% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-children_gender_stereo.R
\docType{data}
\name{children_gender_stereo}
\alias{children_gender_stereo}
\title{Gender Stereotypes in 5-7 year old Children}
\format{
This data object is more unusual than most. It is a list of 4 data
frames. The four data frames correspond to the data used in Studies 1-4 of
the referenced paper, and these data frames each have variables (columns)
that are among the following:
\describe{
\item{subject}{Subject ID. Note that Subject 1 in the first data frame
(dataset) does \bold{not} correspond to Subject 1 in the second data frame.}
\item{gender}{Gender of the subject.}
\item{age}{Age of the subject, in years.}
\item{trait}{The trait that the children were making a judgement about,
which was either \code{nice} or \code{smart}.}
\item{target}{The age group of the people the children were making judgements
about (as being either nice or smart): \code{children} or \code{adults}.}
\item{stereotype}{The proportion of trials where the child picked a gender
target that matched the trait that was the same as the gender of the child.
For example, suppose we had 18 pictures, where each picture showed 2 men and
2 women (and a different set of people in each photo). Then if we asked a
boy to pick the person in each picture who they believed to be really smart,
this \code{stereotype} variable would report the fraction of pictures where
the boy picked a man. When a girl reviews the photos, then this
\code{stereotype} variable reports the fraction of photos where she picked
a woman. That is, this variable differs in meaning depending on the gender
of the child. (This variable design is a little confusing, but it is useful
when analyzing the data.)}
\item{high_achieve_caution}{The proportion of trials where the child said
that children of their own gender were high-achieving in school.}
\item{interest}{Average score that measured the interest of the child in
the game.}
\item{difference}{A difference score between the interest of the child in
the \dQuote{smart} game and their interest in the \dQuote{try-hard} game.}
}
}
\source{
Bian L, Leslie SJ, Cimpian A. 2017. "Gender stereotypes about intellectual
ability emerge early and influence children's interests". Science 355:6323
(389-391). https://www.science.org/doi/10.1126/science.aah6524.

The original data may be found \href{https://osf.io/yund6/?view_only=9a8505d4e87b456a89f255b43e21234e}{here}.
}
\usage{
children_gender_stereo
}
\description{
Stereotypes are common, but at what age do they start? This study
investigates stereotypes in young children aged 5-7 years old. There are
four studies reported in the paper, and all four datasets are provided here.
}
\details{
The structure of the data object is a little unusual, so we recommend
reviewing the Examples section before starting your analysis.

Thank you to Nicholas Horton for pointing us to this study and the data!

Most of the results in the paper can be reproduced using the data provided
here.

\% TODO(David) - Add short descriptions of each study.
}
\examples{

# This dataset is a little funny to work with.
# If wanting to review the data for a study, we
# recommend first assigning the corresponding
# data frame to a new variable. For instance,
# below we assign the second study's data to an
# object called `d` (d is for data!).
d <- children_gender_stereo[[2]]
}
\keyword{datasets}
