R/meta_comp.R
meta.sub.pbcor.Rd
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 level for 1-alpha confidence
vector of estimated means for group 1
vector of estimated means for group 2
vector of estimated SDs for group 1
vector of estimated SDs for group 2
vector of group 1 sample sizes
vector of group 2 sample sizes
set to 1 for weighted variance
set to 2 for unweighted variance
vector of group indicators:
1 for set A
2 for set B
0 to ignore
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
Bonett DG (2020). “Point-biserial correlation: Interval estimation, hypothesis testing, meta-analysis, and sample size determination.” British Journal of Mathematical and Statistical Psychology, 73(S1), 113--144. ISSN 0007-1102, doi:10.1111/bmsp.12189 .
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)
#> 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
# 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