R/meta_se.R
se.ave.cor.nonover.Rd
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.
se.ave.cor.nonover(cor12, cor34, cor13, cor14, cor23, cor24, n)
estimated correlation between variables 1 and 2
estimated correlation between variables 3 and 4
estimated correlation between variables 1 and 3
estimated correlation between variables 1 and 4
estimated correlation between variables 2 and 3
estimated correlation between variables 2 and 4
sample size
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
se.ave.cor.nonover(.357, .398, .755, .331, .347, .821, 100)
#> 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
# 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