Weights can be optimized differently. There is more than one
reasonable way to create weights for a survey. Two statisticians might come up with different weights for the same survey, with each set more accurate for some purposes than others. This doesn’t mean weighting is unscientific or that one set of weights is as good as another; it just means that weights optimized for one purpose, such as accurately representing the relative proportions of men and women, will not necessarily be the same as weights optimized for another purpose. Account for household selection. Weights are variables that
determine how much each respondent counts. They are calculated by multiplying several weighting factors together. There may be factors for household selection, respondent selection, and nonresponse as well as a poststratification ad- justment (all described below)—and the final weight is the product of all of these parts. The first component of most weighting describes the relative probability of selection of each household in the sample. In a telephone survey, the major component of household selection probability is the number of telephone numbers that could be used to reach the household (including cell phones, if cell phone num- bers are included in the sample). The weight is the inverse of the number of phone numbers, because households with many numbers have many chances to be included in the survey. A household with only one phone number would have a weighting factor of 1 at this stage, while a household with two numbers would have a factor of ½. Other aspects
30 Campaigns & Elections | Canadian Edition
of sample design can also affect household selection prob- ability, such as samples that are stratified to include cer- tain types of households. The overall household selection weight adjustment is the product of all of these design fac- tors multiplied together.
Typically, very little is known about people who fail to complete a survey, but one thing that is routinely known is where they live.
Account for respondent selection. In most surveys, only one
person in a selected household will be interviewed. The major component of respondent selection probability is the number of people in the household who are eligible for the survey. For example, in a survey of registered vot- ers, the weighting factor for respondent selection would be the number of registered voters living in the household. It is important to weight by this factor to account for the fact that people living in households with several registered voters are less likely to be selected than people living alone.
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