meta.ave.stdmean.ps {vcmeta}

Confidence interval for an average standardized mean difference from paired-samples studies

Description

Computes the estimate, standard error, and confidence interval for an average standardized mean difference from two or more paired-samples studies. Unweighted variances and single group variance are options for the standardizer. Equality of variances within or across studies is not assumed.

Usage

meta.ave.stdmean.ps(alpha, m1, m2, sd1, sd2, cor, n, stdzr, bystudy = TRUE)

Arguments

alpha

alpha level for 1-alpha confidence

m1

vector of estimated means for measurement 1

m2

vector of estimated means for measurement 2

sd1

vector of estimated SDs for measurement 1

sd2

vector of estimated SDs for measurement 2

cor

vector of estimated correlations for paired measurements

n

vector of sample sizes

stdzr

• set to 0 for square root unweighted average variance standardizer

• set to 1 for group 1 SD standardizer

• set to 2 for group 2 SD standardizer

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 (2009). “Meta-analytic interval estimation for standardized and unstandardized mean differences.” Psychological Methods, 14(3), 225–238. ISSN 1939-1463, doi:10.1037/a0016619.

Examples

m1 <- c(23.9, 24.1)
m2 <- c(25.1, 26.9)
sd1 <- c(1.76, 1.58)
sd2 <- c(2.01, 1.76)
cor <- c(.78, .84)
n <- c(25, 30)
meta.ave.stdmean.ps(.05, m1, m2, sd1, sd2, cor, n, 1, bystudy = TRUE)

# Should return: 
#           Estimate        SE        LL         UL
# Average -1.1931045 0.1568034 -1.500433 -0.8857755
# Study 1 -0.6818182 0.1773785 -1.029474 -0.3341628
# Study 2 -1.7721519 0.2586234 -2.279044 -1.2652594