Contents
How does survey weighting work?
In survey sampling, weighting is one of the critical steps. For a given sample survey, to each unit of the selected sample is attached a weight (also called an estimation weight) that is used to obtain estimates of population parameters of interest, such as the average income of a certain population.
What is the target population for your sampling plan?
Sampling is the process of selecting a representative group from the population under study. The target population is the total group of individuals from which the sample might be drawn. A sample is the group of people who take part in the investigation. The people who take part are referred to as “participants”.
How are sampling weights related to target population?
Statistically, the sampling weights re-balance the data set so that the sampled data set represents the target population as closely as reasonably possible. Sampling weights are often the reciprocal of the likelihood of being sampled (i.e., selection probability) of the sampling unit.
Is it okay to weight back to the original target population?
As a general rule, yes, it is okay, and indeed desirable, to weight back to the original target population. Your goal in these problems is usually to estimate an unknown population quantities that is aggregated over a stratified group.
What was the target population for this study?
In a previous study, the researchers started with a target population frame of 50,000 people. They then eliminated 15,000 on the grounds that they had recently received other surveys, leaving a survey population of 35,000. From this, they drew a stratified sample of 4,500 people. 1,730 completed surveys were returned.
How are the weights of a survey calculated?
The researchers stratified on the basis of the 35,000 and calculated survey weights on that basis. However, they seem to have adjusted the weights to give results for the 50,000 — the sample weights add up to 50,000. They also did some non-response weighting, based on the observation that proportionately more women than men responded.