Web Survey Bibliography

Title Practical Considerations in Raking Survey Data
Author Battaglia, M. P., Hoaglin, D.C, Franklin, P. D.
Source Survey Practice, 2, 5
Year 2009
Access date 14.04.2014
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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 target 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, referred to as control variables, agree with the corresponding totals for the population. This operation is known as raking ratio estimation (Deming 1943Kalton 1983), raking, or sample-balancing, and the population totals are usually referred to as control totals. Raking is most often used to reduce biases from nonresponse and noncoverage in sample surveys.

Raking 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. The initial design weights in the raking process are often equal to the inverse of the selection probabilities 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×gender×race×household-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 adjustment cells. It also requires control totals for all cells of the cross-classification. Often this is not feasible (e.g., control totals may be available for age×gender×race but not when those cells are subdivided by household 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 higher-order) weighted distributions of the sample are not required to mimic those of the population.

The next two sections discuss the raking algorithm and its convergence. Subsequent sections discuss control totals and several issues that arise in practical applications: two-variable margins, raking at the state level in national surveys, maintaining adjustments for nonresponse and noncoverage, surveys that involve screening, and weight trimming.

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Year of publication2009
Bibliographic typeJournal article
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