RProjects: Data from four large-scale replication projects
In ReplicationSuccess: Design and Analysis of Replication Studies

RProjects

R Documentation

Data from four large-scale replication projects

Description

Data from Reproduciblity Project Psychology (RPP), Experimental Economics Replication Project (EERP), Social Sciences Replication Project (SSRP), Experimental Philosophy Replicability Project (EPRP). The variables are as follows:

study: Study identifier, usually names of authors from original study
project: Name of replication project
ro: Effect estimate of original study on correlation scale
rr: Effect estimate of replication study on correlation scale
fiso: Effect estimate of original study transformed to Fisher-z scale
fisr: Effect estimate of replication study transformed to Fisher-z scale
se_fiso: Standard error of Fisher-z transformed effect estimate of original study
se_fisr: Standard error of Fisher-z transformed effect estimate of replication study
po: Two-sided p-value from significance test of effect estimate from original study
pr: Two-sided p-value from significance test of effect estimate from replication study
po1: One-sided p-value from significance test of effect estimate from original study (in the direction of the original effect estimate)
pr1: One-sided p-value from significance test of effect estimate from replication study (in the direction of the original effect estimate)
pm_belief: Peer belief about whether replication effect estimate will achieve statistical significance elicited through prediction market (only available for EERP and SSRP)
no: Sample size in original study
nr: Sample size in replication study

Usage

data(RProjects)

Format

A data frame with 143 rows and 15 variables

Details

Two-sided p-values were calculated assuming normality of Fisher-z transformed effect estimates. From the RPP only the meta-analytic subset is included, which consists of 73 out of 100 study pairs for which the standard error of the z-transformed correlation coefficient can be computed. For the RPP sample sizes were recalculated from the reported standard errors of Fisher z-transformed correlation coefficients. From the EPRP only 31 out of 40 study pairs are included where effective sample size for original and replication study are available simultaneously. For more details about how the the data was preprocessed see source below and supplement S1 of Pawel and Held (2020).

Source

RPP: The source files were downloaded from https://github.com/CenterForOpenScience/rpp/. The "masterscript.R" file was executed and the relevant variables were extracted from the generated "final" object (standard errors of Fisher-z transformed correlations) and "MASTER" object (everything else). The data set is licensed under a CC0 1.0 Universal license, see https://creativecommons.org/publicdomain/zero/1.0/ for the terms of reuse.

EERP: The source files were downloaded from https://osf.io/pnwuz/. The required data were then manually extracted from the code in the files "effectdata.py" (sample sizes) and "create_studydetails.do" (everything else). Data regarding the prediction market and survey beliefs were manually extracted from table S3 of the supplementary materials of the EERP. The authors of this R package have been granted permission to share this data set by the coordinators of the EERP.

SSRP: The relevant variables were extracted from the file "D3 - ReplicationResults.csv" downloaded from https://osf.io/abu7k. For replications which underwent only the first stage, the data from the first stage were taken as the data for the replication study. For the replications which reached the second stage, the pooled data from both stages were taken as the data for the replication study. Data regarding survey and prediction market beliefs were extracted from the "D6 - MeanPeerBeliefs.csv" file, which was downloaded from https://osf.io/vr6p8/. The data set is licensed under a CC0 1.0 Universal license, see https://creativecommons.org/publicdomain/zero/1.0/ for the terms of reuse.

EPRP: Data were taken from the "XPhiReplicability_CompleteData.csv" file, which was downloaded from https://osf.io/4ewkh/. The authors of this R package have been granted permission to share this data set by the coordinators of the EPRP.

References

Camerer, C. F., Dreber, A., Forsell, E., Ho, T.-H., Huber, J., Johannesson, M., ... Hang, W. (2016). Evaluating replicability of laboratory experiments in economics. Science, 351, 1433-1436. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1126/science.aaf0918")}

Camerer, C. F., Dreber, A., Holzmeister, F., Ho, T.-H., Huber, J., Johannesson, M., ... Wu, H. (2018). Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nature Human Behaviour, 2, 637-644. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41562-018-0399-z")}

Cova, F., Strickland, B., Abatista, A., Allard, A., Andow, J., Attie, M., ... Zhou, X. (2018). Estimating the reproducibility of experimental philosophy. Review of Philosophy and Psychology. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s13164-018-0400-9")}

Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349, aac4716. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1126/science.aac4716")}

Pawel, S., Held, L. (2020). Probabilistic forecasting of replication studies. PLoS ONE. 15, e0231416. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0231416")}

Examples

data("RProjects", package = "ReplicationSuccess")

## Computing key quantities
RProjects$zo <- RProjects$fiso/RProjects$se_fiso
RProjects$zr <- RProjects$fisr/RProjects$se_fisr
RProjects$c <- RProjects$se_fiso^2/RProjects$se_fisr^2

## Computing one-sided p-values for alternative = "greater"
RProjects$po1 <- z2p(z = RProjects$zo, alternative = "greater")
RProjects$pr1 <- z2p(z = RProjects$zr, alternative = "greater")

## Plots of effect estimates
parOld <- par(mfrow = c(2, 2))
for (p in unique(RProjects$project)) {
  data_project <- subset(RProjects, project == p)
  plot(rr ~ ro, data = data_project, ylim = c(-0.5, 1),
       xlim = c(-0.5, 1), main = p, xlab = expression(italic(r)[o]),
       ylab = expression(italic(r)[r]))
  abline(h = 0, lty = 2)
  abline(a = 0, b = 1, col = "grey")
}
par(parOld)

## Plots of peer beliefs
RProjects$significant <- factor(RProjects$pr < 0.05,
                                levels = c(FALSE, TRUE),
                                labels = c("no", "yes"))
parOld <- par(mfrow = c(1, 2))
for (p in c("Experimental Economics", "Social Sciences")) {
  data_project <- subset(RProjects, project == p)
  boxplot(pm_belief ~ significant, data = data_project, ylim = c(0, 1),
          main = p, xlab = "Replication effect significant", ylab = "Peer belief")
  stripchart(pm_belief ~ significant, data = data_project, vertical = TRUE,
             add = TRUE, pch = 1, method = "jitter")
}
par(parOld)

## Computing the sceptical p-value
ps <- with(RProjects, pSceptical(zo = fiso/se_fiso,
                                 zr = fisr/se_fisr,
                                 c = se_fiso^2/se_fisr^2))

ReplicationSuccess documentation built on April 3, 2023, 5:11 p.m.