Replication Method for Survey Estimation

In summary, the conversation discusses using replication methods for estimation when dealing with a dataset that has no design information, only weighting. The dataset in question is a public data of 15,000 people from the Census, but the survey design variables are not publicly released. The conversation also mentions using GVF to compute variance of variable estimations and the challenges of replicating the original sample's design without access to the survey design variables. The speaker also clarifies the use of the terms "replication" and "survey weighting" in this context.
  • #1
zli034
107
0
Hi all,

What is a good way to use replication methods for estimation? For a dataset with no design information, only weighting remain. I know the basic principles of replication. Are there any considerations to use replication effectively?

There is a public data of 15,000 people from the Census I'm using to estimate its mean and standard error for some variables. The dataset has its survey weighting, but the survey design variables are not released to the public. The clustering and stratification stage of the survey were based on geographic information, I have no where to find them.

I do have GVF to compute variance of some variable estimations. After using some replication methods, my estimations are very different from the official GVF results.
 
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  • #2
You are using terms unfamiliar to me. By "replication", do you mean "re-sampling" or "bootstrapping"?

What kind of information is the "survey weighting" of the data?
 
  • #3
To do Jackknife, each replication need to be a pseudo-sample has same design as the original sample. We can call replications re-samples, it is also similar to bootstrapping. Because of the survey design, each observation unit in the sample has to be weighted, the data have weights, which is good thing. However the data do not have design variables, which means it is impossible to re-sample like the original sample did.

Need to make re-samples has similar design to the original sample.
 

What is the "Replication Method for Survey Estimation"?

The Replication Method for Survey Estimation is a statistical technique used to estimate population parameters from survey data. It involves creating multiple replicates of the original survey data and using these replicates to generate more accurate estimates of population parameters.

Why is the Replication Method important in survey estimation?

The Replication Method is important because it takes into account the complex sampling design of surveys, which can result in biased estimates if not properly accounted for. By creating multiple replicates of the survey data, this method allows for a more accurate representation of the population being studied.

How does the Replication Method work?

The Replication Method works by creating multiple replicates of the original survey data using a statistical technique known as resampling. These replicates are then used to generate estimates of population parameters, taking into account the complex sampling design of the survey. The final estimate is an average of the estimates from all the replicates, resulting in a more accurate estimate of the population parameter.

What are the benefits of using the Replication Method in survey estimation?

The benefits of using the Replication Method include more accurate estimates of population parameters, reduced bias in estimates, and the ability to calculate measures of precision, such as standard errors. This method also allows for the analysis of complex survey data that may not be possible with traditional statistical techniques.

Are there any limitations to the Replication Method?

Although the Replication Method is a powerful tool for survey estimation, it does have some limitations. It requires a large sample size to produce reliable estimates, and it may not be suitable for all types of surveys. Additionally, the replication process can be computationally intensive and time-consuming.

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