replicate.cor {vcmeta} | R Documentation |
Compares and combines Pearson or partial correlations in original and follow-up studies
Description
This function can be used to compare and combine Pearson or partial correlations from an original study and a follow-up study. The confidence level for the difference is 1 – 2*alpha, which is recommended for equivalence testing.
Usage
replicate.cor(alpha, cor1, n1, cor2, n2, s)
Arguments
alpha |
alpha level for 1-alpha confidence |
cor1 |
estimated correlation in original study |
n1 |
sample size in original study |
cor2 |
estimated correlation in follow-up study |
n2 |
sample size in follow-up study |
s |
number of control variables in each study (0 for Pearson) |
Value
A 4-row matrix. The rows are:
Row 1 summarizes the original study
Row 2 summarizes the follow-up study
Row 3 estimates the difference in correlations
Row 4 estimates the average correlation
The columns are:
Estimate -correlation estimate (single study, difference, average)
SE - standard error
z - t-value for rows 1 and 2; z-value for rows 3 and 4
p - p-value
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
References
Bonett DG (2021). “Design and analysis of replication studies.” Organizational Research Methods, 24(3), 513–529. ISSN 1094-4281, doi:10.1177/1094428120911088.
Examples
replicate.cor(.05, .598, 80, .324, 200, 0)
# Should return:
# Estimate SE z p LL UL
# Original: 0.598 0.07320782 6.589418 4.708045e-09 0.4355043 0.7227538
# Follow-up: 0.324 0.06376782 4.819037 2.865955e-06 0.1939787 0.4428347
# Original - Follow-up: 0.274 0.09708614 2.633335 8.455096e-03 0.1065496 0.4265016
# Average: 0.461 0.04854307 7.634998 2.264855e-14 0.3725367 0.5411607