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Meta-regression analysis for 2-group standardized mean differences

Source: R/meta_model.R
meta.lm.stdmean2.Rd

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.

For more details, see Section 3.4 of Bonett (2021, Volume 5).

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 .

Bonett DG (2021). Statistical Methods for Psychologists, Vol 1-5, https://dgbonett.sites.ucsc.edu/.

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)
#>    Estimate      SE      z p      LL      UL
#> b0  -1.6988 0.41080 -4.135 0 -2.5040 -0.8937
#> b1   0.2872 0.06498  4.419 0  0.1598  0.4145

# Should return:
#     Estimate      SE      z p      LL      UL
# b0   -1.6988 0.41080 -4.135 0 -2.5040 -0.8937
# b1    0.2872 0.06498  4.419 0  0.1598  0.4145