Confidence interval for an average correlation of any type

Source: R/meta_ave.R

Computes the estimate, standard error, and confidence interval for an average correlation. Any type of correlation can be used (e.g., Pearson, Spearman, semipartial, factor correlation, Gamma coefficient, Somers d coefficient, tetrachoric, point-biserial, biserial, etc.).

meta.ave.cor.gen(alpha, cor, se, bystudy = TRUE)

Arguments

alpha

alpha level for 1-alpha confidence

cor

vector of estimated correlations

se

vector of standard errors

bystudy

logical to also return each study estimate (TRUE) or not

Value

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

References

Bonett DG (2008). “Meta-analytic interval estimation for bivariate correlations.” Psychological Methods, 13(3), 173--181. ISSN 1939-1463, doi:10.1037/a0012868 .

Examples

cor <- c(.396, .454, .409, .502, .350)
se <- c(.104, .064, .058, .107, .086)
meta.ave.cor.gen(.05, cor, se, bystudy = TRUE)
#>         Estimate         SE        LL        UL
#> Average   0.4222 0.03853362 0.3438560 0.4947070
#> Study 1   0.3960 0.10400000 0.1753200 0.5787904
#> Study 2   0.4540 0.06400000 0.3200675 0.5701415
#> Study 3   0.4090 0.05800000 0.2893856 0.5160375
#> Study 4   0.5020 0.10700000 0.2651183 0.6817343
#> Study 5   0.3500 0.08600000 0.1716402 0.5061435

# Should return:
#         Estimate         SE        LL        UL
# Average   0.4222 0.03853362 0.3438560 0.4947070
# Study 1   0.3960 0.10400000 0.1753200 0.5787904
# Study 2   0.4540 0.06400000 0.3200675 0.5701415
# Study 3   0.4090 0.05800000 0.2893856 0.5160375
# Study 4   0.5020 0.10700000 0.2651183 0.6817343
# Study 5   0.3500 0.08600000 0.1716402 0.5061435