# Web Survey Bibliography

Title Tips and Tricks for Raking Survey Data (a.k.a. Sample Balancing)

Author Battaglia, M. P.; Izrael, D.; Hoaglin, D.C; Frankel, M. R.

Source AAPOR

Year 2004

Access date 13.05.2014

Full text

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

# Web survey bibliography - AAPOR (11)

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- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2011; 2011
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- AAPOR Report on Online Panels; 2010; P., Blumberg, S. J., Brick, J. M., Rivers, D. et. al.Baker, R. P.
- Tips and Tricks for Raking Survey Data (a.k.a. Sample Balancing); 2004; Battaglia, M. P.; Izrael, D.; Hoaglin, D.C; Frankel, M. R.