In a classical meta analysis model y_i = X_i * beta_i + e_i, data
{y_i} are assumed to be independent effect sizes. However, I'm
encountering the following two scenarios:
(1) Each source has multiple effect sizes, thus {y_i} are not fully
independent with each other.
(2) Each source has multiple effect sizes, each of the effect size
from a source can be categorized as one of a factor levels (e.g.,
happy, sad, and neutral). Maybe better denote the data as y_ij, effect
size at the j-th level from the i-th source. I can code the levels
with dummy variables into the X_i matrix, but apparently the data from
the same source are correlated with each other. In this case, I would
like to run a few tests one of which is, for example, whether there is
any difference across all the levels of the factor.
Can metafor handle these two cases?
Thanks,
Gang
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