Rescaling is based on two methods: For pweights_a
, the sample weights
probability_weights
are adjusted by a factor that represents the proportion
of group size divided by the sum of sampling weights within each group. The
adjustment factor for pweights_b
is the sum of sample weights within each
group divided by the sum of squared sample weights within each group (see
Carle (2009), Appendix B). In other words, pweights_a
"scales the weights
so that the new weights sum to the cluster sample size" while pweights_b
"scales the weights so that the new weights sum to the effective cluster
size".
Regarding the choice between scaling methods A and B, Carle suggests that
"analysts who wish to discuss point estimates should report results based on
weighting method A. For analysts more interested in residual between-group
variance, method B may generally provide the least biased estimates". In
general, it is recommended to fit a non-weighted model and weighted models
with both scaling methods and when comparing the models, see whether the
"inferential decisions converge", to gain confidence in the results.
Though the bias of scaled weights decreases with increasing group size,
method A is preferred when insufficient or low group size is a concern.
The group ID and probably PSU may be used as random effects (e.g. nested
design, or group and PSU as varying intercepts), depending on the survey
design that should be mimicked.