Confidence interval for a subgroup difference in average point-biserial correlations
Source: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.
Arguments
- 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
set to 1 for weighted variance
set to 2 for unweighted variance
- group
vector of group indicators:
1 for set A
2 for set B
0 to ignore
Value
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
References
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 .
Examples
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