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

Source: R/meta_ave.R

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

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 measurement 1 SD standardizer

  • set to 2 for measurement 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)
#>           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

# 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