Weighting (also known as sample balancing, post-survey adjustment, raking, and poststratification) is the technique of adjusting survey results to bring them into line with some known characteristics of the population. For example, if a sample contains 40% males and the population contains 49% males, weighting can be used to adjust for this discrepancy.
What you'll get from this ebook
This How To Weight Survey Data ebook describes all the key steps involved in weighting survey data (also known as sample balancing).
- Post-survey adjustment
- Raking
- Poststratification
- Non-response weighting
More about this ebook
Commercial weighting tends to be different to that used in academic studies which is in-turn, different to that used in official statistics and other government statistics. This book focuses on the commercial applications of weighting.1
A key aspect of commercial weighting is that typically one or a small number of weight variables are created for use in many subsequent analyses. Where there is only a single analysis of primary interest, such as a single regression model or an opinion poll, alternative approaches may be preferable, including:
- Performing a regression with the adjustment variables that would have been used to create the weights (see the next chapter) included as predictors.
- Using MrP (“Mister P”). See: http://www.misterp.org/papers.html.
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