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.

## Requirements

- A weight variable set up as a frequency weight

## Method

- 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.

## Next

How To Create Expansion Weights

How to Configure a Weight from Variable(s)