R/meta_rep.R
replicate.cor.Rd
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.
replicate.cor(alpha, cor1, n1, cor2, n2, s)
alpha level for 1-alpha confidence
estimated correlation in original study
sample size in original study
estimated correlation in follow-up study
sample size in follow-up study
number of control variables in each study (0 for Pearson)
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
Bonett DG (2021). “Design and analysis of replication studies.” Organizational Research Methods, 24(3), 513--529. ISSN 1094-4281, doi:10.1177/1094428120911088 .
replicate.cor(.05, .598, 80, .324, 200, 0)
#> Estimate SE z p LL
#> Original: 0.598 0.07320782 6.589418 4.708045e-09 0.4355043
#> Follow-up: 0.324 0.06376782 4.819037 2.865955e-06 0.1939787
#> Original - Follow-up: 0.274 0.09708614 2.633335 8.455096e-03 0.1065496
#> Average: 0.461 0.04854307 7.634998 2.264855e-14 0.3725367
#> UL
#> Original: 0.7227538
#> Follow-up: 0.4428347
#> Original - Follow-up: 0.4265016
#> Average: 0.5411607
# 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