se.ave.cor.over: Computes the standard error for the average of two Pearson...

In dgbonett/vcmeta: Varying Coefficient Meta-Analysis

Computes the standard error for the average of two Pearson correlations with one variable in common that have been estimated from the same sample

Description

In a study that reports the sample size and three correlations (cor12, cor13, and cor23 where variable 1 is called the "overlapping" variable), and variables 2 and 3 are different measurements of the same attribute, this function can be used to compute the average of cor12 and cor13 and its standard error. The average correlation and the standard error from this function can be used as input in the meta.ave.cor.gen function in a meta-analysis where some studies have reported cor12 and other studies have reported cor13.

Usage

se.ave.cor.over(cor12, cor13, cor23, n)

Arguments

cor12	estimated correlation between variables 1 and 2
cor13	estimated correlation between variables 1 and 3
cor23	estimated correlation between variables 2 and 3
n	sample size

Value

Returns a two-row matrix. The first row gives results for the average correlation and the second row gives the results with a Fisher transformation. The columns are:

• Estimate - estimated average of cor12 and cor13

• SE - standard error

• VAR(cor12) - variance of cor12

• VAR(cor13) - variance of cor13

• COV(cor12,cor13) - covariance of cor12 and cor13

Examples

se.ave.cor.over(.462, .518, .755, 100)

# Should return:
#                Estimate         SE  VAR(cor12) VAR(cor13) COV(cor12,cor13)
# Correlation:  0.4900000 0.07087351 0.006378045 0.00551907      0.004097553
# Fisher:       0.5360603 0.09326690 0.010309278 0.01030928      0.007119936

dgbonett/vcmeta documentation built on July 12, 2024, 3:12 p.m.