replicate.cor {vcmeta}

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 Pearson correlation in original study

n1

sample size in original study

cor2

estimated Pearson 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 - Pearson or partial 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