meta.lm.stdmean2 {vcmeta}

Meta-regression analysis for 2-group standardized mean differences

Description

This function estimates the intercept and slope coefficients in a meta-regression model where the dependent variable is a 2-group standardized mean difference. The estimates are OLS estimates with robust standard errors that accommodate residual heteroscedasticity. Use the unweighted variance standardizer for 2-group experimental designs, and use the weighted variance standardizer for 2-group nonexperimental designs. A single-group standardizer can be used in either experimental or nonexperimental designs.

Usage

meta.lm.stdmean2(alpha, m1, m2, sd1, sd2, n1, n2, X, stdzr)

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

X

matrix of predictor values

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

• set to 3 for square root weighted average variance standardizer

Value

Returns a matrix. The first row is for the intercept with one additional row per predictor. The matrix has the following columns:

• Estimate - OLS estimate

• SE - standard error

• z - z-value

• p - p-value

• 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

n1 <- c(65, 30, 29, 45, 50)
n2 <- c(67, 32, 31, 20, 52)
m1 <- c(31.1, 32.3, 31.9, 29.7, 33.0)
m2 <- c(34.1, 33.2, 30.6, 28.7, 26.5)
sd1 <- c(7.1, 8.1, 7.8, 6.8, 7.6)
sd2 <- c(7.8, 7.3, 7.5, 7.2, 6.8)
x1 <- c(4, 6, 7, 7, 8)
X <- matrix(x1, 5, 1)
meta.lm.stdmean2(.05, m1, m2, sd1, sd2, n1, n2, X, 0)

# Should return:
#      Estimate        SE         z p         LL         UL
# b0 -1.6988257 0.4108035 -4.135373 0 -2.5039857 -0.8936657
# b1  0.2871641 0.0649815  4.419167 0  0.1598027  0.4145255