How do you use post-stratification weights?
First, you adjust the margin of race, so that each of the weighted totals of race categories aligns with the known population total. (This is precisely post-stratification on race). Then you post-stratify on age, then on gender, then on education, then on income.
When should I use weighting?
Unfortunately, weighting is often considered as a process restricted to survey sampling and for the production of statistics related to finite populations. This should not be the case because, when using survey data, statistical analyses, modeling and index estimation should use weights in their calculation.
What is Poststrata?
Poststratification involves adjusting the sampling weights so that they sum to the population sizes within each poststratum. This usually results in decreasing bias because of nonresponse and underrepresented groups in the population. Poststratification also tends to result in smaller variance estimates.
What does weighting do to data?
Weighting is a technique in survey research where the tabulation of results becomes more than a simple counting process. It can involve re-balancing the data in order to more accurately reflect the population and/or include a multiplier which projects the results to a larger universe.
When should you not weight data?
A general rule of thumb is never to weight a respondent less than . 5 (a 50% weighting) nor more than 2.0 (a 200% weighting). Keep in mind that up-weighting data (weight › 1.0) is typically more dangerous than down-weighting data (weight ‹ 1.0).
What is post stratification weight?
Post-stratification weight 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.
What is purposeful sampling?
Purposeful sampling is a technique widely used in qualitative research for the identification and selection of information-rich cases for the most effective use of limited resources (Patton, 2002). Nevertheless, sampling must be consistent with the aims and assumptions inherent in the use of either method.
