Mark Schaffer <[email protected]> writes:
> In addition to the other advice you've gotten, a simple way to keep aweights
> and pweights straight in your head is that (usually? always?) aweights +
> robust = pweights. In any dataset, try (1) aweights on its own, (2)
> aweights with robust, and (3) pweights. You will usually (always?) find
> that (2) and (3) give you the same output.
aweights + robust is _usually_ equal to pweights, but that is more a
computational coincidence than anything substantive. One exception to this
equivalence is -intreg-, where some extra work has to be done to honor the
definition of aweights.
By definition, aweights are for cell means data, i.e. data which have been
collapsed through averaging, and pweights are for sampling weights. In most
cases, because of the way observation-level likelihood contributions are
weighted, you can treat a cell mean of 5 observations (aweight) equivalently
to one observation which represents 5 population members (pweight) as long as
you do a robust variance calculation inherent to the analysis of survey data.
The apparent universal equivalence of the two, however, is part of the reason
that mistakes can be made and articles written citing the misuse of weights.
I would not recommend that someone use aweights + robust in lieu of pweights
when the latter are unavailable for a particular command, or vice versa. If a
type of weighting is unavailable for a command, there is usually good reason,
for example, the concept of an aweight for panel data is not well-defined.
--Bobby
[email protected]
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