Survey weights in the ESS
For data collection the ESS uses strictly probability-based samples. Every element in the ESS target population should therefore have a greater than zero probability of being included into the sample. When analysing ESS data estimates, the likelihood of each respondent to be part of the sample should also be taken into account - which means that the most accurate estimates will be obtained only after weighting the data.
Which weighting variables are there?
Four weighting variables are available: Analysis weight (anweight - combination of pspwght and pweight), Post-stratification weight (pspwght), Design weight (dweight), and Population weight (pweight).
Anweight corrects for differential selection probabilities within each country as specified by sample design, for nonresponse, for noncoverage, and for sampling error related to the four post-stratification variables, and takes into account differences in population size across countries. It is constructed by first deriving the design weight, then applying a post-stratification adjustment, and then a population size adjustment. Further details of how the weights are derived are documented in the round-specific report on the production of weights. Starting from Round 9, anweight is provided for you in the integrated data file. If you are using data from earlier ESS rounds, you can derive anweight yourself.
While the design weights account for differences in inclusion probabilities, sampling errors (related to attempting to measure only a fraction of the population) and possible non-response errors (which may lead to a systematic over- or under-representation of people with certain characteristics) are still present. Post-stratification weights are a more sophisticated weighting strategy that uses auxiliary information to reduce the sampling error and potential non-response bias. They have been constructed using information on age group, gender, education, and region. The post-stratification weights are obtained by adjusting the design weights in such a way that they will replicate the distribution of the cross-classification of age group, gender, and education in the population and the marginal distribution for region in the population. The population distributions for the adjusting variables were obtained from the European Union Labour Force Survey.
For a short description of how the post-stratification weights were computed in Rounds 1 to 5, see the documentation. For additional details, see the reports listed in the sidebar. For information on Round 8, see the ESS8 weighting strategy.
Several countries use complex sampling designs where some groups or regions of the population have higher probabilities of selection. The main purpose of the design weights is to correct for the fact that in some countries respondents have different probabilities to be part of the sample due to the sampling design used. Applying the weights allows for the construction of design unbiased estimators. The design weights are computed as the inverse of the inclusion probabilities, i.e. the probability of each person to be included into the sample. The inverse inclusion probabilities are then scaled such that their sum equals the net sample size and the mean equals one.
For further information on design weights, see also the ESS Data Documentation Reports.
The population size weights are the same for all persons within a country but differ across countries. These weights correct for the fact that most countries taking part in the ESS have different population sizes but similar sample sizes.Without this weight, any figures combining data from two or more countries might be biased, over-representing smaller countries at the expense of larger ones. The population size weight makes an adjustment to ensure that each country is represented in proportion to its population size. The population size weight is calculated as PWEIGHT=[Population size aged 15 years and above]/[(Net sample size in country)*10 000].
Using the ESS survey weights
Weights should always be used when analysing ESS data. We recommend to use the analysis weight, which is suitable for all types of analysis. For more information on how to use weights on ESS data consult the ESS weighting guide before conducting any analysis of ESS data.