meta.sub.pbcor | R Documentation |
Computes the estimate, standard error, and confidence interval for a difference in average point-biserial correlations for two mutually exclusive subgroups of studies. Each subgroup can have one or more studies. Two types of point-biserial correlations can be analyzed. One type uses an unweighted variance and is recommended for 2-group experimental designs. The other type uses a weighted variance and is recommended for 2-group nonexperimental designs with simple random sampling (but not stratified random sampling) within each study. Equality of variances within or across studies is not assumed.
meta.sub.pbcor(alpha, m1, m2, sd1, sd2, n1, n2, type, group)
alpha |
alpha level for 1-alpha confidence |
m1 |
vector of estimated means for group 1 |
m2 |
vector of estimated means for group 2 |
sd1 |
vector of estimated SDs for group 1 |
sd2 |
vector of estimated SDs for group 2 |
n1 |
vector of group 1 sample sizes |
n2 |
vector of group 2 sample sizes |
type |
|
group |
vector of group indicators:
|
Returns a matrix with three rows:
Row 1 - estimate for Set A
Row 2 - estimate for Set B
Row 3 - estimate for difference, Set A - Set B
The columns are:
Estimate - estimated average correlation or difference
SE - standard error
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Bonett2020bvcmeta
m1 <- c(45.1, 39.2, 36.3, 34.5)
m2 <- c(30.0, 35.1, 35.3, 36.2)
sd1 <- c(10.7, 10.5, 9.4, 11.5)
sd2 <- c(12.3, 12.0, 10.4, 9.6)
n1 <- c(40, 20, 50, 25)
n2 <- c(40, 20, 48, 26)
group <- c(1, 1, 2, 2)
meta.sub.pbcor(.05, m1, m2, sd1, sd2, n1, n2, 2, group)
# Should return:
# Estimate SE LL UL
# Set A: 0.36338772 0.08552728 0.1854777 0.5182304
# Set B: -0.01480511 0.08741322 -0.1840491 0.1552914
# Set A - Set B: 0.37819284 0.12229467 0.1320530 0.6075828
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