Given a number of different studies estimating the same effect size, it is often desired to explain heterogeneity of outcomes using concomitant covariates. For very large sample sizes, effect size estimates are approximately normally distributed and a straightforward application of weighted least squares is appropriate. However in practice within study sample variances are often small to moderate, casting doubt on the normality assumption for effect sizes and results based on weighted least squares. One can alternatively variance stabilize the effect size estimates and adopt a generalized linear model. Both methods are compared on two examples when effect sizes are the standardized difference of means. Then simulation studies are conducted to compare the coverage and width of confidence intervals for the meta-regression coefficients. These simulations show that the coverage probability associated with weighted least squares can be well below the nominated level for small to moderate sample sizes. Further empirical investigations reveal a bias in estimation due to using estimated weights which were assumed to be known. For these models, the generalized linear model approach resulted in much improved coverage probabilities.