meta.ave.cor {vcmeta}

Confidence interval for an average Pearson or partial correlation

Description

Computes the estimate, standard error, and confidence interval for an average Pearson or partial correlation from two or more studies. The sample correlations must be all Pearson correlations or all partial correlations. Use the meta.ave.gen function to meta-analyze any combination of Pearson, partial, or Spearman correlations.

Usage

meta.ave.cor(alpha, n, cor, s, bystudy = TRUE)

Arguments

alpha

alpha level for 1-alpha confidence

n

vector of sample sizes

cor

vector of estimated correlations

s

number of control variables (set to 0 for Pearson)

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

n <- c(55, 190, 65, 35)
cor <- c(.40, .65, .60, .45)
meta.ave.cor(.05, n, cor, 0, bystudy = TRUE)

# Should return:
#         Estimate         SE        LL        UL
# Average    0.525 0.05113361 0.4176678 0.6178816
# Study 1    0.400 0.11430952 0.1506943 0.6014699
# Study 2    0.650 0.04200694 0.5594086 0.7252465
# Study 3    0.600 0.08000000 0.4171458 0.7361686
# Study 4    0.450 0.13677012 0.1373507 0.6811071