What weights to use in Stata?
There are four different ways to weight things in Stata. These four weights are frequency weights ( fweight or frequency ), analytic weights ( aweight or cellsize ), sampling weights ( pweight ), and importance weights ( iweight ). Frequency weights are the kind you have probably dealt with before.
What are sample weights?
Sampling weights, also known as survey weights, are positive values associated with the observations (rows) in your dataset (sample), used to ensure that metrics derived from a data set are representative of the population (the set of observations). Ideally, a sample is perfectly reflective of the population.
How do you calculate sample size weight?
The formula to calculate the weights is W = T / A, where “T” represents the “Target” proportion, “A” represents the “Actual” sample proportions and “W” is the “Weight” value.
What are analytic weights?
Analytical weights: An analytical weight (sometimes called an inverse variance weight or a regression weight) specifies that the i_th observation comes from a sub-population with variance σ2/wi, where σ2 is a common variance and wi is the weight of the i_th observation.
Should I weight my data?
When data must be weighted, try to minimize the sizes of the weights. 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).
Why do we use sample weights?
Sampling weights (the inverse probabilities of selection for each observation) allow us to reconfigure the sample as if it was a simple random draw of the total population, and hence yield accurate population estimates for the main parameters of interest.
When can sampling weights be ignored?
When conditioning on the population values of the design variables, if the sampling scheme satisfies the condition (3.3), it can be ignored in the inference process and no additional adjustments, like weighting for example, are needed. ignored and standard regression procedures apply.
How do you calculate effective sample size?
The effective sample size (ESS) is an estimate of the sample size required to achieve the same level of precision if that sample was a simple random sample. Mathematically, it is defined as n/D, where n is the sample size and D is the design effect.
Should I weight my survey data?
What are the different types of weights in Stata?
Weighted Data in Stata. There are four different ways to weight things in Stata. These four weights are frequency weights (fweight or frequency), analytic weights (aweight or cellsize), sampling weights (pweight), and importance weights (iweight).
Is there a problem with Stata’s aweight paradigm?
So we have found a problem with Stata’s aweight paradigm. Stata assumes that with aweights, the scale of the weights does not matter. This is not true for the estimate of sigma. John Gleason (1997) wrote an excellent article that shows the estimate of rho also depends on the scale of the weights.
What’s the name of the rake program in Stata?
This > technique for this is known as “raking”. In Stata this is available > in Nick Winter’s program -survwgt rake-. Type “ssc install > survwgt”.
Which is an estimate of the variance of muhat?
V_srs: an estimate of the variance of muhat assuming a simple random sample of the same number of observations muhat is the obvious weighted sample mean, and V_db is pretty complicated; see [SVY] variance estimation for details.