SSRP: Data from the Social Sciences Replication Project

In ReplicationSuccess: Design and Analysis of Replication Studies

Data from the Social Sciences Replication Project

Description

Data from the Social Sciences Replication Project (SSRP) including the details of the interim analysis. The variables are as follows:

study

Study identifier, usually names of authors from original study

ro

Effect estimate of original study on correlation scale

ri

Effect estimate of replication study at the interim analysis on correlation scale

rr

Effect estimate of replication study at the final analysis on correlation scale

fiso

Effect estimate of original study transformed to Fisher-z scale

fisi

Effect estimate of replication study at the interim analysis transformed to Fisher-z scale

fisr

Effect estimate of replication study at the final analysis transformed to Fisher-z scale

se_fiso

Standard error of Fisher-z transformed effect estimate of original study

se_fisi

Standard error of Fisher-z transformed effect estimate of replication study at the interim analysis

se_fisr

Standard error of Fisher-z transformed effect estimate of replication study at the final analysis

no

Sample size in original study

ni

Sample size in replication study at the interim analysis

nr

Sample size in replication study at the final analysis

po

Two-sided p-value from significance test of effect estimate from original study

pi

Two-sided p-value from significance test of effect estimate from replication study at the interim analysis

pr

Two-sided p-value from significance test of effect estimate from replication study at the final analysis

n75

Sample size calculated to have 90% power in replication study to detect 75% of the original effect size (expressed as the correlation coefficient r)

n50

Sample size calculated to have 90% power in replication study to detect 50% of the original effect size (expressed as the correlation coefficient r)

Usage

data(SSRP)

Format

A data frame with 21 rows and 18 variables

Details

Two-sided p-values were calculated assuming normality of Fisher-z transformed effect estimates.A two-stage procedure was used for the replications. In stage 1, the authors had 90% power to detect 75% of the original effect size at the 5% significance level in a two-sided test. If the original result replicated in stage 1 (two-sided P-value < 0.05 and effect in the same direction as in the original study), the data collection was stopped. If not, a second data collection was carried out in stage 2 to have 90% power to detect 50% of the original effect size for the first and the second data collections pooled. n75 and n50 are the planned sample sizes calculated to reach 90% power in stage 1 and 2, respectively. They sometimes differ from the sample sizes that were actually collected (ni and nr, respectively). See supplementary information of Camerer et al. (2018) for details.

Source

https://osf.io/abu7k

References

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")}

See Also

RProjects

Examples

# plot of the sample sizes
plot(ni ~ no, data = SSRP, ylim = c(0, 2500), xlim = c(0, 400),
     xlab = expression(n[o]), ylab = expression(n[i]))
abline(a = 0, b = 1, col = "grey")


plot(nr ~ no, data = SSRP, ylim = c(0, 2500), xlim = c(0, 400),
     xlab = expression(n[o]), ylab = expression(n[r]))
abline(a = 0, b = 1, col = "grey")

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