Description Usage Format Details Source References See Also Examples

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

`no`

Sample size in original study

`nr`

Sample size in replication study

1 |

A data frame with 143 rows and 13 variables

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 coeffient can
be computed. For the RPP also sample sizes were recalculated from
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 details about how the the data was preprocessed see
supplement S1 of Pawel and Held (2020).

RPP: https://github.com/CenterForOpenScience/rpp/

EERP: https://osf.io/pnwuz/

SSRP: https://osf.io/abu7k

EPRP: https://osf.io/4ewkh/

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.
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.
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*. doi: 10.1007/s13164-018-0400-9

Open Science Collaboration. (2015). Estimating the reproducibility of
psychological science. *Science*, **349**, aac4716.
doi: 10.1126/science.aac4716

Pawel, S., Held, L. (2020). Probabilistic forecasting of replication studies.
*PLoS ONE*. **15**, e0231416. doi: 10.1371/journal.pone.0231416

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | ```
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))
``` |

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