Ideally, a selected sample is a miniature of the population it came from. This should be reflected in the sample being representative of all variables measured in the survey.
Unfortunately, this is usually not the case. One of the problems is non-response. It may cause some groups to be over-or under-represented.
Another problem is self-selection (in an online survey). If such problems occur, no reliable conclusions can be drawn from the observed survey data unless something has been done to correct the lack of representativity.
A commonly applied correction technique is weighting adjustment. It assigns an adjustment weight to each survey respondent.
Persons in underrepresented groups get a weight larger than 1, and those in overrepresented groups get a weight smaller than 1.
In the computation of means, totals, and percentages, not just the values of the variables are used, but the weighted values.
A weighting adjustment technique can only be used if proper auxiliary variables are available. Such variables must have been measured in the survey, and their population distribution must be available. Typical auxiliary variables are gender, age, marital status, and country regions.
By comparing the observed frequency distribution of a variable with its population distribution, you can establish whether the survey response is representative of this variable.
If there exists a substantial difference between the sample distribution and the population distribution, you can conclude that there is a lack of representativity concerning this variable.