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Weighting guidance

A number of weights are provided corresponding to different instruments and waves of use. This section of the user guide provides advice on the correct weight to use depending on your analysis.

Weights are provided in order to adjust for differential nonresponse, and for unequal selection probabilities and potential sampling error. Weighted analysis will adjust for response rate differences between subgroups of the sample. The appropriate weight to use will depend on the nature of the analysis being undertaken. Weights should be selected carefully following advice in this section.

If you aim to generalise results to the population of Great Britain, our advice is to always use weights. Note that adjusting for first wave nonresponse is different from adjusting for attrition and requires variables which have values for both responding households and never responding households.

Tips for analysts: Weighting before and after Wave 14

From Wave 14 onwards two main types of weights are provided: cross-sectional weights related to the current wave, and issue weights which can be used to create your own longitudinal weight tailored to your analysis. If all of your data comes from before Wave 14 see the Selecting the correct weight and naming conventions pages.

Not using weights

Note that an unweighted analysis does not reflect the population structure correctly unless the assumptions below are true. It is suggested that researchers publishing or presenting unweighted estimates make these assumptions explicit.

If no weighting is used, the analysis assumes:

  1. that people who live at an address with more than three dwellings or more than three households are the same as those who don’t;
  2. that people who responded at wave 1 are the same with respect to your estimates as those who did not; that people who continued to respond at subsequent waves are the same as those who did not; and that people who responded to each particular instrument used in the analysis (individual interview, self-completion questionnaire etc.) are the same as those who did not.

We therefore strongly suggest using weighted analyses whenever inference to the population is required.

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