The workbooks and a pdf version of this guide can be downloaded from here.
A meta-analyst can choose between a 鈥榝ixed effect鈥 model and a 鈥榬andom effects鈥 model. In the 鈥fixed effect鈥 model it is assumed that all differences between effect sizes observed in different studies are only due to sampling error. In other words, it is assumed that there is no 鈥渉eterogeneity鈥. In the 鈥random effects鈥 model it is assumed that there is heterogeneity. The assumptions underlying the fixed effect model are very rarely met. Furthermore, when a fixed effect model would make sense to use, i.e., when there is little variance in effect sizes, the random effects model automatically converges into a fixed effect model. Therefore, it is strongly recommended to always use the random effects model, and to interpret the heterogeneity measures before deciding to use the fixed effect model (if at all).