Official documentation regarding analytical weights states (where aweights and fweights refer to analytic and frequency weights respectively):.

By default SPSS uses something like aweights for their regression procedure. There are multiple reasons why the sample may not exactly reflect the population. Meanwhile, for sampling weights, the text later on states that (pweights being sampling weights): Analytic weights observations as if each observation is a mean computed from a sample of size n, where n is the weight variable. Survey weights: Survey weights (also called sampling weights or probability weights) indicate that an observation in a survey represents a certain number of people in a finite population. When you use pweight, Stata uses a Sandwich (White) estimator to compute thevariance-covariancematrix. Example 1: Using expand and sample. Calibrating Survey Weights in Stata Jeff Pitblado StataCorp LLC 2018 Canadian Stata Users Group Meeting Vancouver, Canada. Survey weights are often the reciprocals of the selection probabilities for the survey design. Unconditional level 1 sampling weights can be made conditional by dividing by the level 2 sampling weight. Thus, if you want to get the right standard error of the “mean” (i.e., muhat), you must consider clustering and stratification as well as sampling weights. Outline Motivation Methods Syntax Stata Example Summary.
For reference, Stata treats frequency, sampling and analytic weights identically for point estimates, but not for their variance. These weights are used for sample survey designs. PISA collects data from a sample, not on the whole population of 15-year-old students. We will be looking at a dataset with 200 frequency-weighted observations. For example, if you have a population of 10 widgets and you select 3 into your sample, your sampling fraction would be 3/10 and your probability weight would be 10/3 = 3.33. I would appreciate if anyone could provide a code to do this in Stata. Jeremy Albright. Sample Weights & Design Effects The NLSY97 sampling weights, which are constructed in each survey year, provide the researcher with an estimate of how many individuals in the United States are represented by each NLSY97 respondent. Probability or sampling weights are the inverse of the probability that an observation from a population will be included in the sample. Hi everyone, I am using the NLSY97 data to do a matching analysis, and I think it important to consider the sampling weights of NLSY97. Methodology to analyse the PISA database Use sampling weights for unbiased estimates and standard-errors . Both Stata’s mixed command and Mplus have options for scaling the level 1 weights. Sampling weights are the inverse of the likelihood of being sampled. Sampling weights, clustering, and stratification can all have a big effect on the standard error of muhat. I found some useful

Sampling weights (a.k.a. You often find this type of weight in complex survey data. probability weights) cover situations where random sampling without replacement occurs. Stata can use aweights or pweights.
Stata offers three options: size, effective and gk. This can also partly compensate for a poor design at the expense of increasing standard errors. Posted on Mar 1, 2019 sampling weights. Stata has contributed commands ipfweight, ipfraking, survwgt rake, and calibrate that can do this. Weights make it possible to form inferences based on a sample that does not look exactly like the population from which it was drawn. Motivation Survey data analysis We collect data from a population of interest so that we can describe the population and make inferences about the population. • [in Stata, these are the pweights] 2 Types of Survey Weights • Two most common types: –Design Weights –Post-Stratificationor Non-response weights • Design Weight: –Normally used to compensate for over-or under-sampling of specific cases or for disproportionate stratification. Now I want to estimate summary statistics using bootstrap; however, but for the bootstrap process I want to sample observations based on sampling weight. Probability sampling weights (pweights). I have a sampling -weight- variable which came with data. Moreprecisely,ifyouconsiderthefollowingmodel: y j = x j + u j where j indexes mobservations and there are k variables, and estimate it using pweight,withweightsw j,theestimatefor isgivenby: ^ = (X~ 0X~) 1X~ y~ In Stata, you can easily sample from your dataset using these weights by using expand to create a dataset with an observation for each unit and then sampling from your expanded dataset. So first of all you need to know the proportions of boys and girls in your population, too. If you google "sampling weight construction" or related terms, you might also find some helpful suggestions. The frequency weights (fw) range from 1 to 20. Where Do Sampling Weights Come From?