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Title Tips and Tricks for Raking Survey Data (a.k.a. Sample Balancing)
Source AAPOR
Year 2004
Access date 13.05.2014
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Abstract
A survey sample may cover segments of the target population
in proportions that do not match the proportions of those
segments in the population itself. The differences may arise,
for example, from sampling fluctuations, from nonresponse, or
because the sample design was not able to cover the entire
population. In such situations one can often improve the
relation between the sample and the population by adjusting
the sampling weights of the cases in the sample so that the
marginal totals of the adjusted weights on specified
characteristics agree with the corresponding totals for the
population. This operation is known as raking ratio estimation
(Kalton 1983), raking, or sample-balancing, and the
population totals are usually referred to as control totals.
Raking may reduce nonresponse and noncoverage biases, as
well as sampling variability. The initial sampling weights in
the raking process are often equal to the reciprocal of the
probability of selection and may have undergone some
adjustments for unit nonresponse and noncoverage. The
weights from the raking process are used in estimation and
analysis. The adjustment to control totals is sometimes
achieved by creating a cross-classification of the categorical
control variables (e.g., age categories x gender x race x
family-income categories) and then matching the total of the
weights in each cell to the control total. This approach,
however, can spread the sample thinly over a large number of
cells. It also requires control totals for all cells of the crossclassification.
Often this is not feasible (e.g., control totals
may be available for age x gender x race but not when those
cells are subdivided by family income). The use of marginal
control totals for single variables (i.e., each margin involves
only one control variable) often avoids many of these
difficulties. In return, of course, the two-variable (and higherorder)
weighted distributions of the sample are not required to
mimic those of the population. Raking (or sample-balancing)
usually proceeds one variable at a time, applying a
proportional adjustment to the weights of the cases that belong
to the same category of the control variable. Izrael et al.
(2000) introduced a SAS macro for raking (sometimes
referred to as the IHB raking macro) that combines simplicity
and versatility. More recently, the IHB raking macro has been
enhanced to increase its utility and diagnostic capability
(Izrael et al. 2004).
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Year of publication2004
Bibliographic typeJournal article
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