I guess I am saying do you have to run a different type of regression or interpret the results different this way. I was going to look on line but I could not even think what topic covers this.

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I guess I am saying do you have to run a different type of regression or interpret the results different this way. I was going to look on line but I could not even think what topic covers this.

Does this help?

In all the years of reading about regression I have not seen that addressed.

An answer I received elsewhere. Which was totally new to me.

Your question is about cluster-robust inference, and the short answer is that typically, this does not change your estimate of a parameter (such as ββ is a linear regression or a logistic regression), but it will affect your standard errors. Typically, two assumptions that are commonly made are that standard errors are uncorrelated across observations, and that the variance of the error term is constant (this is called homoskedasticity).

In the case of uncorrelated errors that are different, the extension for linear regressions is to compute White standard errors.

In your case, the issue is that standard errors are indeed correlated across observations, but in a particular way: they are correlated across the distinct counselors. This is called clustered errors, and many methods exist to accommodate clustering. See this Stackoverflow post for some R packages that allow for clustering in logistic regressions.

Additionally, I highly suggest you take a look (at least at the intro and first few sections) of this excellent introduction to clustered errors.

In the case of uncorrelated errors that are different, the extension for linear regressions is to compute White standard errors.

In your case, the issue is that standard errors are indeed correlated across observations, but in a particular way: they are correlated across the distinct counselors. This is called clustered errors, and many methods exist to accommodate clustering. See this Stackoverflow post for some R packages that allow for clustering in logistic regressions.

Additionally, I highly suggest you take a look (at least at the intro and first few sections) of this excellent introduction to clustered errors.

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