survey design-PSU/id

[Zafar55]

Hello, everyone!

Anyone have any experience with survey design? I'm attempting to comprehend each variable.
Could someone please explain what id means in the survey design? Is it the same primary sampling unit?
Thank you, Zafar.

[jrkrideau]

I think we need some details about the survey. This may help you to give us some information We need a reproducible example (reprex)

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

Traditionally ID stands for Identification number for a survey respondent.

[StatSteph]

Yes, ids is the PSU variable if you are using the survey or srvyr packages. Check out these slides for some more help, possibly. tidy-survey-short-course/Slides-day-3.pdf at main · tidy-survey-r/tidy-survey-short-course · GitHub

[pavel]

ID specifies PSU identifier.
Read carefully the documentation for the survey data you want to use, they would specify the svydesign formula for you ( or twophase() if the survey is using two phase case-control design). For more information regarding how to approach data analysis with survey data:
1)26 Survey analysis | The Epidemiologist R Handbook
2)dplyr-Like Syntax for Summary Statistics of Survey Data • srvyr
3)http://asdfree.com/
4)Survey Data Analysis with R
Also, if you want to focus on a specific subgroup within the survey please, make sure to use subset function e.g

design <- 
    svydesign(
        id = ~ seqnumhh , 
        strata = ~ stratum , 
        weights = ~ provwt_d , 
        data = subset( nis_df , provwt_d > 0 ) 
    )