fem)] # Gender
d[, range(as.Date(as.POSIXlt(staDat, format = c("%d.%m.%Y %H:%M"))))] # Timeframe
d[, summary(as.numeric(as.POSIXlt(endDat, format = c("%d.%m.%Y %H:%M")) - as.POSIXlt(staDat, format = c("%d.%m.%Y %H:%M"))))] # Duration