se.ave.cor.nonover {vcmeta}

Computes the standard error for the average of two Pearson correlations with no variables in common that have been estimated from the same sample

Description

In a study that reports the sample size and six correlations (cor12, cor34, cor13, cor14, cor23, and cor24) where variables 1 and 3 are different measurements of one attribute and variables 2 and 4 are different measurements of a second attribute, this function can be used to compute the average of cor12 and cor34 and its standard error. Note that cor12 and cor34 have no variable in common (i.e., no "overlapping" variable). 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 cor34.

Usage

se.ave.cor.nonover(cor12, cor34, cor13, cor14, cor23, cor24, n)

Arguments

cor12

estimated correlation between variables 1 and 2

cor34

estimated correlation between variables 3 and 4

cor13

estimated correlation between variables 1 and 3

cor14

estimated correlation between variables 1 and 4

cor23

estimated correlation between variables 2 and 3

cor24

estimated correlation between variables 2 and 4

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 cor34

• SE - standard error

• VAR(cor12) - variance of cor12

• VAR(cor34) - variance of cor34

• COV(cor12,cor34) - covariance of cor12 and cor34

Examples

se.ave.cor.nonover(.357, .398, .755, .331, .347, .821, 100)

# Should return:
#               Estimate         SE VAR(cor12)  VAR(cor34) COV(cor12,cor34)
# Correlation:  0.377500 0.07768887 0.00784892 0.007301895      0.004495714
# Fisher:       0.397141 0.09059993 0.01030928 0.010309278      0.006122153