R/data.r

In autodb: Automatic Database Normalisation for Data Frames

#' Nudge meta-analysis data
#'
#' Data used for a meta-analysis on the effectiveness of nudges, i.e. choice
#' architecture interventions.
#'
#' @usage nudge
#' @format A data frame with 447 effect size measurements and 25 columns:
#'   \enumerate{
#'     \item publication_id, integer ID number for the publication. Note that
#'     two publications were erroneously assigned the same ID number, so this
#'     is not a unique publication identifier.
#'     \item study_id, integer ID number for the study.
#'     \item es_id, integer ID number for the effect size measured.
#'     \item reference, publication citation in "Author(s) (year)" format. Due
#'     to two publications being assigned the same reference, this is also not a
#'     unique publication identifier.
#'     \item title, title of the publication. Due to the error in assigning
#'     publication ID numbers, this is the unique publication identifier within
#'     the data set.
#'     \item year, year of the publication.
#'     \item location, geographical location of the intervention. This is given
#'     as a factor, rather than an integer, using the information
#'     provided in the codebook.
#'     \item domain, factor giving the intervention's behavioural domain.
#'     \item intervention_category, factor giving the intervention's category,
#'     based on the taxonomy in Münscher et al. (2016).
#'     \item intervention_technique, factor giving the intervention's technique,
#'     based on the taxonomy in Münscher et al. (2016).
#'     \item type_experiment, factor giving the type of experiment, as defined
#'     by Harrison and List (2004).
#'     \item population, factor giving the intervention's target population.
#'     This is given as a factor, rather than an integer, using the information
#'     provided in the codebook.
#'     \item n_study, sample size of the overall study.
#'     \item n_comparison, combined sample size of the control and the
#'     intervention for the measured effect size.
#'     \item n_control, sample size of the control condition for the
#'     measured effect size.
#'     \item n_intervention, sample size of the intervention condition
#'     for the measured effect size.
#'     \item binary_outcome, logical for whether the outcome scale is binary or
#'     continuous.
#'     \item mean_control, mean of outcome for the control condition.
#'     \item sd_control, SD of outcome for the control condition.
#'     \item mean_intervention, mean of outcome for the intervention condition.
#'     \item sd_intervention, SD of outcome for the intervention condition.
#'     \item cohens_d, extracted effect size of intervention.
#'     \item variance_d, variance of extracted effect size.
#'     \item approximation, logical for whether effect size extraction involved
#'     approximation.
#'     \item wansink, logical for whether the study was (co-)authored by Brian
#'     Wansink. This was added on revision, because, a few years before
#'     publication, Wansink had many papers retracted or corrected, due to
#'     various questionable practices, resulting in Wansink being determined to
#'     have committed scientific misconduct. This column was added to check
#'     whether the findings were robust to the exclusion of non-retracted
#'     studies by the Cornell Food and Brand Laboratory, of which Wansink was
#'     the director.
#'   }
#' @source \url{https://osf.io/fywae/}
#' @references Mertens S., Herberz M., Hahnel U. J. J., Brosch T. (2022) The
#'   effectiveness of nudging: A meta-analysis of choice architecture
#'   interventions across behavioral domains. *Proc. Natl. Acad. Sci. U.S.A.*, **4**,
#'   119(1).
"nudge"