Computes the estimate, standard error, and confidence interval for an average Spearman correlation from two or more studies. The Spearman correlation is preferred to the Pearson correlation if the relation between the two quantitative variables is monotonic rather than linear or if the bivariate normality assumption is not plausible.
meta.ave.spear(alpha, n, cor, bystudy = TRUE)alpha level for 1-alpha confidence
vector of sample sizes
vector of estimated Spearman correlations
logical to also return each study estimate (TRUE) or not
Returns a matrix. The first row is the average estimate across all studies. If bystudy is TRUE, there is 1 additional row for each study. The matrix has the following columns:
Estimate - estimated effect size
SE - standard error
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Bonett DG (2008). “Meta-analytic interval estimation for bivariate correlations.” Psychological Methods, 13(3), 173--181. ISSN 1939-1463, doi:10.1037/a0012868 .
n <- c(150, 200, 300, 200, 350)
cor <- c(.14, .29, .16, .21, .23)
meta.ave.spear(.05, n, cor, bystudy = TRUE)
#> Estimate SE LL UL
#> Average 0.206 0.02944265 0.14763960 0.2629309
#> Study 1 0.140 0.08071009 -0.02151639 0.2943944
#> Study 2 0.290 0.06627745 0.15476515 0.4145671
#> Study 3 0.160 0.05671051 0.04689807 0.2690514
#> Study 4 0.210 0.06850496 0.07187439 0.3402225
#> Study 5 0.230 0.05136319 0.12690280 0.3281809
# Should return:
# Estimate SE LL UL
# Average 0.206 0.02944265 0.14763960 0.2629309
# Study 1 0.140 0.08031750 -0.02151639 0.2943944
# Study 2 0.290 0.06492643 0.15476515 0.4145671
# Study 3 0.160 0.05635101 0.04689807 0.2690514
# Study 4 0.210 0.06776195 0.07187439 0.3402225
# Study 5 0.230 0.05069710 0.12690280 0.3281809