In most situations, when people refer to weights they are referring to sampling weights. By default, Q assumes that any weight is a sampling weight designed to correct for representativeness issues in a sample (e.g., to correct for an over- or under-representation of women in the sample).
However, it is possible to scale the sampling weight so that it can be analyzed using software that is designed for a frequency weight.
For example, if a study has a sample size of 300, an average weight of 1.3, and an effective sample size of 120, then each weight is multiplied by 120 / (1.3 * 300). The resulting weight, which can be referred to as the calibrated weight, is then treated as a frequency weight.
In this situation, statistical inference is conducted under the assumption that the weights are frequency weights where the frequency weights are the supplied weights normalized to have an average value of 1 and then divided by the supplied extra design effect.
- A weight variable set up as a frequency weight
- From the toolbar menu, select Edit > Project Options > Customize and then go to the Statistical Assumptions tab.
- On the Significance levels tab, change the Weights and significance setting to deff = 1.
- Click OK to apply the updated settings.