replicationInterval (version 2.0.1)

replicationInterval-package: Replication Interval Functions

Description

A common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. One solution to this problem is to examine the effect size from the original study and generate the range of effect sizes that could reasonably be obtained (due to random sampling) in a replication attempt (i.e., calculate a replication interval). If a replication effect size falls outside the replication interval, then that effect likely did not occur due to the effects of sampling error alone. Alternatively, if a replication effect size falls within the replication interval, then the replication effect could have reasonably occurred due to the effects of sampling error alone. This package has functions that calculate the replication interval for the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. The calculations used in version 2.0.0 and onward differ from past calculations due to feedback during the journal review process. The new calculations allow for a more precise interpretation of the replication interval.

Package:

replicationInterval

Type:

Package

Version:

2.0.1

Date:

2016-05-24

License:

MIT License + file LICENSE

Details

ri.r creates a replication interval for a correlation (i.e., r ) ri.d creates a replication interval for a standardized mean difference (i.e., d ) ri.m creates a replication interval for a mean (i.e., M )

ri.r.demo demonstrates RI capture percentage for a correlation (i.e., r ) ri.d.demo demonstrates RI capture percentage for a standardized mean difference (i.e., d ) ri.m.demo demonstrates RI capture percentage for a mean (i.e., M )

References

Spence, J.R. & Stanley, D.J.(in prep). Replication Interval: What to expect when you're expecting a replication. Also: Cumming, G. & Maillardet, R. (2006). Confidence intervals and replication: where will the next mean fall? Psychological Methods, 11(3), 217-227. Estes, W.K. (1997). On the communication of information by displays of standard error and confidence intervals. Psychonomic Bulleting & Review, 4(3), 330-341. Zou, G.Y. (2007). Toward using a confidence intervals to compare correlations. Psychological Methods, 12(4), 399-413.

Examples

ri.r(r=.35,n=100,rep.n=200)
ri.d(d=.65,n1=50,n2=50,rep.n1=100,rep.n2=100)
ri.m(M=2.53,SD=1.02,n=40,rep.n=80)