Computes the estimate, standard error, and confidence interval for an average slope coefficient in a simple linear regression model from two or more studies. A Satterthwaite adjustment to the degrees of freedom is used to improve the accuracy of the confidence interval.
meta.ave.slope(alpha, n, cor, sdy, sdx, bystudy = TRUE)
alpha level for 1-alpha confidence
vector of sample sizes
vector of estimated correlations
vector of estimated SDs of y
vector of estimated SDs of x
logical to also return each study estimate (TRUE) or not
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
df - degrees of freedom
n <- c(45, 85, 50, 60)
cor <- c(.24, .35, .16, .20)
sdy <- c(12.2, 14.1, 11.7, 15.9)
sdx <- c(1.34, 1.87, 2.02, 2.37)
meta.ave.slope(.05, n, cor, sdy, sdx, bystudy = TRUE)
#> Estimate SE LL UL df
#> Average 1.7731542 0.4755417 0.8335021 2.712806 149.4777
#> Study 1 2.1850746 1.3084468 -0.4536599 4.823809 43.0000
#> Study 2 2.6390374 0.7262491 1.1945573 4.083518 83.0000
#> Study 3 0.9267327 0.8146126 -0.7111558 2.564621 48.0000
#> Study 4 1.3417722 0.8456799 -0.3510401 3.034584 58.0000
# Should return:
# Estimate SE LL UL df
# Average 1.7731542 0.4755417 0.8335021 2.712806 149.4777
# Study 1 2.1850746 1.3084468 -0.4536599 4.823809 43.0000
# Study 2 2.6390374 0.7262491 1.1945573 4.083518 83.0000
# Study 3 0.9267327 0.8146126 -0.7111558 2.564621 48.0000
# Study 4 1.3417722 0.8456799 -0.3510401 3.034584 58.0000