R/anchor_estimate.R

In gitrman/itns: Datasets from the book Introduction to the New Statistics

#'Two Independent Groups - Anchor Adjust Effect
#'
#'An example of data from a study with a two independent groups design used in Chapter 7 of the book 
#'\emph{Introduction to the New Statistics}.
#'
#'Psychological research suggests that how a question is worded can influence people's judgements.
#'This is known as the 'anchor-adjustment' effect.  A team of psychologists conducted a multi-site study
#'attempting to replicate this phenomenon. Participants were asked to estimate three different quantities (number of
#'babies born in the U.S. each day; population of Chicago; height of Mount
#'Everest).  For each question, though, participants were either given a low or
#'high numerical anchor (see below).  For example, they were told either that
#'the number of babies born in the U.S. was more than 200,000 (low anchor) or
#'less than 5,000,000 (high anchor).  The primary research question was: to what extent does
#'having a low or high anchor in mind influence the estimate made?
#'
#'The anchors were as follows:
#'
#'\emph{Babies}
#'
#'Low: More than 100 babies are born per day in the United States. How many babies do you think are born in the U.S. each day?
#'
#'High: Less than 50,000 babies are born per day in the United States. How many babies do you think are born in the U.S. each day?
#'
#'\emph{Population}
#'
#'Low: The population of Chicago is more than 200,000. What do you think the population of Chicago is?
#'
#'High: The population of Chicago is less than 5,000,000. What do you think the population of Chicago is?
#'
#'\emph{Everest}
#'
#'High: Mount Everest is shorter than 45,500 feet. How tall do you think Mount Everest is?
#'
#'Low: Mount Everest is taller than 2,000 feet.  How tall do you think Mount Everest is?
#'
#'@format A data frame with 90 rows and 8 variables:
#'\describe{
#'  \item{sessionid}{Respondent identifier}
#'  \item{location}{Lab that ran the study.  Equal to "ithaca" for all participants in this dataset.}
#'  \item{babies_anchor}{Babies Anchor: "lowanchor" (100) or "highanchor" (50,000)}
#'  \item{babies_est}{Participants' estimate of number of  babies born per day}
#'  \item{chicago_anchor}{Chicago Anchor: "low anchor" (200,000) or "high anchor" (5,000,000)}
#'  \item{chicago_est}{Participants' estimate of the population of Chicago}
#'  \item{everest_anchor}{Everest Anchor: "low anchor" (2,000 feet) or "high anchor" (45,000 feet)}
#'  \item{everest_est}{Participants' estimate of height of Mt Everest (in feet)}
#'}
#'
#'@source
#'The data is available online at
#'  \url{https://osf.io/wx7ck/} and is from one replication site (Ithaca) of the following study:
#'
#'Klein, R. A., Ratliff, K. A., Vianello, M., Adams ., R. B., Bahnik, S., Bernstein, M. J., ... & Nosek, B. A. (2014).
#'Investigating Variation in Replicability. \emph{Social Psychology, 45}, 142-152. \url{http://doi.org/10.1027/1864-9335/a000178}
#'
#'  The original study exploring this effect is: Jacowitz, K. E., & Kahneman, D.
#'  (1995). Measures of Anchoring in Estimation Tasks. \emph{Personality and
#'  Social Psychology Bulletin, 21}, 1161-1166.
#'  \url{http://doi.org/10.1177/01461672952111004}
#'
#'@references
#'Cumming, G., & Calin-Jageman, R. (2017).
#'  \emph{Introduction to the New Statistics}. New York; Routledge.
"anchor_estimate"