R/efa-data.R

#' Exploratory Factor Analysis Practice Dataset
#'
#' Study: This dataset has data on the Openness to
#' Experience scale collected as part of an undergraduate
#' honor's thesis project.
#'
#' The instructions were:
#' Below are some phrases describing people's behaviors.
#' Please use the rating scale below to describe how
#' accurately each statement describes you. Describe
#' yourself as you generally are now, not as you wish to
#' be in the future. Describe yourself as you honestly
#' see yourself in relation to other people of your
#' gender and of roughly your same age.  Please read
#' each statement carefully, and then check the box
#' that corresponds to your response.
#'
#' Scale: very inaccurate, moderately inaccurate,
#' neither inaccurate nor accurate, moderately
#' accurate, very accurate
#'
#' @docType data
#'
#' @usage data(efa)
#'
#' @format A data frame with 99 rows and 21 variables.
#'
#'\describe{
#'   \item{o1}{Believe in the importance of art.}
#'   \item{o2}{Have a vivid imagination.}
#'   \item{o3}{Tend to vote for liberal political candidates.}
#'   \item{o4}{Carry the conversation to a higher level.}
#'   \item{o5}{Enjoy hearing new ideas.}
#'   \item{o6}{Enjoy thinking about things.}
#'   \item{o7}{Can say things beautifully.}
#'   \item{o8}{Enjoy wild flights of fantasy.}
#'   \item{o9}{Get excited by new ideas.}
#'   \item{o10}{Have a rich vocabulary.}
#'   \item{o11}{Am not interested in abstract ideas.}
#'   \item{o12}{Do not like art.}
#'   \item{o13}{Avoid philosophical discussions.}
#'   \item{o14}{Do not enjoy going to art museums.}
#'   \item{o15}{Tend to vote for conservative political candidates.}
#'   \item{o16}{Do not like poetry.}
#'   \item{o17}{Rarely look for a deeper meaning in things.}
#'   \item{o18}{Believe that too much tax money goes to support artists.}
#'   \item{o19}{Am not interested in theoretical discussions.}
#'   \item{o20}{Have difficulty understanding abstract ideas.}
#'   \item{condition}{a group condition each participant received}
#' }
#'
#' @keywords datasets
"efa"
doomlab/learnSEM documentation built on Jan. 25, 2024, 2 p.m.